Plantacion de masas arbustivas. ZONA 3

Geolocation of shrub and perennial plant (masas arbustivas y vivaces) plantations in Zone 3 (Lote 3) of a Spanish municipal green infrastructure program. Each record captures the planting location, species, action type, improvement status, and scheduling information for individual shrub planting units across administrative districts.

conservation biodiversity land-use environmental-monitoring urban-forestry vegetation-management

2026

DISTRITO

[placename] Administrative district name where the planting is located

LOCALIZAC

[placename] Named location or address of the planting unit

geometry

[geometry] Polygon/point geometry in EPSG:25830 (ETRS89 / UTM zone 30N)

STRUCT(xmin FLOAT, ymin FLOAT, xmax FLOAT, ymax FLOAT)

2 files sampled for this table

4326

[geometry] Polygon/point geometry in OGC:CRS84 (WGS84 lon/lat)

2 files sampled for this table

Source Cooperative product page

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

providers: role [producer, licensor] for 'Spanish municipal authority (inferred from administrative column structure and EPSG:25830)' is an AI guess — No README; inferred from Spanish projected CRS (ETRS89/UTM zone 30N) and columns such as DISTRITO consistent with a Spanish municipal open data publication. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [2026.geometry (geometry), 2026.DISTRITO (placename), 2026.LOCALIZAC (placename), 4326.geometry (geometry), 4326.DISTRITO (placename), 4326.LOCALIZAC (placename)]. Add bbox once the placename → bbox gazetteer covers it.


EUBUCCO

Geospatial database of 200+ million individual building footprints across 27 EU countries and Switzerland, with attributes on building type, height, and construction year. Harmonized from 50 open government datasets and OpenStreetMap.

building-footprints europe building-height construction-year

EU member states (government data sources)

Dataset files (3224 x .parquet)

Source Cooperative product page


Energy Performance Certificate Ratings (Domestic) - England and Wales

Energy Performance Certificates (EPCs) for domestic properties in England and Wales, converted to cloud-native geoparquet format. Each record contains energy efficiency ratings, inspection dates, potential energy savings, and property locations with UPRN identifiers and local authority attribution.

energy-efficiency property-ratings domestic-buildings building-performance uk-property

Dataset files (344 x .parquet)

Source Cooperative product page

## README.md
# Energy Performance Certificates (England and Wales)
## Description
This dataset is a copy of the [Energy Performance Certificates](https://epc.opendatacommunities.org/) for England and Wales made available in a [cloud-native geospatial](https://cloudnativegeo.org) geoparquet format. The original dataset is distributed as a zipped collection CSVs, and is available for download from [here](https://epc.opendatacommunities.org/).

## Accessing the Data
The data has been made available as admin-partitioned geoparquet.You can query the entire dataset using DuckDB like so.
```bash
duckdb -c "Select count(*) from read_parquet('s3://us-west-2.opendata.source.coop/addresscloud/epc/geoparquet-local-authority/*.parquet');"
```
Alternatively, you can use the aws cli to access the data directly from the S3 bucket:
```bash
aws s3 --no-sign-request ls s3://us-west-2.opendata.source.coop/addresscloud/epc/
```
Or if you want the data for just one local authority then choose one from the browse section of this page.

The geoparquet format is compliant with GDAL 3.5 onwards and readable in QGIS and many other platforms. See the [geoparquet website]() for more info on this

## Data extraction process
These steps should help you emulate the process of extracting the EPC The data source data exists [here](https://epc.opendatacommunities.org/). You will need to sign up. Once doing this you should find the data avaible to download as a ZIP.
This ZIP contains a directory for each local authortiy in England and Wales. I've created this script to extract just the certificates.csv from each directory.
```bash
#!/bin/bash
# Zip file name in the current directory
zip_file="all-domestic-certificates.zip"
# Loop through the zip file contents and extract certificates.csv
unzip -l "$zip_file" | grep certificates.csv | while read -r line; do
file_name=$(echo "$line" | awk '{print $NF}')
folder_name=$(dirname "$file_name")
# Extract the specific file and save it with the folder name as filename
unzip -p "$zip_file" "$file_name" > "epc_csv/${folder_name//\//-}.csv"
done
```
## Data conversion
The next steps involve using the DuckDB client to load the data, geocoding each row by joining to the [OS Open UPRN dataset](https://www.ordnancesurvey.co.uk/products/os-open-uprn) and then exporting the entire dataset to admin-partitioned parquet files. Then we'll need to convert these to geoparquet using gpq tool.
There are 347 CSVs totalling 23.4 GB in size. You can convert the data to parquet using various tools. Once you've done that the total combined file size of the Parquet files is only 3.7 GB.
The steps below
```bash
$ duckdb
```
```sql
create or replace view epc_certs as select * from read_parquet('epc/*.parquet');
```
```sql
create or replace view opuprn as select * from read_parquet('opuprn.parquet');
```
```sql
create or replace view epc as
select epc_certs.*,
opuprn.LATITUDE as lat,
opuprn.LONGITUDE as lon
from epc_certs
join opuprn ON epc_certs.uprn = opuprn.uprn::text;
```
Once all the views have been created we can then export to partionied parquet files. The partition used here is the local authority name.
```sql
install spatial;
load spatial;
COPY (Select UPRN as uprn,
LOCAL_AUTHORITY as local_authority,
LOCAL_AUTHORITY_LABEL as local_authority_label,
CURRENT_ENERGY_RATING as current_energy_rating,
POTENTIAL_ENERGY_RATING as potential_rating,
INSPECTION_DATE as inspection_date,
ST_AsText(ST_Point(lon::double,lat::double)) as geometry
from epc)
TO 'epc_partitioned' (FORMAT PARQUET, PARTITION_BY (LOCAL_AUTHORITY_LABEL));
```
## Converting to Geoparquet
It is currently not possible to export to geoparquet using DuckDB so to convert all the outputted parquet to geoparquet and remove the parquet files I created this script
```bash
#!/bin/bash
shopt -s globstar
for d in ./**/*.parquet
do
dir="${d%/*}" # Strip the *.parquet pathname back to the containing directory
#echo $dir
parquet="${d##*/}" # Strip the *.parquet pathname back to just the filename
new_name=$(echo "$dir" | tr ' ' _ |cut -d'=' -f2)
#echo "Converted $parquet to geoparquet" >&2
(
cd "$dir" || exit
find . -type f -name "*.parquet" -size -100b -delete
if [ -f $parquet ]
then
gpq convert $parquet $new_name.parquet
fi
)
done
for d in ./**/*.parquet
do find . -type f -name "data*" -delete;
done
```
## Licence
Date is licensed under the Open Government Licence v3.0. More details can be found [here](https://epc.opendatacommunities.org/docs/copyright).
## Contact
This is an experimental dataset and I hope to add more attributes in the future. If you have any questions about the process you can contact me using matt@addresscloud.com


Invasive species occurrence and distribution mapping dataset for the Adirondack region, likely covering New York State's Adirondack Park. No README is available; description is inferred from the dataset title alone.

invasive-species adirondacks new-york biodiversity species-mapping ecology

# Adirondack Research Invasive Species Mapping

### Adirondack Research Invasive Species Mapping

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/adkres-invasive`

#### Overview

This data set contains interpolated lake characteristics data of twelve lakes, including depth, substrate hardness, and vegetation presence. These data can be useful for calculating the probability of occurrence of other biological organisms that have habitat preferences related to water depth and substrate. More information about the survey methodology is available [here](https://adkres.org/portfolio/aquatic-invasive-species-early-detection-and-rapid-response-with-microsoft/).

#### Citation, license, and contact information

For questions about this data set, contact [Ezra Schwartzberg](mailto:ezra@adkres.org) at Adirondack Research.

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 .csv format, with the following columns:

* **Distance_from_shore** (feet)
* **Depth** (feet)
* **Bottom_Hardness** (relative units)
* **Vegetation** (relative units, indicating vegetation height)
* **Longitude**
* **Latitude**
* **Lake_Name**
* **County**
* **Township**
* **Date_surveyed**
* **AIS_Species** (name of invasive species present, if any)
* **AIS_Density** (relative units)

#### Downloading the data

Data download links:

[Download from GCP](https://storage.googleapis.com/public-datasets-lila/adkres-invasive/data_12_lakes.zip) (155MB)

[Download from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/adkres-invasive/data_12_lakes.zip) (155MB)

[Download from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/adkres-invasive/data_12_lakes.zip) (155MB)

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

![](http://lila.science/wp-content/uploads/2020/06/adkinvasives650.jpg)


Aerial Seabirds West Africa

Aerial survey dataset of seabird detections along the West African coast, comprising labeled observations of six species: Caspian tern, Royal tern, Gray-headed gull, Slender-billed gull, Great cormorant, and Great white pelican. Assets include aerial imagery (GeoTIFF and JPEG), georeferenced vector data (GeoPackage), and CSV annotation files with per-detection species labels and confidence flags.

remote-sensing aerial-survey seabirds west-africa wildlife-detection bird-classification object-detection machine-learning

(unknown)
Caspian tern
Gray headed gull
Great cormorant
Great white Pelican
Royal tern
Slender-billed gull
label

labels_birds_full.csv

Source Cooperative product page

extent.spatial.bbox: omitted; probe found no spatial dimension and AI did not flag any column as geographic.


Biome Health Project Maasai Mara 2018

Field survey imagery collected by the Biome Health Project in the Maasai Mara ecosystem during 2018. The dataset consists of approximately 37,075 JPEG images accompanied by a single JSON metadata file, likely representing camera trap or ground-survey photographs used to assess wildlife and habitat health.

maasai-mara wildlife camera-trap biodiversity kenya ecology

# Biome Health Project Maasai Mara 2018

### Biome Health Project Maasai Mara 2018

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/biome-health-project-maasai-mara-2018`

#### Overview

This dataset contains 37,075 images from the [WWF-UK/UCL Biome Health Project](https://www.biomehealthproject.com/) site in the Maasai Mara, Kenya. These images form the training, test, and validation sets for the [Maasai Mara Classifier model](https://github.com/omipan/camera_traps_classifier). This dataset was created by labeling one image per five-minute period across 176 camera sites.

Labels are provided for 100 categories, most of which are species, but some of which are species groups (for example, the 'shoat' category is sheep and goats combined). Labels include wild mammals, wild birds, and domestic mammals. Blank images and images containing humans have been removed.

#### Citation, license, and contact information

If you use this dataset, please cite:

Connolly E, Pringle HA, Pantazis O, Ferreira GB, Madsen EK, Ingram DJ, Bains T, Brostow GJ, Carroll S, Cronshaw G, De Ornellas P, Di Minin E, Ewers RM, Gichangi K, Mac Aodha O, Mulama M, Njuguna M, Pattullo L, Pickering A, Rabeau A, Rowcliffe M, Spooner F, Thomas L, Wato Y, Woodhouse E, Collen B, Mace GM, Jones KE. [Sustainable cattle management by communities supports African wildlife](https://www.biorxiv.org/content/10.1101/2025.10.09.681397v2.abstract). bioRxiv. 2025 Oct 10:2025-10.

For questions about this data set, contact [Kate Jones](mailto:kate.e.jones@ucl.ac.uk) at University College London.

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/biome-health-project-maasai-mara-2018/biome-health-project-maasai-mara-2018.json).

Images are available in the following cloud storage folders:

* gs://public-datasets-lila/biome-health-project-maasai-mara-2018 (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/biome-health-project-maasai-mara-2018 (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/biome-health-project-maasai-mara-2018 (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:

IMG_1686.JPG

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

https://storage.googleapis.com/public-datasets-lila/biome-health-project-maasai-mara-2018/IMG_1686.JPG

http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/biome-health-project-maasai-mara-2018/IMG_1686.JPG

https://lilawildlife.blob.core.windows.net/lila-wildlife/biome-health-project-maasai-mara-2018/IMG_1686.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/).

![two zebras in a camera trap image](http://lila.science/wp-content/uploads/2025/11/biome-mara-IMG_1686_700.jpg)

## Non-camera-trap datasets


Forest Canopy Height in Mexican Ecosystems

Raster dataset of forest canopy height across Mexican ecosystems, covering central to southeastern Mexico. Contains 1,105 GeoTIFF files (~62 GB) representing canopy height measurements derived from remote sensing.

remote-sensing land-use forest canopy-height mexico land-cover

Dataset files (1105 x .tif)

Source Cooperative product page

providers: role [producer] for 'Unknown' is an AI guess — No README; producer inferred from dataset subject — likely a Mexican or international research institution. Verify before flipping to _state: reviewed.


Boxes on Bees and Pollen

A dataset titled 'Boxes on Bees and Pollen', distributed as a single ZIP archive (~2 GB). No README is available; the subject matter appears to relate to bee colonies or hive boxes and pollen, but content, coverage, and methodology cannot be confirmed without documentation.

# Boxes on Bees and Pollen

### Boxes on Bees and Pollen

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/boxes-on-bees`

#### Overview

The goal of the [BeeLivingSensor](https://en.beelivingsensor.org/) project is to non-invasively track honey bees at hive entrances, and to track the type and volume of pollen they bring into the hive. By analyzing the color of the pollen and aggregating it with other data, the project aims to to determine the plant biodiversity around the beehive.

This data set contains approximately 5,000 images of bees annotated with bounding boxes on both bees and pollen, for a total of around 50,000 annotations.

#### Data format

The zipfile linked below contains 4993 image files, each of which is associated with an .xml file of the same name. For example, "Chueried_Churied_01_ST_216.xml" contains the annotations for the image "Chueried_Churied_01_ST_216.jpg".

Annotations are in the [Pascal VOC XML format](https://roboflow.com/formats/pascal-voc-xml#w-tabs-0-data-w-pane-3) for objection detection. A typical individual bee object, for example, would be annotated as:

```

bee
Unspecified
0
0

1550
1216
1675
1282

```
#### Citation

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

[Noninvasive bee tracking in videos: deep learning algorithms and cloud platform design specifications](https://drive.google.com/file/d/10RQTUYoL42aGlVVYzzCWjOv31g3D9T1n/view?usp=sharing). Dataset, 2021.

#### Contact information

For questions about this data set, contact the project team via the project's [contact form](https://en.beelivingsensor.org/blank).

#### License

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

#### Downloading the data

This dataset is provided as a single zipfile:

[Download from GCP](https://storage.googleapis.com/public-datasets-lila/boxes-on-bees/boxes-on-bees-2021.zip) (2GB)

[Download from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/boxes-on-bees/boxes-on-bees-2021.zip) (2GB)

[Download from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/boxes-on-bees/boxes-on-bees-2021.zip) (2GB)

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

![](http://lila.science/wp-content/uploads/2021/03/bee_thumb.png)


California Small Animals

A large image collection of small animals in California, comprising approximately 2.28 million photographs (JPG and PNG). The dataset likely documents wildlife or fauna species observed across California, though specific subjects, taxonomy, and collection methodology are not documented.

california small-animals wildlife wildlife-photography fauna images

# California Small Animals

### California Small Animals

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/california-small-animals`

#### Overview

The dataset contains 2,278,071 images, collected by the California Department of Fish and Wildlife following a standardized [protocol](https://storymaps.arcgis.com/collections/a6a06b22b05f43cd863568b2690b08c1?item=1). These data were collected to better understand the potential impacts of land use and climate-related changes on wildlife populations in California. The images were collected from downward-facing, short-focus (factory modified to 40 cm) Reconyx cameras, which were deployed in combination with a drift fence. Image labeling was conducted using the [Wildlife Insights](https://wildlifeinsights.org) platform.

Target species include small mammals, reptiles, and amphibians. In general, images were identified to the taxonomic level possible based on the image alone (species distributions within California were not considered) following these recommendations for [mammals](https://experience.arcgis.com/experience/35f1057df2c746d2b6b888485201aa86/page/ID-Guides?views=Mammals) and for [amphibians and reptiles](https://experience.arcgis.com/experience/35f1057df2c746d2b6b888485201aa86/page/ID-Guides?views=Reptiles-%26-Amphibians). The most common categories are blank/misfire (1,320,489 images), mouse species (252,986 images), arvicolinae subfamily (124,342 images), and rodent (112,248 images).

#### Citation, license, and contact information

If you use this dataset, please cite:

Cannabis and Sentinel Site Projects, California Department of Fish and Wildlife

For questions about this data set, contact [Lindsey Rich](mailto:lindsey.rich@wildlife.ca.gov).

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

#### 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/california-small-animals/california_small_animals_with_sequences.zip).

Images are available in the following cloud storage folders:

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

2002875_cemap-small-animals_exclude-identify/images/deployment/2238003/prod/directUpload/742e531e-cd87-4b6d-9ebf-88986ab3bcf0.JPG

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

https://storage.googleapis.com/public-datasets-lila/california-small-animals/2002875_cemap-small-animals_exclude-identify/images/deployment/2238003/prod/directUpload/742e531e-cd87-4b6d-9ebf-88986ab3bcf0.JPG

http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/california-small-animals/2002875_cemap-small-animals_exclude-identify/images/deployment/2238003/prod/directUpload/742e531e-cd87-4b6d-9ebf-88986ab3bcf0.JPG

https://lilawildlife.blob.core.windows.net/lila-wildlife/california-small-animals/2002875_cemap-small-animals_exclude-identify/images/deployment/2238003/prod/directUpload/742e531e-cd87-4b6d-9ebf-88986ab3bcf0.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/).

![weasel in a camera trap image](http://lila.science/wp-content/uploads/2025/10/deployment_2238003_prod_directUpload_742e531e-cd87-4b6d-9ebf-88986ab3bcf0.jpg)


Caltech Camera Traps

A large collection of 244,584 camera trap photographs assembled by the California Institute of Technology for wildlife research and computer vision benchmarking. The images support development and evaluation of automated animal detection and classification algorithms in real-world field conditions.

camera-traps wildlife animal-detection computer-vision ecology

# Caltech Camera Traps

### Caltech Camera Traps

#### S3 base

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

#### Overview

This data set contains 243,100 images from 140 camera locations in the Southwestern United States, with labels for 21 animal categories (plus empty), primarily at the species level (for example, the most common labels are opossum, raccoon, and coyote), and approximately 66,000 bounding box annotations. Approximately 70% of images are labeled as empty.

More information about this data set is available [here](https://beerys.github.io/CaltechCameraTraps/).

#### Citation, license, and contact information

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

Sara Beery, Grant Van Horn, Pietro Perona. Recognition in Terra Incognita. Proceedings of the 15th European Conference on Computer Vision (ECCV 2018). ([bibtex](http://lila.science/wp-content/uploads/2018/11/BeeryHP18.bib))

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 [caltechcameratraps@gmail.com](mailto:caltechcameratraps@gmail.com).

#### 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.

We have also divided locations (i.e., cameras) into training and validation splits to allow for consistent benchmarking on this data set. The file describing this split specifies a train/val split for all locations in the data set, and also provides the train/val split used in the ECCV paper listed above. The "eccv_train" split here corresponds to the "train" locations and all "cis" locations in the ECCV paper; the "eccv_val" split here corresponds to all "trans" locations in the ECCV paper.

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/caltech-unzipped/cct_images (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/caltech-unzipped/cct_images (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/caltech-unzipped/cct_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).

* Images (105GB) ([GCP link](https://storage.googleapis.com/public-datasets-lila/caltechcameratraps/cct_images.tar.gz)) ([Azure link](https://lilawildlife.blob.core.windows.net/lila-wildlife/caltechcameratraps/cct_images.tar.gz)) ([AWS link](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/caltechcameratraps/cct_images.tar.gz))

Metadata download links:

* [Image-level annotations](https://storage.googleapis.com/public-datasets-lila/caltechcameratraps/labels/caltech_camera_traps.json.zip) (9MB)
* [Bounding box annotations](https://storage.googleapis.com/public-datasets-lila/caltechcameratraps/labels/caltech_bboxes_20200316.json) (35MB)
* [Recommended train/val splits](https://storage.googleapis.com/public-datasets-lila/caltechcameratraps/CaltechCameraTrapsSplits_v0.json) (4KB)

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

#### CCT20 Benchmark subset

The initial publication of this data set in [Beery et al. 2018](https://openaccess.thecvf.com/content_ECCV_2018/html/Beery_Recognition_in_Terra_ECCV_2018_paper.html) proposed a specific subset of Caltech Camera Traps data for benchmarking. Images in this benchmark dataset (CCT20) have been downsized to a maximum of 1024 pixels on a side.

The CCT20 benchmark set is available here:

[Benchmark images](https://storage.googleapis.com/public-datasets-lila/caltechcameratraps/eccv_18_all_images_sm.tar.gz) (6GB)

[Metadata files for train/val/cis/trans splits](https://storage.googleapis.com/public-datasets-lila/caltechcameratraps/eccv_18_annotations.tar.gz) (3MB)

#### 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/CaltechCameraTraps_web.jpg)


Channel Islands Camera Traps

Camera trap photograph dataset from the Channel Islands, comprising approximately 240,000 JPEG images collected for wildlife monitoring. Suitable for ecological research and machine learning model development on island fauna.

camera-traps wildlife-monitoring channel-islands island-ecology biodiversity

# Channel Islands Camera Traps

### Channel Islands Camera Traps

#### S3 base

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

#### Overview

This data set contains 246,529 camera trap images from 73 camera locations in the Channel Islands, California. All animals are annotated with bounding boxes. Data were provided by The Nature Conservancy.

Animals are classified as rodent1 (82914), fox (48150), bird (11099), skunk (1071), or other (159). 114,949 images (47%) are empty.

1All images of rats were taken on islands already known to have rat populations.

#### Citation, license, and contact information

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

The Nature Conservancy (2021): Channel Islands Camera Traps 1.0. The Nature Conservancy. Dataset.

For questions about this data set, contact [Nathaniel Rindlaub](mailto:nathaniel.rindlaub@TNC.ORG) at The Nature Conservancy.

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/).

#### Downloading the data

Metadata is available [here](https://storage.googleapis.com/public-datasets-lila/channel-islands-camera-traps/channel-islands-camera-traps.json.zip) (18MB).

Images are available as a single zipfile:

[Download from GCP](https://storage.googleapis.com/public-datasets-lila/channel-islands-camera-traps/channel-islands-camera-traps-images.zip) (86GB)

[Download from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/channel-islands-camera-traps/channel-islands-camera-traps-images.zip) (86GB)

[Download from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/channel-islands-camera-traps/channel-islands-camera-traps-images.zip) (86GB)

Images are also available (unzipped) in the following cloud storage folders:

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

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/2020/12/channel-islands-thumb.jpg)


Community Fish Detection Dataset

A large community-contributed image dataset for fish detection, comprising approximately 1.9 million images in JPG and PNG format totalling ~261 GB. Intended for training and evaluating fish detection or classification models.

fish-detection computer-vision image-classification aquatic wildlife machine-learning

# Community Fish Detection Dataset

### Community Fish Detection Dataset

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/community-fish-detection-dataset`

#### Overview

This dataset is a curated compilation of fish imagery and annotations from a variety of publicly available datasets, with a focus on object detection. It includes >1.9M images/frames and >935,000 bounding boxes from around 17 individual datasets, spanning freshwater, marine, and lab-based captures.

The original datasets include bounding box annotations and metadata in a variety of formats, and the original labels vary in granularity from species-level identifications to generic “fish” labels. This dataset harmonizes the source metadata into a single COCO-formatted archive, with a single category (“fish”).

#### License and contact information

Each dataset is licensed under its own terms. Most are under permissive licenses including CC-BY, CC-BY-SA, CC0, or CDLA, but some licenses include an NC restriction. See below for individual dataset license information.

For questions about this dataset, contact [Filippo Varini](mailto:fppvrn@gmail.com).

#### Data format

Data is provided in COCO .json format, with a single category (“fish”). In addition to the standard COCO fields, the following fields are included:

* *dataset*: the name of the original dataset from which this image (or frame) was collected
* *original_data_source*: the filename of this image in the original dataset
* *is_train*: a reference train/val split was selected, using location information whenever it was available to ensure that any location appeared in the training or validation splits, but not both. Location information was not always available, so it’s not guaranteed to be the case that the same background appears exclusively in one split.

#### Downloading the data

Metadata is available [here](https://lilawildlife.blob.core.windows.net/lila-wildlife/community-fish-detection-dataset/community_fish_detection_dataset.json.zip).

Images are available in the following cloud storage folders:

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

JPEGImages/marine_detect_3759335805_train.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/community-fish-detection-dataset/JPEGImages/marine_detect_3759335805_train.jpg](https://storage.googleapis.com/public-datasets-lila/community-fish-detection-dataset/JPEGImages/marine_detect_3759335805_train.jpg)

http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/community-fish-detection-dataset/JPEGImages/marine_detect_3759335805_train.jpg

https://lilawildlife.blob.core.windows.net/lila-wildlife/community-fish-detection-dataset/JPEGImages/marine_detect_3759335805_train.jpg

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

#### Constituent datasets

Brief descriptions and license information for each dataset are included here; for more detailed information and thumbnails from each dataset, see the [fish-datasets](https://github.com/filippovarini/fish-datasets?tab=readme-ov-file) GitHub repo. The dataset from which each image was drawn is available in the “dataset” field in the .json file.

[**The Brackish Dataset**](https://www.kaggle.com/datasets/aalborguniversity/brackish-dataset)

This dataset includes 89 short videos (~14,764 extracted frames) from brackish water in Denmark, annotated with boxes on fish, crabs and other fauna. This dataset is released under CC BY-SA 4.0.

[**Coralscapes**](https://josauder.github.io/coralscapes/)

This dataset contains 2,027 images captured by diver-borne GoPro cameras from a variety of global coral reefs. This dataset is licensed under Apache 2.0.

[**Deep Vision Fish Dataset**](https://metadata.nmdc.no/metadata-api/landingpage/01d102345aef4639f063a13ea20cd3f3)

This dataset is constructed from images collected using the Deep Vision system during two surveys from 2017 and 2018 that targeted economically important pelagic species. The fish species annotated are blue whiting, Atlantic herring, mesopelagic fishes and Atlantic mackerel. This dataset is licensed under CC-BY 4.0.

[**DeepFish**](https://alzayats.github.io/DeepFish/)

This dataset includes ~40k seafloor images from 20 tropical habitats in northern Australia, aimed at benchmarking habitat‑aware fish detection and segmentation. The original dataset contains a mix of classification, segmentation, and counting labels; only images with segmentation masks were used here, resulting in 310 images with 338 annotations. The subset used here is redistributed under the MIT License.

[**F4K Detection and Tracking**](https://github.com/perceivelab/f4k-detection-and-tracking)

This dataset includes 17 10-minute videos representing a variety of water conditions, with segmentation masks on a subset of frames. This dataset’s license is not explicitly stated.

[**FathomNet**](https://database.fathomnet.org/fathomnet/#/)

FathomNet is an image database containing a variety of marine taxa; the subset used here is intended to capture fish. Specifically, this subset includes descendents of *Actinopterygii*, *Sarcopterygii*, *Chondrichthyes*, and *Myxini*. This dataset is released under CC-BY-ND 4.0

[**FishCLEF‑2015**](https://github.com/perceivelab/FishCLEF-2015)

This dataset includes 93 videos annotated with bounding boxes and fish species (14k annotations on 20k frames). This dataset’s license is not explicitly stated.

[**Tropical freshwater fish in Northern Australia**](https://zenodo.org/records/7250921#.ZEAmZezMJqs)

This dataset includes 44,112 images with 82,904 bounding box annotations for 23 tropical freshwater fish taxa from northern Australia. Images were derived from Remote Underwater Video (RUV) deployments in deep channel and shallow lowland billabongs, Kakadu National Park, Northern Territory Australia. RUV deployments were conducted during the Supervising Scientists annual fish monitoring program in the 2016, 2017 and 2018 recessional flow period (dry season). This dataset is released under CC-BY 4.0.

[**Marine Detect**](https://github.com/Orange-OpenSource/marine-detect)

This dataset is itself a composite of several other publicly-available datasets; most of the sub-datasets come either from GBIF or from Roboflow Universe. A list of the sub-datasets is available [here](https://github.com/Orange-OpenSource/marine-detect?tab=readme-ov-file#datasets). Datasets from Roboflow universe are licensed under CC-BY 4.0; datasets from GBIF are licensed under CC-BY-NC 4.0.

[**MIT Sea Grant River Herring**](https://lila.science/datasets/mit-sea-grant-river-herring/)

This dataset was developed from 1,434 video clips from rivers in Massachusetts, USA, comprising a total of ~262k frames, with ~91k bounding boxes annotated on ~60k of those frames (the remaining ~162k frames were reviewed and classified as empty). This dataset is released under CDLA-permissive 1.0.

[**Puget Sound Nearshore Fish**](https://lila.science/datasets/noaa-puget-sound-nearshore-fish)

This dataset contains 77,739 images sampled from video collected on and around shellfish aquaculture farms in an estuary in the Northeast Pacific, in which 67,990 objects (fish and crustaceans) have been annotated on 30,384 images (the remainder have been annotated as “empty”). This dataset is released under CDLA-permissive 1.0.

[**Project Natick Underwater Video**](https://github.com/microsoft/Project_Natick_Analysis)

This auxiliary release from Microsoft’s "Project Natick" underwater data center experiment provides 1,072 RGB images (with box annotations on fish and squid) recorded by cameras fixed on the outside of the data center pod. This dataset is released as part of an MIT-licensed software project, but the dataset license is not stated explicitly.

[**Roboflow Fish Dataset**](https://public.roboflow.com/object-detection/fish)

This dataset is a collection of 1,350 images and boxes covering 26 fish categories, sourced from the Web. This dataset is released under CC0.

[**Salmon Computer Vision**](https://github.com/Salmon-Computer-Vision/salmon-computer-vision)

This dataset includes 532,000 frames labeled with bounding boxes on 15 species from a weir on the Kitwanga River in BC, Canada. This dataset is licensed under CC-BY-NC-SA 4.0.

[**Tasmanian Orange Roughy Stereo Image Machine Learning Dataset (TORSI)**](https://data.csiro.au/collection/64913)

This dataset is a collection of annotated stereo image pairs collected by a net-attached Acoustic and Optical System (AOS) during orange roughy (Hoplostethus atlanticus) biomass surveys off the northeast coast of Tasmania, Australia in July 2019. This dataset is released under CC-BY-NC-SA 4.0.

[**FishTrack23**](https://viame.kitware.com/#/collection/65a140e8a4c218785d408b42)

This dataset includes approximately 850k bounding boxes across 26 tracks in a variety of ocean conditions. This dataset is released under CC-BY 4.0.

[**AAU Zebrafish Re-Identification Dataset**](https://www.kaggle.com/datasets/aalborguniversity/aau-zebrafish-reid)

This dataset includes ~2200 images of zebrafish in a lab environment with bounding box annotations and individual identifications. Six fish were recorded in total, in two sessions with three fish at a time. A 32 x 32 x 32 cm fish tank was used, with the fish constrained to swim within the first 3.5 cm of the tank. This dataset is released under CC BY 4.0.

![fish with bounding boxes](http://lila.science/wp-content/uploads/2025/08/marine_detect_3759335805_train_gt.jpg)


Conservation Drones

Imagery and associated data collected by conservation drones (UAVs) for wildlife and habitat monitoring purposes. The dataset is dominated by aerial photographs (JPEG and PNG) alongside tabular files, consistent with systematic survey flights over natural areas. No README is present to clarify scope, species, or geographic coverage.

remote-sensing conservation wildlife-monitoring drone-imagery aerial-imagery uav

# Conservation Drones

### Conservation Drones

#### S3 base

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

#### Overview

Monitoring of protected areas to curb illegal activities like poaching is a monumental task. Real-time data acquisition has become easier with advances in unmanned aerial vehicles (UAVs) and sensors like TIR cameras, which allow surveillance at night when poaching typically occurs. However, it is still a challenge to accurately and quickly process large amounts of the resulting TIR data. The Benchmarking IR Dataset for Surveillance with Aerial Intelligence (BIRDSAI, pronounced "bird's-eye") is a long-wave thermal infrared (TIR) dataset containing nighttime images of animals and humans in Southern Africa. The dataset allows for testing of automatic detection and tracking of humans and animals with both real and synthetic videos, in order to protect animals in the real world.

There are 48 real aerial TIR videos and 124 synthetic aerial TIR videos (generated with [AirSim](https://github.com/microsoft/AirSim)), for a total of 62k and 100k images, respectively. Tracking information is provided for each of the animals and humans in these videos. We break these into labels of animals or humans, and also provide species information when possible, including for elephants, lions, and giraffes. We also provide information about noise and occlusion for each bounding box.

#### Data layout

In the training set that is provided, there are two folders, one for simulated data (TrainSimulation), one for real data (TrainReal). Each of these folders contains folders for the annotation .csv files for each video (annotations) and the individual .jpg frames in each video (images).

In the "images" folder in "TrainSimulation", there are folders for each video; in addition to the .jpg infrared images, these zip files also contain infrared .png, RGB, and segmentation images provided by AirSim. We include in "TrainSimulation/annotations" two files containing the infrared digital counts for the different objects in the scene for both winter and summer. These, combined with the infrared simulation .png files, allow you to search for different objects in the images, if you're looking for further information than is provided in the annotation .csv files.

The test set contains a single folder called "TestReal", which follows the same pattern as "TrainReal".

#### Annotation format

We follow the [MOT annotation format](https://motchallenge.net/), which is a .csv file with the following columns:

[frame_number], [object_id], [x], [y], [w], [h], [class], [species], [occlusion], [noise]

* *class*: 0 if animals, 1 if humans
* *species*: -1: unknown, 0: human, 1: elephant, 2: lion, 3: giraffe, 4: dog, 5: crocodile, 6: hippo, 7: zebra, 8: rhino. 3 and 4 occur only in real data. 5, 6, 7, 8 occur only in synthetic data.
* *occlusion*: 0 if there is no occlusion, 1 if there is an occlusion (i.e., either occluding or occluded) (note: intersection over union threshold of 0.3 used to assign occlusion; more details in paper)
* *noise*: 0 if there is no noise, 1 if there is noise (note: noise labels were interpolated from object locations in previous and next frames; for more than 4 consecutive frames without labels, no noise labels were included; more details in paper)

Within the "TrainReal/annotations" folders, we include a file called "water_metadata....txt", which contains has the names of videos that contain water.
The same is found in "TestReal/annotations".

Although "annotations" can be used for single- and multi-object tracking, we have included within "TrainReal/annotations" the exact training sequences and splits used in our single-object tracking experiments under "tracking". The same is included in "TestReal/annotations/tracking".

Helper code and a README is provided at [github.com/exb7900/BIRDSAI](https://github.com/exb7900/BIRDSAI) to give more detail on how to use these sequences.

#### Downloading the data

*Unzipped data*

Data is already unzipped and available in the following cloud storage folders; see LILA's [direct image access guide](https://lila.science/image-access) for download instructions.

**Google Cloud Storage folders**

gs://public-datasets-lila/conservationdrones/v01/TrainReal

gs://public-datasets-lila/conservationdrones/v01/TrainSimulation

gs://public-datasets-lila/conservationdrones/v01/conservationdrones-testset/TestReal

**AWS S3 folders**

s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/conservationdrones/v01/TrainReal

s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/conservationdrones/v01/TrainSimulation

s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/conservationdrones/v01/conservationdrones-testset/TestReal

**Azure Blob Storage folders**

https://lilawildlife.blob.core.windows.net/lila-wildlife/conservationdrones/v01/TrainReal

https://lilawildlife.blob.core.windows.net/lila-wildlife/conservationdrones/v01/TrainSimulation

https://lilawildlife.blob.core.windows.net/lila-wildlife/conservationdrones-testset/TestReal

*Big zipfiles*

You can also download the archive in three zipfiles (for real training data, simulated training data, and real test data).

**GCP links**

[conservation_drones_train_real.zip](https://storage.googleapis.com/public-datasets-lila/conservationdrones/v01/conservation_drones_train_real.zip) (2.1GB)

[conservation_drones_train_simulation.zip](https://storage.googleapis.com/public-datasets-lila/conservationdrones/v01/conservation_drones_train_simulation.zip) (39.5GB)

[conservation_drones_test_real.zip](https://storage.googleapis.com/public-datasets-lila/conservationdrones/v01/conservation_drones_test_real.zip) (1.6GB)

**AWS links**

[conservation_drones_train_real.zip](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/conservationdrones/v01/conservation_drones_train_real.zip) (2.1GB)

[conservation_drones_train_simulation.zip](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/conservationdrones/v01/conservation_drones_train_simulation.zip) (39.5GB)

[conservation_drones_test_real.zip](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/conservationdrones/v01/conservation_drones_test_real.zip) (1.6GB)

**Azure links**

[conservation_drones_train_real.zip](https://lilawildlife.blob.core.windows.net/lila-wildlife/conservationdrones/v01/conservation_drones_train_real.zip) (2.1GB)

[conservation_drones_train_simulation.zip](https://lilawildlife.blob.core.windows.net/lila-wildlife/conservationdrones/v01/conservation_drones_train_simulation.zip) (39.5GB)

[conservation_drones_test_real.zip](https://lilawildlife.blob.core.windows.net/lila-wildlife/conservationdrones/v01/conservation_drones_test_real.zip) (1.6GB)

#### Challenges

This dataset was used as the basis for the ICVGIP Visual Data Challenge at [ICVGIP 2020](http://www.iitj.ac.in/icvgip20-21/).

**Having trouble downloading?**

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

#### License, citation, and acknowledgements

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

If you use this dataset, please consider citing our paper:

Bondi E, Jain R, Aggrawal P, Anand S, Hannaford R, Kapoor A, Piavis J, Shah S, Joppa L, Dilkina B, Tambe M. BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos. ([bibtex](http://lila.science/wp-content/uploads/2020/02/conservationdrones.txt))

For questions about this dataset, contact Elizabeth Bondi at Harvard University ([ebondi@g.harvard.edu](mailto:ebondi@g.harvard.edu)).

This work was supported by Microsoft AI for Earth, NSF grants CCF-1522054 and IIS-1850477, MURI W911NF-17-1-0370, and the Infosys Center for Artificial Intelligence, IIIT-Delhi. We also thank the labeling team.

[video src="https://lilawildlife.blob.core.windows.net/lila-wildlife/conservationdrones/conservation_drones_0000000367_0000000000.mp4" poster="https://lilawildlife.blob.core.windows.net/lila-wildlife/conservationdrones/conservation_drones_0000000367_0000000000.jpg"]


Amur Tiger Re-identification

Amur Tiger Re-identification in the Wild (ATRW) is a computer-vision benchmark dataset containing images of 92 individual Amur tigers from 10 zoos in China, packaged with ~9,500 bounding boxes, pose keypoints, and ~3,600 individual identity labels. Data are split into detection, pose-estimation, and re-identification tasks with train/val/test partitions matching those used in the ICCV 2019 Workshop on Computer Vision for Wildlife Conservation challenge.

amur-tiger wildlife biodiversity re-identification object-detection machine-learning pose-estimation computer-vision zoo

LILA (Labeled Information Library of Alexandria)

Dataset files (11 x .gz)

Parent Source Cooperative product page

Li, S., Li, J., Lin, W., & Tang, H. (2019). Amur Tiger Re-identification in the Wild. arXiv preprint arXiv:1906.05586.

sci_doi is null: the citation references arXiv:1906.05586 which is a preprint, not a formal DOI; a published DOI may exist but is not stated in the README.

temporal_extent is [null, null]: no image-capture date range is given in the README; the dataset was published in 2019 but the actual study/capture period is unspecified.

spatial_extent is zoo-based (captive animals in China); no geographic bbox is derivable from the README or inventory.

The README says 'more than 8,000 video clips' but the actual files are tar archives of images (not video clips); the description likely means image crops or frames extracted from video.

extent.spatial.bbox: omitted; named region: China (10 zoos)


Desert Lion Conservation Camera Traps

Camera trap imagery and video collected for desert lion conservation monitoring. The dataset comprises over 63,000 photographs and nearly 200 video clips, likely from motion-triggered cameras deployed across desert terrain to observe lion presence, behavior, and movement.

camera-traps wildlife-monitoring lions desert conservation carnivores

# Desert Lion Conservation Camera Traps

### Desert Lion Conservation Camera Traps

#### S3 base

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

#### Overview

This dataset contains 65,959 images and 199 videos from the [Desert Lion Conservation Project](https://www.desertlion.info) in Northern Namibia. Desert Lion Conservation is a small non-profit organisation dedicated to the conservation of desert-adapted lions. Their main focus is to collect important baseline ecological data on the lion population and to study their behaviour, biology, and adaptation to survive in the harsh environment. They use this information to collaborate with other conservation bodies in the quest to find a solution to human-lion conflict, to elevate the tourism value of lions, and to contribute to the conservation of the species.

Labels are provided for 46 categories, primarily at the species level. There are no images annotated as empty.

#### License and contact information

For questions about this data set, contact [Peter van Lunteren](mailto:peter@addaxdatascience.com).

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://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/).

#### Downloading the data

Metadata is available [here](https://storage.googleapis.com/public-datasets-lila/desert-lion-camera-traps/desert_lion_camera_traps.json.zip).

Images are available in a zipfile [here](https://storage.googleapis.com/public-datasets-lila/desert-lion-camera-traps/desert-lion-camera-traps-images.zip), and unzipped images are available in the following cloud storage folders:

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

panthera leo/Camera Trap/2017/02/PvL_seq_d8579c60-9f99-4779-8792-2395ab0e3afa/20170218-PICT0729.JPG

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

[https://storage.googleapis.com/public-datasets-lila/desert-lion-camera-traps/annotated-imgs/panthera%20leo/Camera Trap/2017/02/PvL_seq_d8579c60-9f99-4779-8792-2395ab0e3afa/20170218-PICT0729.JPG](https://storage.googleapis.com/public-datasets-lila/desert-lion-camera-traps/annotated-imgs/panthera%20leo/Camera%20Trap/2017/02/PvL_seq_d8579c60-9f99-4779-8792-2395ab0e3afa/20170218-PICT0729.JPG)

http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/desert-lion-camera-traps/annotated-imgs/panthera%20leo/Camera%20Trap/2017/02/PvL_seq_d8579c60-9f99-4779-8792-2395ab0e3afa/20170218-PICT0729.JPG

https://lilawildlife.blob.core.windows.net/lila-wildlife/desert-lion-camera-traps/annotated-imgs/panthera%20leo/Camera%20Trap/2017/02/PvL_seq_d8579c60-9f99-4779-8792-2395ab0e3afa/20170218-PICT0729.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/2024/01/desert-lion-camera-traps-thumb.jpg)


ENA24-detection

ENA24-detection is an image-based object detection dataset comprising 8,789 JPEG images with accompanying JSON annotation files. No further description is available — the dataset has no README.

# ENA24-detection

### ENA24-detection

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/ena24`

#### Overview

This data set contains approximately 10,000 camera trap images representing 23 classes from Eastern North America, with bounding boxes on each image. The most common classes are "American Crow", "American Black Bear", and "Dog".

#### Citation, license, and contact information

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

The data set is described in more detail in the associated manuscript:

Yousif H, Kays R, Zhihai H. Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild. IEEE Transactions on Circuits and Systems for Video Technology, 2019. ([bibtex](http://lila.science/wp-content/uploads/2019/12/hayder2019_bibtex.txt))

Please cite this manuscript if you use this data set.

For questions about this data set, contact [Hayder Yousif](mailto:hyypp5@mail.missouri.edu).

#### 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. Images containing humans were removed from the data set, but the metadata still contains information about those images.

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/ena24/images (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/ena24/images (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/ena24/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:

[Images](https://storage.googleapis.com/public-datasets-lila/ena24/ena24.zip) (GCP, 3.6GB)

[Metadata](https://storage.googleapis.com/public-datasets-lila/ena24/ena24.json) (GCP, 3.6MB)

[Images](https://lilawildlife.blob.core.windows.net/lila-wildlife/ena24/ena24.zip) (Azure, 3.6GB)

[Metadata](https://lilawildlife.blob.core.windows.net/lila-wildlife/ena24/ena24.json) (Azure 3.6MB)

[Images](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/ena24/ena24.zip) (AWS, 3.6GB)

[Metadata](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/ena24/ena24.json) (AWS, 3.6MB)

An "unofficial" version of the metadata file that only includes annotations for images that are present in the public data set (i.e., from which metadata for images of humans has been removed) is available at:

[Metadata (non-human images only)](https://storage.googleapis.com/public-datasets-lila/ena24/ena24_public.json) (2.9MB)

It is not guaranteed that this version will be maintained across changes to the underlying data set, but we're like 99.9999999% sure that if you're reading this, it's still accurate.

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/ena24_candidate05.jpg)


GeoLifeCLEF 2020

GeoLifeCLEF 2020 is a benchmark dataset for plant and organism species identification from geographic location context, created for the GeoLifeCLEF 2020 challenge. It pairs geo-located species occurrence records with multi-spectral satellite image patches (RGB, near-infrared, altitude) and environmental rasters covering France and the United States. The dataset supports research in species distribution modeling and location-based species prediction.

biodiversity species-distribution remote-sensing ecology species-occurrence satellite-imagery land-use

# GeoLifeCLEF 2020

### GeoLifeCLEF 2020

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/geolifeclef-2020`

#### Overview

Understanding the geographic distribution of species is a key concern in conservation. By pairing species occurrences with environmental features, researchers can model the relationship between an environment and the species which may be found there. To facilitate research in this area, we present the GeoLifeCLEF 2020 (GLC2020) dataset, which consists of 1.9 million geo-located species observations paired with high-resolution remote sensing imagery, land cover data, and altitude, in addition to traditional low-resolution climate and soil variables. The observations in this dataset cover 31,435 plant and animal species from the United States and France. The dataset was originally prepared for the [GeoLifeCLEF 2020 competition](https://www.aicrowd.com/challenges/lifeclef-2020-geo). Full details can be found in the [dataset paper](https://arxiv.org/abs/2004.04192) (cited below).

#### Citation

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

Cole E, Deneu B, Lorieul T, Servajean M, Botella C, Morris D, Jojic N, Bonnet P, Joly A. The GeoLifeCLEF 2020 Dataset. arXiv preprint arXiv:2004.04192. 2020 Apr 8. ([bibtex](http://lila.science/wp-content/uploads/2019/05/robinson2019large_bibtex.txt))

#### Data format

The dataset consists of three components: **high-resolution patches**, **annotations** for those patches, and **low-resolution covariate rasters**.

Each **high-resolution patch** is stored as a pair of .npy files: XXX.npy containing RGB-IR and land cover (256x256x5 uint8 array) and XXX_alti.npy containing altitude (256x256x1 uint16 array).

**Annotations** for the high-resolution patches are provided as JSON files that adhere to a modified version of the [COCO dataset annotation format](https://cocodataset.org/). The format is as follows:

```

	"images": [image],
	"categories": [category],
	"annotations": [annotation]

image

	"id": int,
	"width": int,
	"height": int,
	"file_name": str,
	"file_name_alti": str,
	"lon": float,
	"lat": float,
	"country": str

category

	"id": int,
	"gbif_id": int,
	"gbif_name": str

annotation

	"id": int,
	"image_id": int,
	"category_id" int

```

The **low-resolution covariate rasters** are provided as pairs of .tif files (one for the US and one for France) for each variable. On the [competition GitHub page](https://github.com/maximiliense/GLC) we provide code to extract values for 27 environmental characteristics at any location in the US or France.

#### Download links

**High-resolution-patches (GCP links)**

High-resolution patches are available at:

https://storage.googleapis.com/public-datasets-lila/geolifeclef-2020/patches_[region]_[index].tar.gz (11GB each)

...where *region* is "us" or "fr" for the US and France, respectively, and "index" is 01, 02, ... 20. For example:

https://storage.googleapis.com/public-datasets-lila/geolifeclef-2020/patches_fr_01.tar.gz

**High-resolution-patches (AWS links)**

High-resolution patches are available at:

http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/geolifeclef-2020/patches_[region]_[index].tar.gz (11GB each)

...where *region* is "us" or "fr" for the US and France, respectively, and "index" is 01, 02, ... 20. For example:

http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/geolifeclef-2020/patches_fr_01.tar.gz

**High-resolution-patches (Azure links)**

High-resolution patches are available at:

https://lilawildlife.blob.core.windows.net/lila-wildlife/geolifeclef-2020/patches_[region]_[index].tar.gz (11GB each)

...where *region* is "us" or "fr" for the US and France, respectively, and "index" is 01, 02, ... 20. For example:

https://lilawildlife.blob.core.windows.net/lila-wildlife/geolifeclef-2020/patches_fr_01.tar.gz

**High-resolution-patches (folder structure)**

For each country, each of the 20 .tar.gz files contains five directories from the set  00/, ..., 99/ . For example, patches_fr_01.tar.gz contains 00/, ..., 04/ and patches_fr_02.tar.gz contains 05/, ..., 09/, and so on. Then each of those directories contains subdirectories 00/, ..., 99/. These indices do not have a semantic interpretation, they are just to help the operating system deal with the number of files. Each pair of patch files is named XXXXABCD.npy and XXXXABCD_alti.npy, and lives at CD/AB/ where CD and AB are each among 00/, ..., 99/.

The contents of each file should be extracted to the same directory, such that, for example, the 00/ folder contains the contents of both patches_us_01/ and patches_fr_01/.

**Annotations**

* Training (450MB) ([GCP link](https://storage.googleapis.com/public-datasets-lila/geolifeclef-2020/annotations_train.json)) ([Azure link](https://lilawildlife.blob.core.windows.net/lila-wildlife/geolifeclef-2020/annotations_train.json)) ([AWS link](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/geolifeclef-2020/annotations_train.json))
* Validation (11MB) ([GCP link](https://storage.googleapis.com/public-datasets-lila/geolifeclef-2020/annotations_val.json)) ([Azure link](https://lilawildlife.blob.core.windows.net/lila-wildlife/geolifeclef-2020/annotations_val.json)) ([AWS link](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/geolifeclef-2020/annotations_val.json))
* Testing (13MB) ([GCP link](https://storage.googleapis.com/public-datasets-lila/geolifeclef-2020/annotations_test.json)) ([Azure link](https://lilawildlife.blob.core.windows.net/lila-wildlife/geolifeclef-2020/annotations_test.json)) ([AWS link](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/geolifeclef-2020/annotations_test.json))

**Low-resolution covariate rasters**

Covariate rasters (2.4GB) ([GCP link](https://storage.googleapis.com/public-datasets-lila/geolifeclef-2020/rasters.tar.gz)) ([Azure link](https://lilawildlife.blob.core.windows.net/lila-wildlife/geolifeclef-2020/rasters.tar.gz)) ([AWS link](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/geolifeclef-2020/rasters.tar.gz))

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

#### Contact information

For questions about this dataset, contact [geolifeclef@inria.fr](mailto:geolifeclef@inria.fr).

#### License information

**RGB-IR imagery**

*US*

Source: NAIP/USGS

License: public domain

*FR*

Source: IGN

License: CC BY 4.0 (By Permission of IGN)

**Land cover**

*US*

Source: NLCD/USGS

License: public domain

*FR*

Source: CESBIO

License: ODC-BY 1.0

**Species occurrences**

*US*

Source: iNaturalist

License: CC BY-NC 4.0

*FR*

Source #1: Pl@ntNet and iNaturalist

License #1: CC BY 4.0

Source #2: iNaturalist

License #2: CC BY-NC 4.0

**Altitude**

Source: SRTM/NASA

License: public domain

**Soil rasters**

Source: SoilGrids

License: CC BY 4.0

**Bioclimatic rasters**

Source: WorldClim

License: CC BY-SA 4.0

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


HKH Glacier Mapping

Glacier extent mapping dataset for the Hindu Kush Himalaya (HKH) region, covering one of the world's largest concentrations of glaciers outside the polar regions. The single compressed archive likely contains glacier boundary or inventory data derived from satellite or aerial observation.

glacier hindu-kush-himalaya cryosphere mountain-glaciers glacial-extent remote-sensing

# HKH Glacier Mapping

### HKH Glacier Mapping

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/icimod-glacier-mapping`

#### Overview

The Hindu Kush Himalayas (HKH) glacier mapping dataset includes imagery of the Hindu Kush Himalayas (HKH) region, along with polygons indicating the locations of glaciers. This dataset is intended to facilitate the training of models that can identify glaciers in remotely-sensed imagery.

The HKH is also known as the world’s “Third Pole”, as it consists of one of the largest concentrations of snow and ice besides the two poles. It constitutes more than four million square kilometers of hills and mountains in the eight countries of Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan. Glaciers within this region have been identified and classified by experts at the International Centre for Integrated Mountain Development ([ICIMOD](https://www.icimod.org/)).

This dataset couples those annotated glacier locations with multispectral imagery from Landsat 7 [1] and digital elevation and slope data from SRTM [2]. Imagery are provided as thirty-five Landsat tiles and 14,190 extracted numpy patches. Labels are available as raw vector data in shapefile format and as multichannel numpy masks. Both the labels and the masks are cropped according to the borders of the HKH region.

Python code for training and testing machine learning models using PyTorch, as well as the source for a glacier mapping web tool, can be found in the accompanying GitHub repository:

https://github.com/krisrs1128/glacier_mapping

#### Dataset organization

##### Tiles

At the highest level, this dataset is organized by *tiles*. A *tile* is a spatial area measuring roughly 6km x 7.5km (with definitions that roughly match up with USGS quarter quadrangles). Each tile comes with one corresponding GeoTIFF file. The entire glacier mapping dataset contains 35 tiles from Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan.

Each GeoTIFF tile consists of 15 channels:

1. LE7 B1 (blue)
2. LE7 B2 (green)
3. LE7 B3 (red)
4. LE7 B4 (near infrared)
5. LE7 B5 (shortwave infrared 1)
6. LE7 B6_VCID_1 (low-gain thermal infrared)
7. LE7 B6_VCID_2 (high-gain thermal infrared)
8. LE7 B7 (shortwave infrared 2)
9. LE7 B8 (panchromatic)
10. LE7 BQA (quality bitmask)
11. NDVI (vegetation index)
12. NDSI (snow index)
13. NDWI (water index)
14. SRTM 90 elevation
15. SRTM 90 slope

These data were acquired from Google Earth Engine’s [LE7](https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_RT) and [SRTM](https://developers.google.com/earth-engine/datasets/catalog/CGIAR_SRTM90_V4) collections using this [script](https://code.earthengine.google.com/04430576383081722f303ad774fba209?accept_repo=users%2Fbibekaryal7%2Ffall2020).

All channels are aligned at 30m spatial resolution. Elevation and slope channels were upsampled from 90m to 30m resolution.

##### Glacier annotations

Digital polygon data indicating the status of glaciers in the HKH region from 2002 to 2008 were provided by [ICIMOD](https://www.icimod.org/) [3].

##### Patches

We also provide 14190 numpy patches. The numpy patches are all of size 512 x 512 x 15 and corresponding 512 x 512 x 2 pixel-wise mask labels; the two channels in the pixel-wise masks correspond to clean-iced and debris-covered glaciers. Patches’ geolocation information, time stamps, source Landsat IDs, and glacier density are available in a geojson metadata file. We show an example of the metadata below:

  "type": "Feature", "properties":   "img_source": "\/datadrive\/glaciers\/unique_tiles\/LE07_149037_20041024.tif", "mask_source": "\/datadrive\/glaciers\/processed_exper\/masks\/mask_00.npy", "img_slice": "\/datadrive\/glaciers\/processed_exper\/slices\/slice_0_img_003.npy", "mask_slice": "\/datadrive\/glaciers\/processed_exper\/slices\/slice_0_mask_003.npy", "mask_mean_0": 0.0, "mask_mean_1": 0.0, "mask_mean_2": 0.0, "img_mean": 189.76698303222656  , "geometry":   "type": "Polygon", "coordinates": [ [ [ 386932.71929824561812, 3564585.0 ], [ 386932.71929824561812, 3579767.12783851986751 ], [ 371750.78947368421359, 3579767.12783851986751 ], [ 371750.78947368421359, 3564585.0 ], [ 386932.71929824561812, 3564585.0 ] ] ]
#### Download links

[All data (patches, polygons, images)](https://storage.googleapis.com/public-datasets-lila/icimod-glacier-mapping/hkh_patches.tar.gz) (GCP link) (29.4GB)

[All data (patches, polygons, images)](https://lilawildlife.blob.core.windows.net/lila-wildlife/icimod-glacier-mapping/hkh_patches.tar.gz) (Azure link) (29.4GB)

[All data (patches, polygons, images)](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/icimod-glacier-mapping/hkh_patches.tar.gz) (AWS link) (29.4GB)

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

#### Citation and contact information

For questions about this dataset, contact [anthony.ortiz@microsoft.com](mailto:anthony.ortiz@microsoft.com) or [ksankaran@wisc.edu](mailto: ksankaran@wisc.edu).

If you use this dataset, please cite:

Baraka S, Akera B, Aryal B, Sherpa T, Shresta F, Ortiz A, Sankaran K, Lavista Ferres J, Matin M, Bengio Y. 2020. [Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya](https://arxiv.org/abs/2012.05013). NeurIPS 2020 Climate Change AI Workshop (2020).

#### License

*Annotations*

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

*Images*

Landsat and SRTM data have been released into the public domain. License information about SRTM and Landsat is available [here](https://lpdaac.usgs.gov/documents/13/SRTM_Quick_Guide.pdf) and [here](https://www.usgs.gov/faqs/are-there-any-restrictions-use-or-redistribution-landsat-data?qt-news_science_products=0#qt-news_science_products), respectively.

#### References

1. United States Geological Survey. Landsat 7. [Online](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)
2. SRTM 90m DEM Digital Elevation Database. [Online](http://srtm.csi.cgiar.org/)
3. Bajracharya, S. R., & Shrestha, B. R. (2011). The status of glaciers in the Hindu Kush-Himalayan region. International Centre for Integrated Mountain Development (ICIMOD). [Online](http://rds.icimod.org/Home/DataDetail?metadataId=9359&searchlist=True)

![](http://lila.science/wp-content/uploads/2020/11/glacier_thumbnail.png)


Idaho Camera Traps

A large collection of camera trap photographs from Idaho, comprising over 1.5 million JPEG images. Camera trap datasets of this scale are typically used for wildlife monitoring, species detection, and ecological research.

camera-traps wildlife wildlife-monitoring imagery idaho

# Idaho Camera Traps

### Idaho Camera Traps

#### S3 base

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

#### Overview

This data set contains approximately 1.5 million camera trap images from Idaho. Labels are provided for 62 categories, most of which are animal classes ("deer", "elk", and "cattle" are the most common animal classes), but labels also include some state indicators (e.g. "snow on lens", "foggy lens").
Approximately 70.5% of images are labeled as empty. Annotations were assigned to image *sequences*, rather than individual images, so annotations are meaningful only at the *sequence* level.

The metadata contains references to images containing humans, but these have been removed from the dataset (along with images containing vehicles and domestic dogs).

#### Citation, license, and contact information

Images were provided by the Idaho Department of Fish and Game. No representations or warranties are made regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose. Some information shared under this agreement may not have undergone quality assurance procedures and should be considered provisional. Images may not be sold in any format, but may be used for scientific publications. Please acknowledge the Idaho Department of Fish and Game when using images for publication or scientific communication.
#### 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/).

#### Downloading the data

Metadata is available [here](https://storage.googleapis.com/public-datasets-lila/idaho-camera-traps/idaho-camera-traps.json.zip) (25MB).

Images are available in the following cloud storage folders:

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

    loc_0000/loc_0000_im_000003.jpg

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

https://storage.googleapis.com/public-datasets-lila/idaho-camera-traps/public/loc_0000/loc_0000_im_000003.jpg

http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/idaho-camera-traps/public/loc_0000/loc_0000_im_000003.jpg

https://lilawildlife.blob.core.windows.net/lila-wildlife/idaho-camera-traps/public/loc_0000/loc_0000_im_000003.jpg

Finally, though we don't recommend downloading this way, images are also available in five giant zipfiles:

Images (part 0) ([GCP](https://storage.googleapis.com/public-datasets-lila/idaho-camera-traps/idaho-camera-traps-images.part_0.zip)) ([AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/idaho-camera-traps/idaho-camera-traps-images.part_0.zip)) ([Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/idaho-camera-traps/idaho-camera-traps-images.part_0.zip)) (300GB)

Images (part 1) ([GCP](https://storage.googleapis.com/public-datasets-lila/idaho-camera-traps/idaho-camera-traps-images.part_1.zip)) ([AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/idaho-camera-traps/idaho-camera-traps-images.part_1.zip)) ([Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/idaho-camera-traps/idaho-camera-traps-images.part_1.zip)) (300GB)

Images (part 2) ([GCP](https://storage.googleapis.com/public-datasets-lila/idaho-camera-traps/idaho-camera-traps-images.part_2.zip)) ([AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/idaho-camera-traps/idaho-camera-traps-images.part_2.zip)) ([Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/idaho-camera-traps/idaho-camera-traps-images.part_2.zip)) (300GB)

Images (part 3) ([GCP](https://storage.googleapis.com/public-datasets-lila/idaho-camera-traps/idaho-camera-traps-images.part_3.zip)) ([AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/idaho-camera-traps/idaho-camera-traps-images.part_3.zip)) ([Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/idaho-camera-traps/idaho-camera-traps-images.part_3.zip)) (300GB)

Images (part 4) ([GCP](https://storage.googleapis.com/public-datasets-lila/idaho-camera-traps/idaho-camera-traps-images.part_4.zip)) ([AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/idaho-camera-traps/idaho-camera-traps-images.part_4.zip)) ([Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/idaho-camera-traps/idaho-camera-traps-images.part_4.zip)) (250GB)

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/2021/07/idaho-camera-traps.jpg)


Island Conservation Camera Traps

Camera trap imagery collected by Island Conservation, comprising over 122,000 JPEG photographs alongside tabular and archive files. The dataset supports wildlife monitoring and invasive species management efforts on islands. No further description is available as the dataset has no README.

camera-traps wildlife-monitoring island-conservation invasive-species biodiversity imagery

# Island Conservation Camera Traps

### Island Conservation Camera Traps

#### S3 base

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

#### Overview

This data set contains approximately 123,000 camera trap images from 123 camera locations from 7 islands in 6 countries. Data were provided by Island Conservation during projects conducted to prevent the extinction of threatened species on islands.

The most common classes are rabbit, rat, petrel, iguana, cat, goat, and pig, with both rat and cat represented between multiple island sites representing significantly different ecosystems (tropical forest, dry forest, and temperate forests). Additionally, this data set represents data from locations and ecosystems that, to our knowledge, are not well represented in publicly available datasets including >1,000 images each of iguanas, petrels, and shearwaters. A complete list of classes and associated image counts is available [here](https://storage.googleapis.com/public-datasets-lila/islandconservationcameratraps/island_conservation_species_list.csv). Approximately 60% of the images are empty. We have also included approximately 65,000 bounding box annotations for about 50,000 images.

In general cameras were dispersed across each project site to detect the presence of invasive vertebrate species that threaten native island species. Cameras were set to capture bursts of photos for each motion detection event (between three and eight photos) with a set delay between events (10 to 30 seconds) to minimize the number of photos. Images containing humans are referred to in metadata, but are not included in the data files.

#### Citation, license, and contact information

For questions about this data set, contact [David Will](mailto:david.will@islandconservation.org) at Island Conservation.

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) format. Timestamps were not present in the original data package, and have been inferred from image pixels using an OCR approach. Let us know if you see any incorrect timestamps.

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://storage.googleapis.com/public-datasets-lila/islandconservationcameratraps/island_conservation_camera_traps_1.02.zip) (5MB).

Images are available as a single zipfile:

[Download from GCP](https://storage.googleapis.com/public-datasets-lila/islandconservationcameratraps/island_conservation_camera_traps.zip) (76GB)

[Download from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/islandconservationcameratraps/island_conservation_camera_traps.zip) (76GB)

[Download from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/islandconservationcameratraps/island_conservation_camera_traps.zip) (76GB)

Images are also available (unzipped) in the following cloud storage folders:

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

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/2020/08/island_conservation_thumb.png)


Izembek Lagoon Waterfowl

A collection of 9,267 images (JPG) associated with waterfowl at Izembek Lagoon, a nationally significant wildlife refuge on the Alaska Peninsula known for its eelgrass beds and migratory bird populations. Two ZIP archives are also included, likely containing ancillary data or metadata.

waterfowl wildlife alaska wetlands imagery

# Izembek Lagoon Waterfowl

### Izembek Lagoon Waterfowl

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/izembek-lagoon-birds`

#### Overview

This dataset contains 9,267 high-resolution (8688x5792) aerial images from Izembek Lagoon in Alaska, collected to survey waterfowl. The dataset includes 521,270 bounding boxes on waterfowl, with each box identified as one of:

* Brant (424,790 boxes)
* Canada goose (47,561 boxes)
* Gull (5,631 boxes)
* Emperor goose (2,013 boxes)
* Other (5,631 boxes)

All were originally annotated as points, and were converted to boxes as a convenience for detector training. Consequently, the boxes are all identical in size, centered on the original annotation points and sized to the typical size of birds in these images. Approximately half of the images (4,281) are annotated as empty.

This dataset is a subset of the [Aerial Photo Imagery from Fall Waterfowl Surveys](https://alaska.usgs.gov/products/data.php?dataid=484) dataset; all non-empty images from the original dataset are included, but only a small fraction of the empty images are included. The original dataset is the dataset of record; the present dataset is provided as a convenience for training AI models, as (a) some effort was required to convert the annotations to a standard format ([import code](https://github.com/agentmorris/usgs-geese/blob/main/usgs-geese-data-import.py)), and (b) the original dataset is quite large (1.82TB). Because the proportion of empty images has been dramatically reduced in the present dataset, models trained on the subset should be evaluated against the original dataset before making claims about precision and recall as they would apply in a real-world setting.

#### Citation

If you use this dataset, please cite the original dataset:

Weiser EL, Flint PL, Marks DK, Shults BS, Wilson HM, Thompson SJ, Fischer JB, 2022, Aerial photo imagery from fall waterfowl surveys, Izembek Lagoon, Alaska, 2017-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9UHP1LE.

#### 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.

#### Downloading the data

Metadata is available [here](https://storage.googleapis.com/public-datasets-lila/izembek-lagoon-birds/izembek-lagoon-birds-metadata.zip).

Images are available as a single zipfile:

[Download from GCP](https://storage.googleapis.com/public-datasets-lila/izembek-lagoon-birds/izembek-lagoon-birds.zip) (124GB)

[Download from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/izembek-lagoon-birds/izembek-lagoon-birds.zip) (124GB)

[Download from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/izembek-lagoon-birds/izembek-lagoon-birds.zip) (124GB)

Images are also available (unzipped) in the following cloud storage folders:

* gs://public-datasets-lila/izembek-lagoon-birds/images (GCP)
* s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/izembek-lagoon-birds/images (AWS)
* https://lilawildlife.blob.core.windows.net/lila-wildlife/izembek-lagoon-birds/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 images via http, you can. For example, one image (with lots of birds) appears in the metadata as:

2017_Replicate_2017-09-30_Cam2_CAM24430.JPG

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

https://storage.googleapis.com/public-datasets-lila/izembek-lagoon-birds/images/2017_Replicate_2017-09-30_Cam2_CAM24430.JPG

http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/izembek-lagoon-birds/images/2017_Replicate_2017-09-30_Cam2_CAM24430.JPG

https://lilawildlife.blob.core.windows.net/lila-wildlife/izembek-lagoon-birds/images/2017_Replicate_2017-09-30_Cam2_CAM24430.JPG

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

![](http://lila.science/wp-content/uploads/2024/02/izembek_sample_image-700.jpg)


Forest Damages - Larch Casebearer

Geospatial dataset recording forest damage attributed to the Larch Casebearer (Coleophora laricella), a moth whose larvae defoliate larch trees. No README is present; content details are inferred from the dataset title alone.

forest-damage larch invasive-species forestry pest-monitoring

# Forest Damages - Larch Casebearer

### Forest Damages - Larch Casebearer

#### S3 base

`s3://us-west-2.opendata.source.coop/agentmorris/lila-wildlife/larch-casebearer`

#### Overview

The larch casebearer, *Coleophora laricella*, is a moth that mainly attacks larch trees and has caused significant damage in larch stands in Västergötland, Sweden.

The Swedish Forest Agency, supported by Microsoft’s AI for Earth program, started a project that utilizes artificial intelligence for identifying, inventorying, mapping, and monitoring forest areas affected by the larch casebearer. The primary intention of the project is to help forest caretakers to quickly identify threats and react to prevent further forest damage.

This dataset is an outcome of the project, and it contains 1,543 images taken from two drone flying occasions over five affected areas in Västergötland, Sweden. The data set is structured in 10 batches, numbered 1 to 10.

All batches contain bounding box annotations around trees, categorized as *Larch* and *Other*. These annotations can be used to train AI models for tree identification. In 1,543 images, there are 101,878 annotated trees.

Batches 1-5 also contain annotations describing tree damage in four categories: *Healthy (H)*, *Light Damage (LD)*, *High Damage (HD)*, and *Other*. These annotations can be used to train models for damage classification. In 840 images there are 44,522 larch trees annotated with damage level.

An overview of the project is available [here](https://www.youtube.com/watch?v=WcVzys6ECys) (video).

*Figure 1: Data set statistics*

![](http://lila.science/wp-content/uploads/2021/03/casebearer_stats.png)

#### Data format

There are ten folders in the zipfile, each corresponding to a drone survey, named according to:

[area_name]_[capture_date]

* area_name is one of "Bebehojd", "Ekbacka", "Jallasvag", "Kampe", "Nordkap"
* capture_date is the capture date, formatted as yyyymmdd

Within each survey folder are two folders: "Annotations" and "Images".

Image files are named according to:

Images/B[batch_number]_[image_number].JPG

* batch_number is a two-digit batch number, from 01 to 10. Each survey folder contains only a single batch number.
* image_number is a five-digit image number

Annotation files are named according to:

Annotations/B[batch_number]_[image_number].XML

* batch_number is a two-digit batch number, from 01 to 10, which will match the corresponding "Images" folder.
* image_number is a five-digit image number

Annotations are in the [Pascal VOC XML format](https://roboflow.com/formats/pascal-voc-xml#w-tabs-0-data-w-pane-3) for objection detection. A typical individual tree object would be annotated as:

```

Larch
LD
Unspecified
0
0

164
107
347
380

```
#### Citation

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

Swedish Forest Agency (2021): Forest Damages - Larch Casebearer 1.0. [National Forest Data Lab](https://skogsdatalabbet.se/). Dataset.

#### Contact information

For questions about this data set, contact Halil Radogoshi ([halil.radogoshi@skogsstyrelsen.se](mailto:halil.radogoshi@skogsstyrelsen.se)) at the Swedish Forest Agency.

#### License

*Labels*

The organizations responsible for generating and funding this dataset make no representations of any kind including, but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data. Although every effort has been made to ensure the accuracy of information, errors may be reflected in data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors, collection methodology, currency of data, and other conditions. Credit should always be given to the data source when this data is transferred, altered, or used for analysis.

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

*Images*

For the drone images, the Swedish Forest Agency has secured [permit for dissemination of geographical data](https://www.lantmateriet.se/en/about-lantmateriet/Rattsinformation/permit-for-dissemination-of-geographical-data/) from Lantmäteriet, an authority belonging to Ministry of Finance in Sweden.

#### Downloading the data

This dataset is provided as a single zipfile:

[Download from GCP](https://storage.googleapis.com/public-datasets-lila/larch-casebearer/Data_Set_Larch_Casebearer.zip) (3.3GB)

[Download from AWS](http://us-west-2.opendata.source.coop.s3.amazonaws.com/agentmorris/lila-wildlife/larch-casebearer/Data_Set_Larch_Casebearer.zip) (3.3GB)

[Download from Azure](https://lilawildlife.blob.core.windows.net/lila-wildlife/larch-casebearer/Data_Set_Larch_Casebearer.zip) (3.3GB)

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

![](http://lila.science/wp-content/uploads/2021/03/sfa_thumb_800.jpg)