Snapshot Kruger

Camera trap image archive from Kruger National Park, South Africa, collected as part of the Snapshot Safari citizen science wildlife monitoring program. The dataset comprises approximately 190,000 photographic images alongside tabular and archival metadata files capturing wildlife encounters recorded by remote camera stations across the reserve.

wildlife camera-traps kruger-national-park south-africa citizen-science biodiversity mammal-survey

# Snapshot Kruger

### Snapshot Kruger

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/KRU`

#### Overview

This data set contains 4747 sequences of camera trap images, totaling 10072 images, from the [Snapshot Kruger](https://www.zooniverse.org/projects/shuebner729/snapshot-kruger) project, part of the [Snapshot Safari](http://www.snapshotsafari.org/) network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. [Kruger National Park](http://www.krugerpark.co.za/), South Africa has been a refuge for wildlife since its establishment in 1898, and it houses one of the most diverse wildlife assemblages remaining in Africa. The Snapshot Safari grid was established in 2018 as part of a research project assessing the impacts of large mammals on plant life as boundary fences were removed and wildlife reoccupied areas of previous extirpation.

Labels are provided for 46 categories, primarily at the species level (for example, the most common labels are impala, elephant, and buffalo). Approximately 61.60% of images are labeled as empty. A full list of species and associated image counts is available [here](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KRU/SnapshotKruger_S1_v1.0.species_list.csv).

Additional data from this project is available as part of the [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/) dataset.

#### Citation, license, and contact information

For questions about this data set, contact [Sarah Huebner](mailto:huebn090@umn.edu) at the University of Minnesota.

This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

The original data set included a "human" class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.

#### Data format

Annotations are provided in [COCO Camera Traps](https://github.com/agentmorris/MegaDetector/blob/main/megadetector/data_management/README.md#coco-camera-traps-format) .json format, as well as .csv format. Note that annotations in the .json format are tied to *images*, but are only reliable at the *sequence* level. For example, there are rare sequences in which two of three images contain a lion, but the third is empty (lions, it turns out, walk away sometimes), but all three images would be annotated as "lion".

For information about mapping this dataset's categories to a common taxonomy, see [this page](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

#### Downloading the data

Images are available in the following cloud folders:

* gs://public-datasets-lila/snapshot-safari/KRU/KRU_public (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/KRU/KRU_public (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KRU/KRU_public (Azure)

A link to a zipfile is also provided below, but - whether you want the whole data set, a specific folder, or a subset of the data (e.g. images for one species) - we recommend checking out our [guidelines for accessing images without using giant zipfiles](http://lila.science/image-access).

Data download links:

[Season 1 images](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KRU/KRU_S1.lila.zip) (28GB) ([.json metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KRU/SnapshotKruger_S1_v1.0.json.zip)) ([.csv metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KRU/KRU_S1.csv.zip)) (GCP)

[Season 1 images](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KRU/KRU_S1.lila.zip) (28GB) ([.json metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KRU/SnapshotKruger_S1_v1.0.json.zip)) ([.csv metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KRU/KRU_S1.csv.zip)) (Azure)

[Season 1 images](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/KRU/KRU_S1.lila.zip) (28GB) ([.json metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/KRU/SnapshotKruger_S1_v1.0.json.zip)) ([.csv metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/KRU/KRU_S1.csv.zip)) (AWS)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

#### Other useful links

[MegaDetector](https://github.com/agentmorris/MegaDetector/blob/main/megadetector.md) results for all camera trap datasets on LILA are available [here](https://lila.science/megadetector-results-for-camera-trap-datasets/).

Information about mapping camera trap datasets to a common taxonomy is available [here](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

![](http://lila.science/wp-content/uploads/2019/12/kru_thumb.KRU_S1_48_R1_IMAG0015.jpg)

### Snapshot Mountain Zebra

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/MTZ`

#### Overview

This data set contains 71688 sequences of camera trap images, totaling 73034 images, from the [Snapshot Mountain Zebra](https://www.zooniverse.org/projects/meredithspalmer/snapshot-mountain-zebra/) project, part of the [Snapshot Safari](http://www.snapshotsafari.org/) network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. [Mountain Zebra National Park](https://www.sanparks.org/parks/mountain_zebra/) is located in the Eastern Cape of South Africa in a transitional area between several distinct biomes, which means it is home to many endemic species. As the name suggests, this park contains the largest remnant population of Cape Mountain zebras, ~700 as of 2019 and increasing steadily every year.

Labels are provided for 54 categories, primarily at the species level (for example, the most common labels are zebramountain, kudu, and springbok). Approximately 91.23% of images are labeled as empty. A full list of species and associated image counts is available [here](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/MTZ/SnapshotMountainZebra_S1_v1.0.species_list.csv).

Additional data from this project is available as part of the [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/) dataset.

#### Citation, license, and contact information

For questions about this data set, contact [Sarah Huebner](mailto:huebn090@umn.edu) at the University of Minnesota.

This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

The original data set included a "human" class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.

#### Data format

Annotations are provided in [COCO Camera Traps](https://github.com/agentmorris/MegaDetector/blob/main/megadetector/data_management/README.md#coco-camera-traps-format) .json format, as well as .csv format. Note that annotations in the .json format are tied to *images*, but are only reliable at the *sequence* level. For example, there are rare sequences in which two of three images contain a lion, but the third is empty (lions, it turns out, walk away sometimes), but all three images would be annotated as "lion".

For information about mapping this dataset's categories to a common taxonomy, see [this page](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

Images are available in the following cloud folders:

* gs://public-datasets-lila/snapshot-safari/MTZ/MTZ_public (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/MTZ/MTZ_public (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/MTZ/MTZ_public (Azure)

A link to a zipfile is also provided below, but - whether you want the whole data set, a specific folder, or a subset of the data (e.g. images for one species) - we recommend checking out our [guidelines for accessing images without using giant zipfiles](http://lila.science/image-access).

Data download links:

[Season 1 images](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/MTZ/MTZ_S1.lila.zip) (109GB) ([.json metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/MTZ/SnapshotMountainZebra_S1_v1.0.json.zip)) ([.csv metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/MTZ/MTZ_S1.csv.zip)) (GCP)

[Season 1 images](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/MTZ/MTZ_S1.lila.zip) (109GB) ([.json metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/MTZ/SnapshotMountainZebra_S1_v1.0.json.zip)) ([.csv metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/MTZ/MTZ_S1.csv.zip)) (Azure)

[Season 1 images](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/MTZ/MTZ_S1.lila.zip) (109GB) ([.json metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/MTZ/SnapshotMountainZebra_S1_v1.0.json.zip)) ([.csv metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/MTZ/MTZ_S1.csv.zip)) (AWS)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

#### Other useful links

[MegaDetector](https://github.com/agentmorris/MegaDetector/blob/main/megadetector.md) results for all camera trap datasets on LILA are available [here](https://lila.science/megadetector-results-for-camera-trap-datasets/).

Information about mapping camera trap datasets to a common taxonomy is available [here](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

![](http://lila.science/wp-content/uploads/2019/12/mtz_thumb.MTZ_S1_B04_R3_IMAG0180.jpg)

### Snapshot Camdeboo

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/CDB`

#### Overview

This data set contains 12132 sequences of camera trap images, totaling 30227 images, from the [Snapshot Camdeboo](https://www.zooniverse.org/projects/shuebner729/snapshot-camdeboo) project, part of the [Snapshot Safari](http://www.snapshotsafari.org/) network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. [Camdeboo National Park](https://www.sanparks.org/parks/camdeboo/), South Africa is crucial habitat for many birds on a global scale, with greater than fifty endemic and near-endemic species and many migratory species.

Labels are provided for 43 categories, primarily at the species level (for example, the most common labels are kudu, springbok, and ostrich). Approximately 43.74% of images are labeled as empty. A full list of species and associated image counts is available [here](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/CDB/SnapshotCamdeboo_S1_v1.0.species_list.csv).

Additional data from this project is available as part of the [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/) dataset.

#### Citation, license, and contact information

For questions about this data set, contact [Sarah Huebner](mailto:huebn090@umn.edu) at the University of Minnesota.

This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

The original data set included a "human" class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.

#### Data format

Annotations are provided in [COCO Camera Traps](https://github.com/agentmorris/MegaDetector/blob/main/megadetector/data_management/README.md#coco-camera-traps-format) .json format, as well as .csv format. Note that annotations in the .json format are tied to *images*, but are only reliable at the *sequence* level. For example, there are rare sequences in which two of three images contain a lion, but the third is empty (lions, it turns out, walk away sometimes), but all three images would be annotated as "lion".

For information about mapping this dataset's categories to a common taxonomy, see [this page](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

#### Downloading the data

Images are available in the following cloud folders:

* gs://public-datasets-lila/snapshot-safari/CDB/CDB_public (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/CDB/CDB_public (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/CDB/CDB_public (Azure)

A link to a zipfile is also provided below, but - whether you want the whole data set, a specific folder, or a subset of the data (e.g. images for one species) - we recommend checking out our [guidelines for accessing images without using giant zipfiles](http://lila.science/image-access).

Data download links:

[Season 1 images](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/CDB/CDB_S1.lila.zip) (33GB) ([.json metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/CDB/SnapshotCamdeboo_S1_v1.0.json.zip)) ([.csv metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/CDB/CDB_S1.csv.zip)) (GCP)

[Season 1 images](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/CDB/CDB_S1.lila.zip) (33GB) ([.json metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/CDB/SnapshotCamdeboo_S1_v1.0.json.zip)) ([.csv metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/CDB/CDB_S1.csv.zip)) (Azure)

[Season 1 images](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/CDB/CDB_S1.lila.zip) (33GB) ([.json metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/CDB/SnapshotCamdeboo_S1_v1.0.json.zip)) ([.csv metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/CDB/CDB_S1.csv.zip)) (AWS)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

#### Other useful links

[MegaDetector](https://github.com/agentmorris/MegaDetector/blob/main/megadetector.md) results for all camera trap datasets on LILA are available [here](https://lila.science/megadetector-results-for-camera-trap-datasets/).

Information about mapping camera trap datasets to a common taxonomy is available [here](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

![](http://lila.science/wp-content/uploads/2019/12/cdbthumb_CDB_S1_C04_R2_IMAG0253.jpg)

### Snapshot Enonkishu

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/ENO`

#### Overview

This data set contains 13301 sequences of camera trap images, totaling 28544 images, from the [Snapshot Enonkishu](https://www.zooniverse.org/projects/aguthmann/snapshot-enonkishu) project, part of the [Snapshot Safari](http://www.snapshotsafari.org/) network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. [Enonkishu Conservancy](https://www.enonkishu.org/) is located on the northern boundary of the Mara-Serengeti ecosystem in Kenya, and is managed by a consortium of stakeholders and land-owning Maasai families. Their aim is to promote coexistence between wildlife and livestock in order to encourage regenerative grazing and build stability in the Mara conservancies.

Labels are provided for 39 categories, primarily at the species level (for example, the most common labels are impala, warthog, and zebra). Approximately 64.76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/ENO/SnapshotEnonkishu_S1_v1.0.species_list.csv).

Additional data from this project is available as part of the [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/) dataset.

#### Citation, license, and contact information

For questions about this data set, contact [Sarah Huebner](mailto:huebn090@umn.edu) at the University of Minnesota.

This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

The original data set included a "human" class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.

#### Data format

Annotations are provided in [COCO Camera Traps](https://github.com/agentmorris/MegaDetector/blob/main/megadetector/data_management/README.md#coco-camera-traps-format) .json format, as well as .csv format. Note that annotations in the .json format are tied to *images*, but are only reliable at the *sequence* level. For example, there are rare sequences in which two of three images contain a lion, but the third is empty (lions, it turns out, walk away sometimes), but all three images would be annotated as "lion".

For information about mapping this dataset's categories to a common taxonomy, see [this page](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

#### Downloading the data

Images are available in the following cloud folders:

* gs://public-datasets-lila/snapshot-safari/ENO/ENO_public (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/ENO/ENO_public (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/ENO/ENO_public (Azure)

A link to a zipfile is also provided below, but - whether you want the whole data set, a specific folder, or a subset of the data (e.g. images for one species) - we recommend checking out our [guidelines for accessing images without using giant zipfiles](http://lila.science/image-access).

Data download links:

[Season 1 images](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/ENO/ENO_S1.lila.zip) (32GB) ([.json metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/ENO/SnapshotEnonkishu_S1_v1.0.json.zip)) ([.csv metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/ENO/ENO_S1.csv.zip)) (GCP)

[Season 1 images](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/ENO/ENO_S1.lila.zip) (32GB) ([.json metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/ENO/SnapshotEnonkishu_S1_v1.0.json.zip)) ([.csv metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/ENO/ENO_S1.csv.zip)) (Azure)

[Season 1 images](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/ENO/ENO_S1.lila.zip) (32GB) ([.json metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/ENO/SnapshotEnonkishu_S1_v1.0.json.zip)) ([.csv metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/ENO/ENO_S1.csv.zip)) (AWS)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

#### Other useful links

[MegaDetector](https://github.com/agentmorris/MegaDetector/blob/main/megadetector.md) results for all camera trap datasets on LILA are available [here](https://lila.science/megadetector-results-for-camera-trap-datasets/).

Information about mapping camera trap datasets to a common taxonomy is available [here](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

![](http://lila.science/wp-content/uploads/2019/12/enothumb_ENO_S1_B06_R1_IMAG0211.jpg)

### Snapshot Kgalagadi

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/KGA`

#### Overview

This data set contains 3611 sequences of camera trap images, totaling 10222 images, from the [Snapshot Kgalagadi](https://www.zooniverse.org/projects/shuebner729/snapshot-kgalagadi/) project, part of the [Snapshot Safari](http://www.snapshotsafari.org/) network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. The [Kgalagadi Transfrontier Park](https://www.sanparks.org/parks/kgalagadi/) stretches from the Namibian border across South Africa and into Botswana, covering a landscape commonly referred to as the Kalahari – an arid savanna. This region is of great interest to help us understand how animals cope with extreme temperatures at both ends of the scale.

Labels are provided for 31 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, birdother, and ostrich). Approximately 76.14% of images are labeled as empty. A full list of species and associated image counts is available [here](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KGA/SnapshotKgalagadi_S1_v1.0.species_list.csv).

Additional data from this project is available as part of the [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/) dataset.

#### Citation, license, and contact information

For questions about this data set, contact [Sarah Huebner](mailto:huebn090@umn.edu) at the University of Minnesota.

This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

The original data set included a "human" class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.

#### Data format

Annotations are provided in [COCO Camera Traps](https://github.com/agentmorris/MegaDetector/blob/main/megadetector/data_management/README.md#coco-camera-traps-format) .json format, as well as .csv format. Note that annotations in the .json format are tied to *images*, but are only reliable at the *sequence* level. For example, there are rare sequences in which two of three images contain a lion, but the third is empty (lions, it turns out, walk away sometimes), but all three images would be annotated as "lion".

For information about mapping this dataset's categories to a common taxonomy, see [this page](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

#### Downloading the data

Images are available in the following cloud folders:

* gs://public-datasets-lila/snapshot-safari/KGA/KGA_public (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/KGA/KGA_public (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KGA/KGA_public (Azure)

A link to a zipfile is also provided below, but - whether you want the whole data set, a specific folder, or a subset of the data (e.g. images for one species) - we recommend checking out our [guidelines for accessing images without using giant zipfiles](http://lila.science/image-access).

Data download links:

[Season 1 images](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KGA/KGA_S1.lila.zip) (10GB) ([.json metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KGA/SnapshotKgalagadi_S1_v1.0.json.zip)) ([.csv metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KGA/KGA_S1.csv.zip)) (GCP)

[Season 1 images](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KGA/KGA_S1.lila.zip) (10GB) ([.json metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KGA/SnapshotKgalagadi_S1_v1.0.json.zip)) ([.csv metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KGA/KGA_S1.csv.zip)) (Azure)

[Season 1 images](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/KGA/KGA_S1.lila.zip) (10GB) ([.json metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/KGA/SnapshotKgalagadi_S1_v1.0.json.zip)) ([.csv metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/KGA/KGA_S1.csv.zip)) (AWS)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

#### Other useful links

[MegaDetector](https://github.com/agentmorris/MegaDetector/blob/main/megadetector.md) results for all camera trap datasets on LILA are available [here](https://lila.science/megadetector-results-for-camera-trap-datasets/).

Information about mapping camera trap datasets to a common taxonomy is available [here](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

![](http://lila.science/wp-content/uploads/2019/12/kgathumb_KGA_S1_A02_R1_IMAG0007.jpg)

### Snapshot Karoo

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/KAR`

#### Overview

This data set contains 14889 sequences of camera trap images, totaling 38074 images, from the [Snapshot Karoo](https://www.zooniverse.org/projects/shuebner729/snapshot-karoo) project, part of the [Snapshot Safari](http://www.snapshotsafari.org/) network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. [Karoo National Park](https://www.sanparks.org/parks/karoo/), located in the arid Nama Karoo biome of South Africa, is defined by its endemic vegetation and mountain landscapes. Its unique topographical gradient has led to a surprising amount of biodiversity, with 58 mammals and more than 200 bird species recorded, as well as a multitude of reptilian species.

Labels are provided for 38 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, hartebeestred, and kudu). Approximately 83.02% of images are labeled as empty. A full list of species and associated image counts is available [here](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KAR/SnapshotKaroo_S1_v1.0.species_list.csv).

Additional data from this project is available as part of the [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/) dataset.

#### Citation, license, and contact information

For questions about this data set, contact [Sarah Huebner](mailto:huebn090@umn.edu) at the University of Minnesota.

This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

The original data set included a "human" class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.

#### Data format

Annotations are provided in [COCO Camera Traps](https://github.com/agentmorris/MegaDetector/blob/main/megadetector/data_management/README.md#coco-camera-traps-format) .json format, as well as .csv format. Note that annotations in the .json format are tied to *images*, but are only reliable at the *sequence* level. For example, there are rare sequences in which two of three images contain a lion, but the third is empty (lions, it turns out, walk away sometimes), but all three images would be annotated as "lion".

For information about mapping this dataset's categories to a common taxonomy, see [this page](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

#### Downloading the data

Images are available in the following cloud folders:

* gs://public-datasets-lila/snapshot-safari/KAR/KAR_public (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshot-safari/KAR/KAR_public (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KAR/KAR_public (Azure)

A link to a zipfile is also provided below, but - whether you want the whole data set, a specific folder, or a subset of the data (e.g. images for one species) - we recommend checking out our [guidelines for accessing images without using giant zipfiles](http://lila.science/image-access).

Data download links:

[Season 1 images](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KAR/KAR_S1.lila.zip) (40GB) ([.json metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KAR/SnapshotKaroo_S1_v1.0.json.zip)) ([.csv metadata](https://storage.googleapis.com/public-datasets-lila/snapshot-safari/KAR/KAR_S1.csv.zip)) (GCP)

[Season 1 images](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KAR/KAR_S1.lila.zip) (40GB) ([.json metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KAR/SnapshotKaroo_S1_v1.0.json.zip)) ([.csv metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshot-safari/KAR/KAR_S1.csv.zip)) (Azure)

[Season 1 images](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/KAR/KAR_S1.lila.zip) (40GB) ([.json metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/KAR/SnapshotKaroo_S1_v1.0.json.zip)) ([.csv metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshot-safari/KAR/KAR_S1.csv.zip)) (AWS)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

#### Other useful links

[MegaDetector](https://github.com/agentmorris/MegaDetector/blob/main/megadetector.md) results for all camera trap datasets on LILA are available [here](https://lila.science/megadetector-results-for-camera-trap-datasets/).

Information about mapping camera trap datasets to a common taxonomy is available [here](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

![](http://lila.science/wp-content/uploads/2020/01/kar_thumb.KAR_S1_D04_R1_IMAG0073.jpg)


Snapshot Serengeti

A large-scale camera trap image dataset from the Serengeti ecosystem, containing over 7 million JPG photographs of wildlife captured by motion-triggered cameras across the Serengeti landscape.

camera-traps wildlife serengeti biodiversity citizen-science africa mammals

# Snapshot Serengeti

### Snapshot Serengeti

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshotserengeti-unzipped`

#### Overview

This data set contains approximately 2.65M sequences of camera trap images, totaling 7.1M images, from seasons one through eleven of the [Snapshot Serengeti](https://snapshotserengeti.org/) project, the flagship project of the [Snapshot Safari](http://www.snapshotsafari.org/) network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. [Serengeti National Park](https://en.wikipedia.org/wiki/Serengeti_National_Park) in Tanzania is best known for the massive annual migrations of wildebeest and zebra that drive the cycling of its dynamic ecosystem.

Labels are provided for 61 categories, primarily at the species level (for example, the most common labels are wildebeest, zebra, and Thomson's gazelle). Approximately 76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S1-11_v2.1.species_list.csv). We have also added approximately 150,000 bounding box annotations to approximately 78,000 of those images.

Additional data from this project is available as part of the [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/) dataset.

#### Citation, license, and contact information

The images and species-level labels are described in more detail in the associated manuscript:

Swanson AB, Kosmala M, Lintott CJ, Simpson RJ, Smith A, Packer C (2015) Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Scientific Data 2: 150026. ([DOI](https://doi.org/10.1038/sdata.2015.26)) ([bibtex](http://lila.science/wp-content/uploads/2018/11/snapshotserengeti.bib))

Please cite this manuscript if you use this data set.

For questions about this data set, contact [Sarah Huebner](mailto:huebn090@umn.edu) at the University of Minnesota.

This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

The original Snapshot Serengeti data set included a "human" class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.

#### Data format

Annotations are provided in [COCO Camera Traps](https://github.com/agentmorris/MegaDetector/blob/main/megadetector/data_management/README.md#coco-camera-traps-format) .json format. .json files are provided for each season, and a single .json file is also provided for all seasons combined. Note that annotations are tied to *images*, but are only reliable at the *sequence* level. For example, there are rare sequences in which two of three images contain a lion, but the third is empty (lions, it turns out, walk away sometimes), but all three images would be annotated as "lion".

Annotations are also provided in a (non-standard) .csv format. These are intended to allow replication of the original dataset paper, but they have not been maintained as diligently as the .json files and their format has not been documented, so unless you have a strong reason to use the .csv files, we recommend using the .json files.

Additional metadata related to the aggregation of human labels into consensus labels is available in an [addendum](http://lila.science/datasets/snapshot-serengeti-addendum).

We have also divided locations (i.e., cameras) into training and validation splits to allow for consistent benchmarking on this data set.

For information about mapping this dataset's categories to a common taxonomy, see [this page](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

#### Downloading the data

Images are available in the following cloud storage folders:

* gs://public-datasets-lila/snapshotserengeti-unzipped (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/snapshotserengeti-unzipped (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-unzipped (Azure)

A link to a zipfile per season is also provided below, but - whether you want the whole data set, a specific folder, or a subset of the data (e.g. images for one species) - we recommend checking out our [guidelines for accessing images without using giant zipfiles](http://lila.science/image-access).

Data download links:

Season 1 (242GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S01_v2_0.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S01_v2_0.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S01_v2_0.zip)) ([metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengetiS01.json.zip))

Season 2 (382GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S02_v2_0.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S02_v2_0.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S02_v2_0.zip)) ([metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengetiS02.json.zip))

Season 3 (25`GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S03_v2_0.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S03_v2_0.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S03_v2_0.zip)) ([metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengetiS03.json.zip))

Season 4 (368GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S04_v2_0.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S04_v2_0.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S04_v2_0.zip)) ([metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengetiS04.json.zip))

Season 5 (596GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S05_v2_0.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S05_v2_0.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S05_v2_0.zip)) ([metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengetiS05.json.zip))

Season 6 (361GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S06_v2_0.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S06_v2_0.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S06_v2_0.zip)) ([metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengetiS06.json.zip))

Season 7 (636GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S07_v2_0.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S07_v2_0.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S07_v2_0.zip)) ([metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengetiS07.json.zip))

Season 8 (part 1) (450GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S08_v2_0_part1.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S08_v2_0_part1.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S08_v2_0_part1.zip)) ([metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengetiS08.json.zip))

Season 8 (part 2) (414GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S08_v2_0_part2.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S08_v2_0_part2.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S08_v2_0_part2.zip))

Season 9 (part 1) (432GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S09_v2_0_part1.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S09_v2_0_part1.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S09_v2_0_part1.zip)) ([metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengetiS09.json.zip))

Season 9 (part 2) (432GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S09_v2_0_part2.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S09_v2_0_part2.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S09_v2_0_part2.zip))

Season 10 (part 1) (500GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S10_v2_0_part1.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S10_v2_0_part1.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S10_v2_0_part1.zip)) ([metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengetiS10.json.zip))

Season 10 (part 2) (166GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S10_v2_0_part2.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S10_v2_0_part2.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S10_v2_0_part2.zip))

Season 11 (479GB) ([images, GCP](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S11_v2_0.zip)) ([images, Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S11_v2_0.zip)) ([images, AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengeti_S11_v2_0.zip)) ([metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/snapshotserengeti-v-2-0/SnapshotSerengetiS11.json.zip))

[All metadata (.csv)](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S1-11_v2_1.csv.zip)

[All metadata (.json)](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengeti_S1-11_v2_1.json.zip)

[Bounding boxes](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengetiBboxes_20190903.json.zip)

[Recommended train/val splits](https://storage.googleapis.com/public-datasets-lila/snapshotserengeti-v-2-0/SnapshotSerengetiSplits_v0.json)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

#### Other useful links

[MegaDetector](https://github.com/agentmorris/MegaDetector/blob/main/megadetector.md) results for all camera trap datasets on LILA are available [here](https://lila.science/megadetector-results-for-camera-trap-datasets/).

Information about mapping camera trap datasets to a common taxonomy is available [here](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

![](http://lila.science/wp-content/uploads/2018/10/ss_web.jpg)


SWG Camera Traps 2018-2020

A collection of nearly 1.9 million camera trap photographs captured between 2018 and 2020, associated with the SWG (likely State Wildlife Grant) program. The dataset consists almost entirely of JPEG images from wildlife monitoring cameras, with no accompanying README to clarify species coverage, geographic scope, or collection methodology.

camera-traps wildlife-monitoring wildlife biodiversity animal-detection

# SWG Camera Traps 2018-2020

### SWG Camera Traps 2018-2020

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/swg-camera-traps`

#### Overview

This data set contains 436,617 sequences of camera trap images from 982 locations in Vietnam and Lao, totaling 2,039,657 images. Labels are provided for 120 categories, primarily at the species level (for example, the most common labels are "Eurasian Wild Pig", "Large-antlered Muntjac", and "Unidentified Murid"). Approximately 12.98% of images are labeled as empty. A full list of species and associated image counts is available [here](https://storage.googleapis.com/public-datasets-lila/swg-camera-traps/swg_camera_traps_classes.txt). 101,659 bounding boxes are provided on 88,135 images.

This data set is provided by the [Saola Working Group](https://www.savethesaola.org/); providers include:

* IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group (SWG)
* Asian Arks
* Wildlife Conservation Society (Lao)
* WWF Lao
* Integrated Conservation of Biodiversity and Forests project, Lao (ICBF)
* Center for Environment and Rural Development, Vinh University, Vietnam

#### Citation, license, and contact information

If you use these data in a publication or report, please use the following citation:

SWG (2021): Northern and Central Annamites Camera Traps 2.0. IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group. Dataset.

For questions about this data set, contact [saolawg@gmail.com](mailto:saolawg@gmail.com).

This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

The original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata.

#### Data format

Annotations are provided in [COCO Camera Traps](https://github.com/agentmorris/MegaDetector/blob/main/megadetector/data_management/README.md#coco-camera-traps-format) format.

For information about mapping this dataset's categories to a common taxonomy, see [this page](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

#### Accessing the data

Class-level annotations are available here:

    [swg_camera_traps.zip](https://storage.googleapis.com/public-datasets-lila/swg-camera-traps/swg_camera_traps.zip)

Bounding box annotations are available here:

    [swg_camera_traps.bounding_boxes.with_species.zip](https://storage.googleapis.com/public-datasets-lila/swg-camera-traps/swg_camera_traps.bounding_boxes.with_species.zip) (with the same classes as the class-level labels)

    [swg_camera_traps.bounding_boxes.no_species.zip](https://storage.googleapis.com/public-datasets-lila/swg-camera-traps/swg_camera_traps.bounding_boxes.no_species.zip) (with just animal/person/vehicle labels)

Images are available in the following cloud storage folders:

* gs://public-datasets-lila/swg-camera-traps (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/swg-camera-traps (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/swg-camera-traps (Azure)

We recommend downloading images (the whole folder, or a subset of the folder) using [gsutil](https://cloud.google.com/storage/docs/gsutil) (for GCP), [aws s3](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/s3/index.html) (for AWS), or [AzCopy](https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10) (for Azure). For more information about using gsutil, aws s3, or AzCopy, check out our [guidelines for accessing images without using giant zipfiles](http://lila.science/image-access).

If you prefer to download images via http, you can. For example, the image referred to in the metadata file as:

    public/vietnam/loc_0815/2019/06/image_00059.jpg

...can be downloaded directly from any of the following URLs (one for each cloud):

https://storage.googleapis.com/public-datasets-lila/swg-camera-traps/public/vietnam/loc_0815/2019/06/image_00059.jpg

http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/swg-camera-traps/public/vietnam/loc_0815/2019/06/image_00059.jpg

https://lilawildlife.blob.core.windows.net/lila-wildlife/swg-camera-traps/public/vietnam/loc_0815/2019/06/image_00059.jpg

Having trouble downloading? Check out our [FAQ](https://lila.science/faq#downloadtips).

#### Other useful links

[MegaDetector](https://github.com/agentmorris/MegaDetector/blob/main/megadetector.md) results for all camera trap datasets on LILA are available [here](https://lila.science/megadetector-results-for-camera-trap-datasets/).

Information about mapping camera trap datasets to a common taxonomy is available [here](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

![](http://lila.science/wp-content/uploads/2021/12/pheasant_web.jpg)


Unmanned aerial system (UAS) imagery of migratory waterfowl collected at wildlife refuges in New Mexico. The dataset consists of a single zip archive of drone imagery intended to support waterfowl population monitoring or habitat assessment.

uas aerial-imagery waterfowl migratory-birds wildlife-refuges new-mexico

# UAS Imagery of Migratory Waterfowl at New Mexico Wildlife Refuges

### UAS Imagery of Migratory Waterfowl at New Mexico Wildlife Refuges

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/uas-imagery-of-migratory-waterfowl/uas-imagery-of-migratory-waterfowl.20240220.zip`

#### Overview

These data were collected as part of [Drones for Ducks](https://aspire.unm.edu/research/funded-research/ducks-and-drones.html), a co-operative agreement between the US Fish and Wildlife Service and the Center for the Advancement of Spatial Informatics Research and Education (ASPIRE) at the University of New Mexico. UAS images were collected at wildlife areas in New Mexico in November and December 2018. Fifteen biologists from the US Fish and Wildlife Service annotated a twelve-image subset of UAS imagery of waterbirds collected at Bosque del Apache National Wildlife Refuge through the online image annotation platform [Labelbox](https://labelbox.com). These benchmark images were chosen to maximize species diversity and include a variety of vegetation and habitat types in the background.

Each observer drew polygons around individual birds and selected an identification from a list of twelve species names, which was derived from a previous survey at the site and refined via consultation with National Refuge System biologists. Anonymized observer ID is preserved in the raw annotations as "labeler_id". This dataset includes both raw annotations and consensus annotations. To derive the consensus annotations, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm was used to identify groups of bounding boxes associated with individual animals; the median coordinates of the bounding boxes implicated in each cluster were used to derive the consensus bounding box. Then, the mode species classification for each cluster was used to define the classification for the cluster to produce a consensus annotation.

Paired with these are annotations produced by volunteers using the participatory science platform, [Zooniverse](https://www.zooniverse.org/projects/rowan-aspire/drones-for-ducks). Instead of species categories, volunteers labeled birds by morphological class: Duck, Goose, or Crane. Raw annotations and consensus annotations derived using the process described above are provided.

#### Citation, license, and contact information

If you use this dataset, please cite:

Converse RC, Lippitt CD, Sesnie SE, Harris GM, Butler MG, Stewart DR. Observer variability in manual-visual interpretation of UAS imagery of wildlife, with insights for deep learning applications. In review.

For questions about this data set, contact the Center for the Advancement of Spatial Informatics Research and Education at [aspire@unm.edu](mailto:aspire@unm.edu).

This data set is released under the [CC-BY NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en).

#### Data format

The expert and volunteer datsets described above are in folders called "experts" and "crowdsourced", respectively. Each of those folders contains a folder called "images" and two COCO-formatted .json files, one containing the raw annotations ("raw"), and one containing the consensus annotations ("refined").

#### Downloading the data

This dataset is provided as a single zipfile:

[Download from GCP](https://storage.googleapis.com/public-datasets-lila/uas-imagery-of-migratory-waterfowl/uas-imagery-of-migratory-waterfowl.20240220.zip) (322MB)

[Download from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/uas-imagery-of-migratory-waterfowl/uas-imagery-of-migratory-waterfowl.20240220.zip) (322MB)

[Download from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/uas-imagery-of-migratory-waterfowl/uas-imagery-of-migratory-waterfowl.20240220.zip) (322MB)

![](http://lila.science/wp-content/uploads/2024/02/nm_waterfowl_sample_image_annotated-800.png)


UNSW Predators

Image dataset from the University of New South Wales (UNSW) containing approximately 131,800 JPEG images related to predators, likely wildlife or animals for computer vision or ecological research purposes. No README is available; description is inferred from the title and file composition alone.

wildlife predators computer-vision image-classification ecology

# UNSW Predators

### UNSW Predators

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/unsw-predators`

#### Overview

This dataset contains 131,802 images from 82 camera locations in New South Wales, Australia. Labels are provided for five categories: dingo (24,540 images), fox (4,421 images), goanna (84,361 images), possum (2,074 images), and quoll (16,406 images). No blank images are included.

Images were collected by [Brendan Alting](https://scholar.google.com.au/citations?user=ygBhEqYAAAAJ&hl=en) through the Myall Lakes Dingo/Dapin Project on Browning Strike Force HD Pro cameras deployed from December 2022 to March 2023 near trails in Myall Lakes National Park, New South Wales. Cameras were positioned approximately two metres away from a PVC pipe containing 200g of inaccessible raw chicken necks, staked into the ground in front of a large log to encourage quolls to walk across the log and present both flanks to the camera. Lures were replaced at each site six times during the survey (every 9-22 days; mean 16 days). Baited cameras were loosely arranged in clusters of three, with each cluster (N=13) spaced 4502 metres apart (range 2510m-6756m), and cameras within clusters were spaced on average 1100m apart (range 495m-1483m). Baited cameras were deployed for 90 days and set to capture three images per sequence, at high sensitivity with a 1s delay.

Data collection was funded by the Oatley Flora and Fauna Society, the Australian Wildlife Society, Taronga Conservation Society Australia, and a UNSW Research Technology Services (ResTech) AWS cloud grant. Work was conducted under approval 22/102A from the UNSW Animal Ethics Committee.

#### Citation, license, and contact information

For questions about this data set, contact [Neil Jordan](mailto:neil.jordan@unsw.edu.au).

This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

#### Data format

Annotations (including species tags and unique location identifiers) are provided in [COCO Camera Traps](https://lila.science/coco-camera-traps) format.

For information about mapping this dataset's categories to a common taxonomy, see [this page](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

#### Downloading the data

Metadata is available [here](https://lilawildlife.blob.core.windows.net/lila-wildlife/unsw-predators/unsw-predators.json.zip).

Images are available in the following cloud storage folders:

* gs://public-datasets-lila/unsw-predators/images (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/unsw-predators/images (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/unsw-predators/images (Azure)

We recommend downloading images (the whole folder, or a subset of the folder) using [gsutil](https://cloud.google.com/storage/docs/gsutil) (for GCP), [aws s3](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/s3/index.html) (for AWS), or [AzCopy](https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10) (for Azure). For more information about using gsutil, aws s3, or AzCopy, check out our [guidelines for accessing images without using giant zipfiles](http://lila.science/image-access).

If you prefer to download individual images via http, you can. For example, the thumbnail below appears in the metadata as:

BrendanAltingMLDP2023Images/Q15/Q15__2022-12-21__05-26-18(33).JPG

This image can be downloaded directly from any of the following URLs (one for each cloud):

https://storage.googleapis.com/public-datasets-lila/unsw-predators/images/BrendanAltingMLDP2023Images/Q15/Q15__2022-12-21__05-26-18(33).JPG

http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/unsw-predators/images/BrendanAltingMLDP2023Images/Q15/Q15__2022-12-21__05-26-18(33).JPG

https://lilawildlife.blob.core.windows.net/lila-wildlife/unsw-predators/images/BrendanAltingMLDP2023Images/Q15/Q15__2022-12-21__05-26-18(33).JPG

Having trouble downloading? Check out our [FAQ](https://lila.science/faq#downloadtips).

#### Other useful links

Information about mapping camera trap datasets to a common taxonomy is available [here](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

![quoll in a camera trap image](http://lila.science/wp-content/uploads/2025/03/unsw-predators-Q15__2022-12-21__05-26-1833-700.jpg)


WCS Camera Traps

A large collection of wildlife camera trap imagery assembled by the Wildlife Conservation Society (WCS), comprising over 1.1 million images (JPEG) and 66 video clips (AVI) captured by motion-triggered field cameras. The dataset is intended for wildlife monitoring, species identification, and ecological research.

camera-traps wildlife wildlife-monitoring biodiversity species-detection remote-sensing

# WCS Camera Traps

### WCS Camera Traps

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/wcs-unzipped`

#### Overview

This data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the [Wildlife Conservation Society](https://www.wcs.org/). The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle). A complete list of classes and associated image counts is available [here](https://storage.googleapis.com/public-datasets-lila/wcs/wcs_specieslist.csv). Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations.

Sequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files.

#### Contact information

This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

#### Data format

Annotations are provided in the [COCO Camera Traps](https://github.com/agentmorris/MegaDetector/blob/main/megadetector/data_management/README.md#coco-camera-traps-format) .json format used for most data sets on lila.science.

For information about mapping this dataset's categories to a common taxonomy, see [this page](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

#### Accessing the data

Class-level annotations are available here:

    [wcs_camera_traps.json.zip](https://storage.googleapis.com/public-datasets-lila/wcs/wcs_camera_traps.json.zip)

Bounding box annotations are available here:

    [wcs_20220205_bboxes_with_classes.zip](https://storage.googleapis.com/public-datasets-lila/wcs/wcs_20220205_bboxes_with_classes.zip) (with the same classes as the class-level labels)

    [wcs_20220205_bboxes_no_classes.zip](https://storage.googleapis.com/public-datasets-lila/wcs/wcs_20220205_bboxes_no_classes.zip) (with just animal/person/vehicle labels)

Recommended train/val/test splits are available here:

    [wcs_splits.json](https://storage.googleapis.com/public-datasets-lila/wcs/wcs_splits.json)

Images are available in the following cloud storage folders:

* gs://public-datasets-lila/wcs-unzipped (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/wcs-unzipped (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/wcs-unzipped (Azure)

We recommend downloading images (the whole folder, or a subset of the folder) using [gsutil](https://cloud.google.com/storage/docs/gsutil) (for GCP), [aws s3](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/s3/index.html) (for AWS), or [AzCopy](https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10) (for Azure). For more information about using gsutil, aws s3, or AzCopy, check out our [guidelines for accessing images without using giant zipfiles](http://lila.science/image-access).

If you prefer to download images via http, you can. For example, one image (with lots of birds) appears in the metadata as:

animals/0011/0009.jpg

This image can be downloaded directly from any of the following URLs (one for each cloud):

https://storage.googleapis.com/public-datasets-lila/wcs-unzipped/animals/0011/0009.jpg

http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wcs-unzipped/animals/0011/0009.jpg

https://lilawildlife.blob.core.windows.net/lila-wildlife/wcs-unzipped/animals/0011/0009.jpg

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

#### Other useful links

[MegaDetector](https://github.com/agentmorris/MegaDetector/blob/main/megadetector.md) results for all camera trap datasets on LILA are available [here](https://lila.science/megadetector-results-for-camera-trap-datasets/).

Information about mapping camera trap datasets to a common taxonomy is available [here](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

![](http://lila.science/wp-content/uploads/2019/06/wcs_thumbnail.jpg)


Wellington Camera Traps

A large collection of camera trap imagery from the Wellington region, comprising 270,450 JPEG photographs. Camera trap datasets of this scale are typically used for wildlife monitoring, species detection, and ecological research.

camera-traps wildlife biodiversity wildlife-monitoring new-zealand

# Wellington Camera Traps

### Wellington Camera Traps

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/wellington-unzipped`

#### Overview

This data set contains 270,450 images from 187 camera locations in Wellington, New Zealand. The cameras (Bushnell 119537, 119476, and 119436) recorded sequences of three images when triggered. Each sequence was labelled by citizen scientists and/or professional ecologists from Victoria University of Wellington into 17 classes: 15 animal categories (for example, the most common labels are bird, cat, and hedgehog), empty, and unclassifiable. Approximately 17% of images are labeled as empty. Images within each sequence share the same species label (even though the animal may not have been recorded in all three images).

#### Citation, license, and contact information

If you use this data set, please cite the associated manuscript:

Victor Anton, Stephen Hartley, Andre Geldenhuis, Heiko U Wittmer 2018. [Monitoring the mammalian fauna of urban areas using remote cameras and citizen science](https://academic.oup.com/jue/article/4/1/juy002/4947839?login=true). Journal of Urban Ecology; Volume 4, Issue 1.

This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

For questions about this data set, contact [Victor Anton](mailto:vykanton@gmail.com).

#### Data format

Annotations are provided in .csv format as well as in [COCO Camera Traps](https://github.com/agentmorris/MegaDetector/blob/main/megadetector/data_management/README.md#coco-camera-traps-format) .json format. In the .csv format, empty images are referred to as “nothinghere”; in the .json format, empty images are referred to as “empty” for consistency with other data sets on this site.

For information about mapping this dataset's categories to a common taxonomy, see [this page](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

#### Downloading the data

Images are available in the following cloud storage folders:

* gs://public-datasets-lila/wellington-unzipped/images (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/wellington-unzipped/images (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/wellington-unzipped/images (Azure)

A link to a zipfile is also provided below, but - whether you want the whole data set, a specific folder, or a subset of the data (e.g. images for one species) - we recommend checking out our [guidelines for accessing images without using giant zipfiles](http://lila.science/image-access).

Download links:

GCP links: [images](https://storage.googleapis.com/public-datasets-lila/wellingtoncameratraps/wct_images.zip) (176GB), [.json metadata](https://storage.googleapis.com/public-datasets-lila/wellingtoncameratraps/wellington_camera_traps.json.zip) (8MB), [.csv metadata](https://storage.googleapis.com/public-datasets-lila/wellingtoncameratraps/wellington_camera_traps.csv.zip) (4MB)

Azure links: [images](https://lilawildlife.blob.core.windows.net/lila-wildlife/wellingtoncameratraps/wct_images.zip) (176GB), [.json metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/wellingtoncameratraps/wellington_camera_traps.json.zip) (8MB), [.csv metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/wellingtoncameratraps/wellington_camera_traps.csv.zip) (4MB)

AWS links: [images](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wellingtoncameratraps/wct_images.zip) (176GB), [.json metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wellingtoncameratraps/wellington_camera_traps.json.zip) (8MB), [.csv metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wellingtoncameratraps/wellington_camera_traps.csv.zip) (4MB)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

#### Other useful links

[MegaDetector](https://github.com/agentmorris/MegaDetector/blob/main/megadetector.md) results for all camera trap datasets on LILA are available [here](https://lila.science/megadetector-results-for-camera-trap-datasets/).

Information about mapping camera trap datasets to a common taxonomy is available [here](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).

![](http://lila.science/wp-content/uploads/2018/12/wellington_thumb_800.jpg)


Whale Shark ID

A dataset related to the identification of individual whale sharks (Rhincodon typus), likely containing imagery, encounter records, or pattern-matching data used for photo-identification of this species. No README is present; description is inferred from the title alone.

whale-shark marine-biology species-identification wildlife-tracking individual-identification ocean

# Whale Shark ID

### Whale Shark ID

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/wild-me/whaleshark.coco.tar.gz`

#### Overview

This data set contains images of whale sharks (*Rhincodon typus*) with bounding boxes and individual animal identifications. This dataset represents a collaborative effort based on the data collection and population modeling efforts conducted at Ningaloo Marine Park in Western Australia from 1995-2008. Photos (7888) and metadata from 2441 whale shark encounters were collected from 464 individual contributors, especially from the original research of Brad Norman and from members of the local whale shark tourism industry who sight these animals annually from April-June. Images were annotated with bounding boxes around each visible whale shark and viewpoints were labeled (e.g., left, right, etc.). A total of 543 individual whale sharks were identified by their unique spot patterning using first computer-assisted spot pattern recognition ([Arzoumanian et al.](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2664.2005.01117.x)) and then manual review and confirmation. A total of 7,693 named sightings were exported.

#### Data format

The dataset is released in the [Microsoft COCO](https://cocodataset.org/) format and therefore uses flat image folders with associated YAML metadata files. We have collapsed the entire dataset into a single "train" label and have left "val" and "test" empty; we do this as an invitation to researchers to experiment with their own novel approaches for dealing with the unbalanced and chaotic distribution on the number of sightings per individual. All of the images in the dataset have been resized to have a maximum dimension of 3,000 pixels. The metadata for all animal sightings is defined by an axis-aligned bounding box and includes information on the rotation of the box (theta), the viewpoint of the animal, a species (category) ID, a source image ID, an individual string ID name, and other miscellaneous values. The temporal ordering of the images, and an anonymized ID for the original photographer, can be determined from the metadata for each image.

#### Citation, license, and contact information

For research or press contact, please direct all correspondence to Wild Me at [info@wildme.org](mailto:info@wildme.org). [Wild Me](https://www.wildme.org) is a registered 501(c)(3) not-for-profit based in Portland, Oregon, USA and brings state-of-the-art computer vision tools to ecology researchers working around the globe on wildlife conservation.

This dataset is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

If you use this dataset in published work, please cite as:

Holmberg J, Norman B, Arzoumanian Z. [Estimating population size, structure, and residency time for whale sharks *Rhincodon typus* through collaborative photo-identification](https://www.int-res.com/abstracts/esr/v7/n1/p39-53/). Endangered Species Research. 2009 Apr 8;7(1):39-53.

#### Downloading the data

Data download link:

[Images and metadata](https://storage.googleapis.com/public-datasets-lila/wild-me/whaleshark.coco.tar.gz) (GCP link) (6GB)

[Images and metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/wild-me/whaleshark.coco.tar.gz) (Azure link) (6GB)

[Images and metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wild-me/whaleshark.coco.tar.gz) (AWS link) (6GB)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

![](http://lila.science/wp-content/uploads/2020/08/whaleshark_w_logo_650.png)

### Great Zebra and Giraffe Count and ID

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/wild-me/gzgc.coco.tar.gz`

#### Overview

This dataset contains images of plains zebra (*Equus quagga*) and Masai giraffe (*Giraffa tippelskirchi*) with bounding boxes and individual animal identifications. Images are taken from a two-day census of Nairobi National Park, located just south of the airport in Nairobi, Kenya. The “Great Zebra and Giraffe Count” (GZGC) photographic census was organized on February 28th and March 1st 2015 and had the participation of 27 different teams of citizen scientists and 55 total photographers. Only images containing either zebras or giraffes were included in this dataset, for a total of 4,948 images. All images are labeled with bounding boxes around the individual animals for which there is ID metadata, meaning some images contain missing boxes and are not intended to be used for object detection training or testing. Viewpoints for all animal annotations were also added. All ID assignments were completed using the HotSpotter algorithm ([Crall et al. 2013](http://cs.rpi.edu/hotspotter/crall-hotspotter-wacv-2013.pdf)) by visually matching the stripes and spots as seen on the body of the animal. A total of 2,056 combined names are released for 6,286 individual zebra and 639 giraffe sightings. This dataset presents as a challenging comparison compared to the [whale shark dataset](http://lila.science/datasets/whale-shark-id) since it contains a significantly higher number of animals that are only seen once during the survey.

#### Data format

The dataset is released in the [Microsoft COCO](https://cocodataset.org/) .json format. We have collapsed the entire dataset into a single "train" label and have left "val" and "test" empty; we do this as an invitation to researchers to experiment with their own novel approaches for dealing with the unbalanced and chaotic distribution on the number of sightings per individual. All of the images in the dataset have been resized to have a maximum dimension of 3,000 pixels. The metadata for all animal sightings is defined by an axis-aligned bounding box and includes information on the rotation of the box (theta), the viewpoint of the animal, a species (category) ID, a source image ID, an individual string ID name, and other miscellaneous values. The temporal ordering of the images, and an anonymized ID for the original photographer, can be determined from the metadata for each image.

#### Citation, license, and contact information

For research or press contact, please direct all correspondence to Wild Me at [info@wildme.org](mailto:info@wildme.org). [Wild Me](https://www.wildme.org) is a registered 501(c)(3) not-for-profit based in Portland, Oregon, USA and brings state-of-the-art computer vision tools to ecology researchers working around the globe on wildlife conservation.

This dataset is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

If you use this dataset in published work, please cite as:

Parham J, Crall J, Stewart C, Berger-Wolf T, Rubenstein DI. [Animal population censusing at scale with citizen science and photographic identification](https://oar.princeton.edu/jspui/handle/88435/pr1s791). In AAAI Spring Symposium-Technical Report 2017 Jan 1.

#### Downloading the data

Data download links:

[Images and metadata](https://storage.googleapis.com/public-datasets-lila/wild-me/gzgc.coco.tar.gz) (GCP link) 10GB)

[Images and metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/wild-me/gzgc.coco.tar.gz) (Azure link) (10GB)

[Images and metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wild-me/gzgc.coco.tar.gz) (AWS link) (10GB)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

![](http://lila.science/wp-content/uploads/2020/08/zebra_w_logo_650.png)

### Leopard ID 2022

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/wild-me/leopard.coco.tar.gz`

#### Overview

This data set contains images of African leopards (*Panthera pardus*) with bounding boxes and individual animal identifications. This dataset represents a collaborative effort based on the data collection and population modeling efforts conducted by Botswana Predator Conservation Trust and Kasim Rafiq from 2011-2022. Photos (6,795) and metadata from various leopard encounters were collected. Images were annotated with bounding boxes around each visible leopard and viewpoints were labeled (e.g., left, right, etc.). A total of 430 individual leopard were identified by their unique spot patterning using first computer-assisted spot pattern recognition and then manual review and confirmation. A total of 6,805 named sightings were exported.

#### Data format

The dataset is released in the [Microsoft COCO](https://cocodataset.org/) .json format. We have collapsed the entire dataset into a single “train” label and have left “val” and “test” empty; we do this as an invitation to researchers to experiment with their own novel approaches for dealing with the unbalanced and chaotic distribution on the number of sightings per individual. All of the images in the dataset have been resized to have a maximum dimension of 2,400 pixels. The metadata for all animal sightings is defined by an axis-aligned bounding box and includes information on the rotation of the box (theta), the viewpoint of the animal, a species (category) ID, a source image ID, an individual string ID name, and other miscellaneous values. The temporal ordering of the images can be determined from the metadata for each image.

#### Citation, license, and contact information

For research or press contact, please direct all correspondence to Wild Me at [info@wildme.org](mailto:info@wildme.org). [Wild Me](https://www.wildme.org) is a registered 501(c)(3) not-for-profit based in Portland, Oregon, USA and brings state-of-the-art computer vision tools to ecology researchers working around the globe on wildlife conservation.

This dataset is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

For research or press contact, please direct all correspondence to Wild Me at [info@wildme.org](mailto:info@wildme.org). [Wild Me](https://www.wildme.org) is a registered 501(c)(3) not-for-profit based in Portland, Oregon, USA and brings state-of-the-art computer vision tools to ecology researchers working around the globe on wildlife conservation.

This dataset is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

If you use this dataset in published work, please cite as:

Botswana Predator Conservation Trust (2022). Panthera pardus CSV custom export. Retrieved from [African Carnivore Wildbook](https://africancarnivore.wildbook.org) 2022-04-28.

#### Downloading the data

Data download links:

* [Download from GCP](https://storage.googleapis.com/public-datasets-lila/wild-me/leopard.coco.tar.gz) (8GB)
* [Download from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wild-me/leopard.coco.tar.gz) (8GB)
* [Download from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/wild-me/leopard.coco.tar.gz) (8GB)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

![](http://lila.science/wp-content/uploads/2022/05/leopard-thumb.jpg)

### Hyena ID 2022

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/wild-me/hyena.coco.tar.gz`

#### Overview

This data set contains images of spotted hyena (*Crocuta crocuta*) with bounding boxes and individual animal identifications. This dataset represents a collaborative effort based on the data collection and population modeling efforts conducted by the Botswana Predator Conservation Trust. Photos (3,104) and metadata from various hyena encounters were collected. Images were annotated with bounding boxes around each visible hyena and viewpoints were labeled (e.g., left, right, etc.). A total of 256 individual spotted hyena were identified by their unique spot patterning using first computer-assisted spot pattern recognition and then manual review and confirmation. A total of 3,129 named sightings were exported.

#### Data format

The dataset is released in the [Microsoft COCO](https://cocodataset.org/) .json format. We have collapsed the entire dataset into a single “train” label and have left “val” and “test” empty; we do this as an invitation to researchers to experiment with their own novel approaches for dealing with the unbalanced and chaotic distribution on the number of sightings per individual. All of the images in the dataset have been resized to have a maximum dimension of 2,400 pixels. The metadata for all animal sightings is defined by an axis-aligned bounding box and includes information on the rotation of the box (theta), the viewpoint of the animal, a species (category) ID, a source image ID, an individual string ID name, and other miscellaneous values. The temporal ordering of the images can be determined from the metadata for each image.

#### Citation, license, and contact information

For research or press contact, please direct all correspondence to Wild Me at [info@wildme.org](mailto:info@wildme.org). [Wild Me](https://www.wildme.org) is a registered 501(c)(3) not-for-profit based in Portland, Oregon, USA and brings state-of-the-art computer vision tools to ecology researchers working around the globe on wildlife conservation.

This dataset is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

If you use this dataset in published work, please cite as:

Botswana Predator Conservation Trust (2022). Panthera pardus CSV custom export. Retrieved from [African Carnivore Wildbook](https://africancarnivore.wildbook.org) 2022-04-28.

#### Downloading the data

Data download links:

* [Download from GCP](https://storage.googleapis.com/public-datasets-lila/wild-me/hyena.coco.tar.gz) (3GB)
* [Download from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wild-me/hyena.coco.tar.gz) (3GB)
* [Download from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/wild-me/hyena.coco.tar.gz) (3GB)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

![](http://lila.science/wp-content/uploads/2022/05/hyena-thumb.jpg)

### Beluga ID 2022

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/wild-me/beluga.coco.tar.gz`

#### Overview

This data set contains Beluga whales (*Delphinapterus leucas*) with pre-cropped images and individual animal identifications. This dataset represents a collaborative effort based on the data collection and population modeling efforts conducted in the Cook Inlet off the cost of Alaska from 2016-2019. The photos (5,902) and metadata from 1,617 unique encounters (within 1 hour) were collected from boat-based cameras and a camera looking down from above on an aerial drone. Images are annotated with full-image bounding boxes and viewpoints were labeled (top, left, right). A total of 788 individual Beluga whales were identified by hand by trained experts using scarring patterns and other visual markings. This dataset is being released in tandem with the "[Where's Whale-do?](https://www.drivendata.org/competitions/96/beluga-whales/page/478/)" ID competition hosted by DrivenData and is identical to the public training set used in that competition.

#### Data format

The training dataset is released in the [Microsoft COCO](https://cocodataset.org/) .json format. We have collapsed the entire dataset into a single “train” label and have left “val” and “test” empty; we do this as an invitation to researchers to experiment with their own novel approaches for dealing with the unbalanced and chaotic distribution on the number of sightings per individual. All of the images in the dataset have been resized to have a maximum dimension of 1,200 pixels. The metadata for all animal sightings is defined by an axis-aligned bounding box and includes information on the viewpoint of the animal, a species (category) ID, a source image ID, an individual string ID name, and other miscellaneous values. The temporal ordering of the images can be determined from the metadata for each image.

Test data was added later, after the competition, and is thus in a different format. Contact the dataset owner for questions about the test data.

#### Citation, license, and contact information

For research or press contact, please direct all correspondence to Wild Me at [info@wildme.org](mailto:info@wildme.org). [Wild Me](https://www.wildme.org) is a registered 501(c)(3) not-for-profit based in Portland, Oregon, USA and brings state-of-the-art computer vision tools to ecology researchers working around the globe on wildlife conservation.

This dataset is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

#### Downloading the data

Data download links for the *train* split:

* [Download the train split from GCP](https://storage.googleapis.com/public-datasets-lila/wild-me/beluga.coco.tar.gz) (563MB)
* [Download the train split from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wild-me/beluga.coco.tar.gz) (563MB)
* [Download the train split from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/wild-me/beluga.coco.tar.gz) (563MB)

Data download links for the *test* split:

* [Download the test split from GCP](https://storage.googleapis.com/public-datasets-lila/wild-me/beluga-id-test.zip) (117MB)
* [Download the test split from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wild-me/beluga-id-test.zip) (117MB)
* [Download the test split from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/wild-me/beluga-id-test.zip) (117MB)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

![](http://lila.science/wp-content/uploads/2022/05/beluga-thumb.jpg)


WNI Giraffes

A dataset titled 'WNI Giraffes' consisting of two ZIP archives totalling approximately 200 GB. No further description is available — the dataset has no README.

# WNI Giraffes

### WNI Giraffes

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/wni-giraffes`

#### Overview

Masai giraffes are endangered from illegal killing and habitat loss. As part of the world's largest giraffe conservation science project, the [Wild Nature Institute](http://wildnatureinstitute.org/) has identified thousands of individual Masai giraffes in Tanzania by their unique and unchanging coat patterns. Photogrammetry allows researchers to also track these giraffes' heights and sizes over time from images, by combining keypoint annotations with laser rangefinder data that indicates the distance between the camera and the giraffe.

This dataset contains 29,806 images of giraffes, annotated with keypoints representing four canonical measurement points: the top of the ossicones (horns), the top of the head, the indentation where the neck meets the chest, and the bottom front of the front hoof. Annotations were created by citizen-science volunteers on [Zooniverse](http://zooniverse.org). Around 40,000 images were annotated in total; this dataset excludes images that were annotated by fewer than five volunteers, and also excludes a test set that can be used in the future to validate algorithms developed based on this dataset to automate keypoint identification.

#### Data format

Annotations are provided in a .json file, structured as:

```

	"info": [metadata about the data set (version, etc.)],
	"annotations": [list of annotations]

annotation

	"filename": str,
	"id": int,
	"collection": str (always "train"),
	"keypoints":

                'too': [list of keypoints],
                'toh': [list of keypoints],
                'ni':  [list of keypoints],
                'fbh': [list of keypoints]

keypoint

	"median_x": float,
	"median_y": float,
        "x": [],
        "y": []

```
Keypoint identifiers have the following interpretations, which are also documented in *info['tool_names']*:

* *too*: top of ossicones
* *toh*: top of head
* *ni*: neck indent
* *fbh*: front bottom hoof

*x* and *y* contain the raw annotations provided by individual annotators; *median_x* and *median_y* are the medians of those values when at least three annotations are present for a specific keypoint. When fewer than three annotations are present for a specific keypoint, *median_x* and *median_y* are empty.

Not all keypoints are visible in all images; some images may contain as few as one annotated keypoint (for example, if only the chest of the giraffe is visible).

Sample Python code to read this dataset and plot keypoints (e.g. to produce the thumbnail image at the bottom of this page) is available [here](https://github.com/agentmorris/MegaDetector/blob/master/data_management/importers/plot_wni_giraffes.py).

#### Citation, license, and contact information

For questions about this dataset, contact Derek Lee ([derek@wildnatureinstitute.org](mailto:derek@wildnatureinstitute.org)) at the Wild Nature Institute.

This dataset is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).

#### Downloading the data

[Download images from GCP](https://storage.googleapis.com/public-datasets-lila/wni-giraffes/wni_giraffes_train_images.zip) (196GB)

[Download annotations from GCP](https://storage.googleapis.com/public-datasets-lila/wni-giraffes/wni_giraffes_train.zip) (8MB)

[Download images from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wni-giraffes/wni_giraffes_train_images.zip) (196GB)

[Download annotations from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/wni-giraffes/wni_giraffes_train.zip) (8MB)

[Download images from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/wni-giraffes/wni_giraffes_train_images.zip) (196GB)

[Download annotations from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/wni-giraffes/wni_giraffes_train.zip) (8MB)

Having trouble downloading? Check out our [FAQ](http://lila.science/faq#downloadtips).

![](http://lila.science/wp-content/uploads/2021/01/wni-giraffes-thumb.jpg)


LILA BC (Labeled Information Library of Alexandria: Biology and Conservation)

LILA BC is a repository for data sets related to biology and conservation, intended as a resource for both machine learning (ML) researchers and those that want to harness ML for biology and conservation.  LILA BC is intended to host data from a variety of modalities, but emphasis is placed on labeled images; we currently host over ten million labeled images.


General Bathymetric Chart of the Oceans (GEBCO) from 2024

GEBCO_2024 is a global terrain model providing elevation and bathymetric depth data in meters at 15 arc-second grid resolution, covering both ocean floors and land surfaces worldwide. It includes a Type Identifier (TID) Grid indicating the source data types used in compilation, and incorporates under-ice topography and bathymetry for Greenland and Antarctica. This 2024 release has since been superseded by a multi-year version available at ausantarctic/gebco.

oceanography elevation topography digital-elevation-model global-coverage cloud-optimized-geotiff cryosphere remote-sensing

GEBCO (General Bathymetric Chart of the Oceans)

Dataset files (1 x .tif)

Source Cooperative product page

## README.md
# GEBCO 2024

## IMPORTANT NOTE

This has been replaced by a multi-year version of the same.

See: [ausantarctic/gebco](https://source.coop/ausantarctic/gebco)

---

## Original docs below

Data originally accessed from the
[GEBCO website](https://www.gebco.net/data_and_products/gridded_bathymetry_data/).

GEBCO’s current gridded bathymetric data set, the GEBCO_2024 Grid, is a global terrain model for ocean and land, providing elevation data, in meters, on a 15 arc-second interval grid. It is accompanied by a Type Identifier (TID) Grid that gives information on the types of source data that the GEBCO_2024 Grid is based on.

This release includes a version of the grid with under-ice topography/bathymetry information for Greenland and Antarctica.

Find out information about [terms of use and attribution here](https://www.gebco.net/data_and_products/gridded_bathymetry_data/#a1).

## Access data

### Stream data

The cloud optimised geotiff can be used directly in QGIS or
other applications using this URL:
`https://data.source.coop/alexgleith/gebco-2024/GEBCO_2024`.

### Example to load as Xarray

```python
from odc.stac import load
from pystac import Item

# Bounding box around Tasmania
bbox = [140.0, -45.0, 150.0, -35.0]

# Get the item as a PySTAC Item
item = Item.from_file(
    "https://data.source.coop/alexgleith/gebco-2024/GEBCO_2024.stac-item.json"
)

# Load using odc.stac.load
data = load(
    [item],
    bbox=bbox,
    chunks={"latitude": 1024, "longitude": 1024},
)

# Plot an interactive map
data.elevation.odc.explore(vmin=-3000, vmax=3000, cmap="terrain")
```

### Downloading

While the idea of cloud native data formats is to not download, you can
download this data using this following URL:
`https://s3.us-west-2.amazonaws.com/us-west-2.opendata.source.coop/alexgleith/gebco-2024/GEBCO_2024.tif`

license: AI returned 'unknown' (README does not state a license). Verify with publisher before flipping to _state: reviewed.


Tasmanian 2 metre Digital Elevation Model

A 2-metre resolution Digital Elevation Model (DEM) covering the land area of Tasmania and associated islands (excluding Macquarie Island). Approximately 65% is derived from LiDAR data; the remainder is interpolated from photogrammetric contours, spot heights, and mean high water level data. Projected in GDA 1994 MGA Zone 55 with vertical heights relative to the Australian Height Datum (AHD).

digital-elevation-model lidar tasmania topography cloud-optimized-geotiff remote-sensing

Mineral Resources Tasmania / The LIST (State of Tasmania)

Dataset files (1 x .tif)

Source Cooperative product page

## README.md
# Tasmania 2 metre Digital Elevation Model

The Tasmania 2 metre Digital Elevation Model (DEM) is a regular grid of ground level elevation values spaced at 2 metre intervals across the extent of the land area of Tasmania and most of the associated islands, but excluding Macquarie Island.

Approximately 65% of the DEM is derived from LiDAR data, with the remainder of the surface interpolated from photogrammetric contours, spot heights and mean high water level data. Outside of LiDAR areas, the data resolution is 10 m. The projection is GDA 1994 MGA Zone 55, with vertical heights relative to AHD.

Original metadata can be found on the [Tasmanian Government website](https://data.thelist.tas.gov.au/datagn/srv/eng/main.home?uuid=71619d6d-bb60-490c-a461-504e2b37e2f30).

## License

Data is available under Creative Commons Attribution 3.0 Australia Licence with attribution to "The LIST ©State of Tasmania"

## Data processing

Alex Leith accessed zipped data on the 29th of November, 2023, and uploaded to Source.Coop.

license: Creative Commons Attribution 3.0 Australia Licence with attribution to "The LIST ©State of Tasmania"

providers: role [processor] for 'Alex Leith' is an AI guess — Described as having accessed and uploaded the data; assumed processor role. Verify before flipping to _state: reviewed.


Sample Earth — Tree Crop Reference Dataset — (Colombia, Ghana)

This dataset contains GPS-located land-cover samples that can be used to train and validate AI models that generate detailed, accurate maps, with a focus on coffee and cocoa production systems. The data were collected across Colombia and Ghana through expert interpretation of high-resolution satellite imagery. Each sample is labelled and quality-controlled to represent the land-cover types. The classification scheme includes four main classes: coffee, cocoa, tree crops, and seasonal agriculture.


Experiments with Source Cooperative

Experiments with source cooperative


FireSmoke Zarr

BlueSky Canada smoke forecast from the Weather Forecast Research Team at the University of British Columbia, reprocessed into zarr-v3 format for cloud-optimized access. The dataset contains four-times-daily atmospheric smoke dispersion predictions derived from wildfire activity in Canada.

zarr weather-forecasting weather air-quality atmospheric-data meteorology time-series environmental-monitoring wildfire conservation

Weather Forecast Research Team, University of British Columbia (BlueSky Canada)

Dataset files (1 x .zarr)

Source Cooperative product page

## README.md
# BlueSky Canada Smoke Forecast in zarr-v3

This data product contains the [BlueSky Canada smoke forecast](https://firesmoke.ca/about/) from the Weather Forecast Research Team at the University of British Columbia, reprocessed in zarr-v3 format.

It updates 4 times daily from Github Actions workflows.

* Github: https://github.com/alukach/firesmoke/
* FireSmoke: https://firesmoke.ca


A test product


Canada Natural Gas – Imports and Exports

This repository contains historical data on Canada's natural gas production, exports, and imports, regulated by the Canada Energy Regulator, covering activities from 1985 to present.

natural gas
canada
imports
exports
cer
neb


repotoday

today


testrepo12


TestRepo401


cozip

Demo archives for cozip. Open a ZIP like a table.
https://github.com/asterisk-labs/cozip