Fields of The World: Field Boundaries for Germany

Crop type classifications and field boundaries for agricultural areas in Brandenburg, Germany, derived from the European Space Agency's Fusion Competition dataset. Includes polygon geometries with crop type labels and associated metadata.

agriculture crop-classification field-boundaries crop-type

## Description

This dataset contains ground reference crop type labels in an area located in Brandenburg, Germany.

- **Source Data Curator:** [European Space Agency](https://beta.source.coop/esa/fusion-competition/)
- **Source Data Provider:** [European Space Agency](https://beta.source.coop/esa/fusion-competition/)
- **Converted by:** Kerner Lab (ASU)
- **License:** DL-DE/BY-2-0
- **Projection:** EPSG:4326
- **Train Dataset Validity:** 2018
- **Test Dataset Validity:** 2019

---

## Attributes

| Property                  | Data Type |
| ------------------------- | --------- |
| id                        | string    |
| determination_datetime    | datetime  |
| geometry                  | polygon   |
| area                      | float     |
| perimeter                 | float     |
| crop_id                   | string    |
| crop_name                 | string    |

Source Cooperative product page

README

Dataset files (1 × .parquet)

German Data License 2.0 with attribution (Datenlizenz Deutschland DL-DE/BY-2-0)


Fields of The World: Field Boundaries for India

Manually delineated crop field boundaries for 10,000 fields across India from high-resolution satellite imagery. Created as part of research on automated field delineation in smallholder farming systems.

agriculture field-boundaries crop-classification remote-sensing smallholder-farming

## Description

This dataset was released by Sherrie Wang, Francois Waldner and David B. Lobell as a part of an academic paper "Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision".

`Sherrie Wang, Francois Waldner, & David B. Lobell. (2022). 10,000 Crop Field Boundaries across India [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7315090`

Ten thousand crop fields in India were delineated manually through inspection of high-resolution satellite imagery (Airbus SPOT).

- **Source Data Curator:** [Sherrie Wang, Francois Waldner, David B. Lobell](https://zenodo.org/records/7315090)
- **Source Data Provider:** [Sherrie Wang, Francois Waldner, David B. Lobell](https://zenodo.org/records/7315090)
- **Converted by:** Kerner Lab (ASU)
- **License:** CC-BY-4.0
- **Projection:** EPSG:4326
- **Dataset Validity:** 2016

---

## Attributes

| Property                  | Data Type |
| ------------------------- | --------- |
| id                        | string    |
| determination_datetime    | datetime  |
| area                      | float     |
| geometry                  | polygon   |

Source Cooperative product page

README

Dataset files (1 × .parquet)


Fields of The World: Field Boundaries for Kenya

Georeferenced field boundary polygons for 874 agricultural fields in Kenya, with crop type labels and determination dates. Created as part of the Bird's-Eye project to improve predictive analytics in smallholder agriculture through AI/ML and satellite imagery.

agriculture field-boundaries crop-classification smallholder-farming remote-sensing

## Description

This is a dataset released as a part of the Bird’s-Eye project, a collaboration between Pula and Tetra Tech under the Enabling Crop Analytics at Scale Project. This work, funded by the Bill & Melinda Gates Foundation, is meant to foster the collection of Ground truth georeferenced Agriculture labelled datasets that would improve predictive analytics in smallholder agriculture, through the use of Artificial intelligence and Machine Learning (AI/ML) and satellite imagery.

- **Source Data Curator:** [ECAAS Project](https://ecass-project-documentation.readthedocs.io/en/latest/modules/data_access.html#data-access)
- **Source Data Provider:** [PULA](https://www.pula-advisors.com/)
- **Converted by:** Kerner Lab (ASU)
- **License:** GPL-2.0-or-later
- **Projection:** EPSG:4326
- **Dataset Validity:** 2022

---

## Attributes

| Property                  | Data Type |
| ------------------------- | --------- |
| id                        | string    |
| determination_datetime    | datetime  |
| geometry                  | polygon   |
| crop_name                 | string    |

Source Cooperative product page

README

Dataset files (1 × .parquet)


Fields of The World: Field Boundaries for Latvia

Field boundaries for Latvia from the Rural Support Service of the Latvian Land Registry, valid for 2021. This dataset includes field geometries, crop information, and agricultural parcel attributes as a subset of the Fields of The World collection.

agriculture field-boundaries crop-classification land-parcels arable-land land-use

## Description

Rural Support Services of Latvian Land Registry maintains and updates geospatial information on a daily basis. We adopted the latest dataset for field boundaries from the official source for the year 2021.

- **Source Data Curator:** [Euro Crops](https://github.com/maja601/EuroCrops/wiki/Latvia)
- **Source Data Provider:** [Rural Support Service](https://www.lad.gov.lv/lv/lauku-registra-dati)
- **Converted by:** Kerner Lab (ASU)
- **License:** CC-BY-NC-4.0
- **Projection:** EPSG:4326
- **Dataset Validity:** 2021

---

## Attributes

| Property                  | Data Type |
| ------------------------- | --------- |
| id                        | string    |
| determination_datetime    | datetime  |
| geometry                  | polygon   |
| area                      | float     |
| perimeter                 | float     |
| crop_id                   | int64     |
| crop_name                 | string    |

Source Cooperative product page

README

Dataset files (1 × .parquet)


Fields of The World: Field Boundaries for Lithuania

Field boundaries and crop area data for Lithuania covering 61,424 agricultural parcels in 2021, sourced from the National Paying Agency under the Ministry of Agriculture. The dataset includes field geometry with crop type and area attributes in EPSG:4326 projection.

agriculture field-boundaries crop-areas cultivated-crops lithuania eurocrops crop-classification

## Description

This is a subset of collection of data on agricultural land and crop areas, cultivated crops in the territory of the Republic of Lithuania.

The original curator of the dataset provided a download service is a set of personalized spatial data of agricultural land and crop areas, cultivated crops. The service provides object geometry with descriptive (attributive) data.

_The field boundaries are valid for the year 2021_

- **Source Data Curator:** [Eurocrops](https://github.com/maja601/EuroCrops/wiki/Lithuania)
- **Original Data Provider:** [The National Paying Agency under the Ministry of Agriculture](https://metadata.vlaanderen.be/srv/dut/catalog.search#/metadata/d869db4a-73f4-470e-b5ca-a0f7cd3ab585)
- **Converted by:** Kerner Lab (ASU)
- **License:** Non-commercial use only
- **Projection:** EPSG:4326

---

## Columns

| Property               | Data Type |
| ---------------------- | --------- |
| id                     | string    |
| determination_datetime | datetime  |
| area                   | float     |
| crop_type              | string    |
| crop_name_translated   | string    |
| HCAT_crop_name         | string    |

Source Cooperative product page

README

Dataset files (1 × .parquet)

Non-commercial use only. Original data from Lithuanian government agency.


Fields of The World: Field Boundaries for Luxembourg

Field boundaries from Luxembourg's FLIK land parcel identification system (LPIS), categorized by parcel type (agricultural, vineyard, ineligible, non-payable). Data available annually from 2016-2021, digitized since 2005 and continuously updated via aerial imagery.

agriculture field-boundaries land-parcels luxembourg land-use lpis aerial-imagery

## Description

This dataset is a subset of dataste form FLIK plot repository provided by Luxembourg's Land Parcel Identification System(LPIS).
LPIS is managed by The Administration of Technical Agricultural Services (ASTA). The data is made available to the public on The Luxembourg Data Platform based on the Creative Commons 1.0 Universal (CC0 1.0) Public Domain Dedication.The plot-based parcel reference system (FLIK) in Luxembourg was digitally recorded for the first time in 2005 based on aerial photos. Since then, the system has been continuously revised via the latest aerial images.

he FLIK parcels are categorized into eligible agricultural parcels, eligible vineyard parcels, ineligible parcels, and non-payable objects. The data has been uploaded to geoDB and is available from 2016 to 2021 annually with updates expected annually on original curators site.

_The field boundaries are valid for the year 2022_

- **Original Data Provider:** [Administration of technical services for agriculture](https://data.public.lu/en/datasets/referentiel-des-parcelles-flik/#resourcese)
- **Converted by:** Kerner Lab (ASU)
- **License:** CC0-1.0
- **Projection:** EPSG:4326

---

## Columns

| Property               | Data Type |
| ---------------------- | --------- |
| id                     | string    |
| determination_datetime | datetime  |
| area                   | float     |
| category               | string    |
| commune_code           | string    |
| section_code           | string    |
| perimeter              | float     |

Source Cooperative product page

README

Dataset files (1 × .parquet)


Fields of The World: Field Boundaries for Netherlands

Agricultural field boundaries and crop type classifications from the Netherlands' Basic Register of Plots (BRP), with annual crop registrations as of May 15. Field boundaries are valid for 2022.

agriculture crop-classification field-boundaries land-parcels cadastral

## Description

BRP - Crop plots consists of the location of agricultural plots linked to the crop grown. The file is a subset of selection of information from the Basic Register of Plots (BRP) of the Netherlands Enterprise Agency. The boundaries of the agricultural plots are based on the Agricultural Area of the Netherlands (AAN).

The user of the plot must annually register his crop plots and indicate which crop is grown on the plot in question. A dataset is generated for each year with reference date May 15.

_The field boundaries are valid for the year 2022_

- **Original Data Provider:** [PDOK Control](https://service.pdok.nl/rvo/brpgewaspercelen/atom/v1_0/basisregistratie_gewaspercelen_brp.xml)
- **Converted by:** Kerner Lab (ASU)
- **License:** CC0-1.0
- **Projection:** EPSG:4326

---

## Columns

| Property               | Data Type |
| ---------------------- | --------- |
| id                     | string    |
| determination_datetime | datetime  |
| crop_type              | string    |
| crop_category          | string    |
| status                 | string    |

Source Cooperative product page

README

Dataset files (1 × .parquet)


Fields of The World: Field Boundaries for Portugal

Agricultural holding plot boundaries with unique identifiers and land use classifications for Portugal, derived from the national Parcel Identification System (SIP/iSIP). Includes plot delimitation and classification data for agricultural land management and subsidy applications.

agriculture field-boundaries land-use-classification parcel-identification land-parcels

## Description

This dataset is maintained and updated by The SIP, which aims to identify the boundaries of agricultural holding plots, which are assigned a unique number, as well as the delimitation and classification of land use, allowing applications for Community Aid to be submitted and Control Actions to be carried out.

Actions to be performed in the iSIP application are only permitted to technicians accredited for this purpose.

- **Source Data Curator:** [Land Occupations – Parcel Identification System (iSIP) – IFAP](https://www.ifap.pt/isip/ows/)
- **Source Data Provider:** [SIP - INSTALLMENT IDENTIFICATION SYSTEM](https://www.ifap.pt/portal/sip-informacao-basica)
- **Converted by:** Kerner Lab (ASU)
- **License:** CC-BY-NC-4.0
- **Projection:** EPSG:4326
- **Dataset Validity:** 2021

---

## Attributes

| Property                  | Data Type |
| ------------------------- | --------- |
| id                        | string    |
| determination_datetime    | datetime  |
| area                      | float     |
| perimeter                 | float     |
| geometry                  | polygon   |

IFAP (SIP - Parcel Identification System)

Source Cooperative product page

README

Dataset files (1 × .parquet)


Fields of The World: Field Boundaries for Rwanda

Field boundaries for smallholder farms in eastern Rwanda, labeled from Planet satellite imagery for the 2021 growing season. Created through the Rwanda Field Boundary Detection Challenge by Nasa Harvest.

agriculture field-boundaries rwanda smallholder-farms remote-sensing crop-classification

## Description

This dataset contains field boundaries for smallholder farms in eastern Rwanda. The Nasa Harvest program funded a team of annotators from TaQadam to label Planet imagery for the 2021 growing season for the purpose of conducting the Rwanda Field boundary detection Challenge.

- **Source Data Curator:** [Nasa Harvest](https://beta.source.coop/repositories/nasa/rwanda-field-boundary-competition/description/)
- **Source Data Provider:** [Nasa Harvest](https://beta.source.coop/repositories/nasa/rwanda-field-boundary-competition/description/)
- **Converted by:** Kerner-Lab (ASU)
- **License:** CC-BY-4.0
- **Projection:** EPSG:4326
- **Dataset Validity:** 2021

---

## Attributes

| Property                  | Data Type |
| ------------------------- | --------- |
| id                        | string    |
| determination_datetime    | datetime  |
| geometry                  | polygon   |

Source Cooperative product page

README

Dataset files (1 × .parquet)


Fields of The World: Field Boundaries for Slovakia

Vector boundaries of agricultural land parcels from Slovakia's LPIS (Land Parcel Identification System) for 2021, including land use classification, acreage, and subsidy eligibility information.

agriculture field-boundaries land-use lpis land-parcels crop-classification

## Description
This dataset is a subset of the original data obtained from the LPIS scheme in Slovakia by the Agricultural Payment Agency. (LPIS parts of land blocks 2021) LPIS is an agricultural land identification system. It represents the vector boundaries of agricultural land and carries information about the unique code, acreage, culture/land use, etc., which is used as a reference for farmers' applications, for administrative and cross-checks, on-site checks and also checks using remote sensing methods.

_The field boundaries are valid for the year 2021_

- **Original Data Provider:** [Agricultural Payment Agency](https://data.slovensko.sk/datasety/7998b5ab-83f4-80f4-9ba3-7fa62dd79cd)
- **Converted by:** Kerner Lab (ASU)
- **License:** CC0-1.0
- **Projection:** EPSG:4326

---

## Columns

| Property                  | Data Type |
| ------------------------- | --------- |
| id                        | string    |
| determination_datetime    | datetime  |
| area                      | float     |
| municipality              | string    |
| eligibility_for_subsidies | string    |
| arable_crop_class_code    | string    |
| arable_crop_class         | string    |
| arable_crop_class_icode   | int8      |

Source Cooperative product page

README

Dataset files (1 × .parquet)


Fields of The World: Field Boundaries for Slovenia

Annual Slovenian agricultural field boundary polygons for crops grown within farmer-managed blocks (GERK), captured each year since 2007. This 2021 dataset represents field boundaries and crop types as reported by farmers on the campaign closing date (May 31).

agriculture field-boundaries crop-classification land-use agricultural-monitoring gerk

## Description

This dataset is a subset of data obtained from the layer of agricultural plants of ollective applicationsby Agency of the Republic of Slovenia for Agricultural Markets and Rural Development.
Every year, farmers must draw crop polygons within the boundaries of their block/GERK. This has been the practice in Slovenia since 2007. The published data for the current year represent the situation on the last date of the campaign (31 May yyyy). Subsequent changes by farmers are not included.

_The field boundaries are valid for the year 2021_

- **Source Data Curator:** [EuroCrop](https://github.com/maja601/EuroCrops/wiki/Slovenia)
- **Original Data Provider:** [Ministry of Agriculture, Forestry and Food - Agency of the Republic of Slovenia for Agricultural Markets and Rural Developmenty](https://metadata.vlaanderen.be/srv/dut/catalog.search#/metadata/d869db4a-73f4-470e-b5ca-a0f7cd3ab585)
- **Converted by:** Kerner Lab (ASU)
- **License:** CC-BY-4.0
- **Projection:** EPSG:4326

---

## Columns

| Property               | Data Type |
| ---------------------- | --------- |
| id                     | string    |
| determination_datetime | datetime  |
| area                   | float     |
| GERK_polygon_id        | uint32    |
| crop_type_code         | string    |
| crop_type              | string    |
| crop_name_translated   | string    |
| HCAT_crop_name         | string    |

Source Cooperative product page

README

Dataset files (1 × .parquet)


Fieldscapes: Field Boundaries for South Africa

Ground reference crop type labels for an agricultural area in Western Cape, South Africa, with field boundary polygons and crop classification data from the 2018 growing season.

agriculture field-boundaries crop-classification land-parcels

Dataset files (1 x .parquet)

Source Cooperative product page

## README.md
## Description

This dataset contains ground reference crop type labels in an area located in Western Cape, South Africa.

- **Source Data Curator:** [European Space Agency](https://beta.source.coop/esa/fusion-competition/)
- **Source Data Provider:** [European Space Agency](https://beta.source.coop/esa/fusion-competition/)
- **Converted by:** Kerner Lab (ASU)
- **License:** CC-BY-NC-SA-4.0
- **Projection:** EPSG:4326
- **Dataset Validity:** 2018

---

## Attributes

| Property                  | Data Type |
| ------------------------- | --------- |
| id                        | string    |
| determination_datetime    | datetime  |
| geometry                  | polygon   |
| area                      | float     |
| perimeter                 | float     |
| crop_id                   | string    |
| crop_name                 | string    |


Fields of The World: Field Boundaries for Spain

Field boundaries and crop classification map for Navarra, Spain derived from the SIGPAC agricultural land registry. Contains 258,465 polygon features representing individual agricultural fields with crop type identifications.

agriculture field-boundaries crop-classification land-use

## Description

SIGPAC Nevarra maintains land use map for the vegetation in the region of Spain, We have collected the Open Licensed dataset for our inclusion in Fieldscapes project.

- **Source Data Curator:** [Euro Crops](https://zenodo.org/records/6868143/files/ES_NA_2020.zip?download=1)
- **Source Data Provider:** [SIGPAC Navarra](https://sigpac.navarra.es/)
- **Converted by:** Kerner Lab (ASU)
- **License:** CC-BY-4.0
- **Projection:** EPSG:4326
- **Dataset Validity:** 2020

---

## Attributes

| Property                  | Data Type |
| ------------------------- | --------- |
| id                        | string    |
| determination_datetime    | datetime  |
| geometry                  | polygon   |
| area                      | float     |
| perimeter                 | float     |
| crop_id                   | string    |
| crop_name                 | string    |

Source Cooperative product page

README

Dataset files (1 × .parquet)


Fields of The World: Field Boundaries for Sweden

Swedish agricultural block inventory containing field boundaries and maximum eligible agricultural land according to EU definitions. Approximately 1.14 million blocks covering 3.2 million hectares, used by the Swedish Agency for Agriculture for administering farmer support. Field boundaries valid for 2021.

agriculture field-boundaries agricultural-blocks land-parcels sweden arable-land land-use pasture-land lpis

## Description

This dataset is a subset of the Swedish agricultural blocks original inventory. Agricultural block is a data set that contains information on maximum eligible agricultural land according to EU definitions. The agricultural blocks are used by the Swedish Agency for Agriculture to administer support to farmers, for example to check the area data in the farmers' applications and to inform the farmers about current data.

Sweden's Land Parcel Identification System (LPIS) data collection (also agricultural blocks/ 'jordbruksblock') is managed by the Swedish Board of Agriculture, and also made available on the INSPIRE Geoportal. The dataset does not contain all agricultural land in Sweden, but only the parts for which a farmer has applied for support at some point.

A block is a polygon/surface that delimits an area of agricultural land. A block is delimited by fixed boundaries. Examples of fixed boundaries are roads, stone walls, forests and buildings.

The dataset Agricultural blocks contains approximately 1,143,000 blocks. Of these, approximately 891,000 are arable land blocks and approximately 252,000 pasture land blocks. The total area is 3.2 million hectares, of which 2.7 million hectares are arable land and 510,000 hectares are pasture land. The average area for the arable land blocks is 3.03 ha and for the pasture blocks the corresponding figure is 2.03 ha.

_The field boundaries are valid for the year 2021_

- **Original Data Provider:** [The Swedish Agency for Agriculture](https://www.geodata.se/geodataportalen/srv/swe/catalog.search;jsessionid=6C2D281619D69AC2356E1BD4C1923A3A#/metadata/df439ba5-014e-44ec-86cb-ddb9e5ba306c)
- **Converted by:** Kerner Lab (ASU)
- **License:** No restrictions on public access
- **Projection:** EPSG:4326

---

## Columns

| Property               | Data Type |
| ---------------------- | --------- |
| id                     | string    |
| determination_datetime | datetime  |
| area                   | float     |
| land_cover_category    | string    |
| land_cover_class       | string    |
| region                 | string    |

Source Cooperative product page

README

Dataset files (1 × .parquet)

No restrictions on public access, Swedish government data


Fields of The World: Field Boundaries for Vietnam

This dataset contains 120,913 field polygons from Vietnam, digitized from satellite imagery with rigorous quality control and topological consistency checks. The polygons are distributed across 30 tiles of approximately 5×5 km each, covering diverse landscape types and field sizes. Field boundaries are valid for August 2021.

## Description

This dataset is a obtained from AI4SmallFarms dataset publised by the University of Twente Research Information. It is updated by the requiremnt of the fieldscapes project.

The large-scale data set consists of 120,913 field polygons divided into 30 tiles of approximately 5×5 km distributed across Vietnam, covering a range of fields and diverse landscape types. The field polygons have been meticulously digitized by original author from satellite images, following a rigorous multi-step quality control process and topological consistency checks.

_The field boundaries are valid for the year August 2021_

- **Original Data Provider:** [DATA Archiving and Networked Services (DANS)](https://research.tudelft.nl/en/publications/ai4smallfarms-a-dataset-for-crop-field-delineation-in-southeast-a)
- **Converted by:** Kerner Lab (ASU)
- **License:** CC-BY-4.0
- **Projection:** EPSG:4326

---

## Columns

| Property               | Data Type |
| ---------------------- | --------- |
| id                     | string    |
| determination_datetime | datetime  |
| area                   | float     |
| \_predicate            | string    |

Source Cooperative product page

README

Dataset files (1 × .parquet)


Fields of The World (FTW)

Fields of The World (FTW) is a benchmark dataset for training and evaluating machine learning models for instance segmentation of agricultural field boundaries. It covers 24 countries across four continents with approximately 1.6 million parcel boundaries and over 70,000 samples, each pairing instance and semantic segmentation masks with multi-date, multi-spectral Sentinel-2 imagery. Train/validation/test splits use a blocked random strategy to prevent spatial data leakage.

field-boundaries agriculture remote-sensing land-parcels lpis instance-segmentation sentinel-2 machine-learning

Dataset files (352753 x .tif)

Source Cooperative product page

## README.md
## Version : v1.0.0

## Description

**Fields of The World (FTW)** is a comprehensive benchmark dataset designed to enhance the development of machine learning models for instance segmentation of agricultural field boundaries. This dataset aims to meet the growing need for accurate and scalable field boundary data for global agricultural monitoring and assessments.

### Key Features:

1. **Near-Global Coverage**: FTW spans four continents—Europe, Africa, Asia, and South America—covering diverse agricultural landscapes across 24 countries. This extensive geographic coverage allows for the development of models that can generalize well to different agricultural practices and field types.

2. **Large-Scale Dataset**: With approximately 1.6 million parcel boundaries and over 70,000 samples, FTW is significantly larger than previously available datasets. Each sample includes instance and semantic segmentation masks paired with multi-date, multi-spectral Sentinel-2 satellite images, enabling detailed temporal and spectral analysis.

3. **Multi-class Segmentation**: The dataset provides masks for both instance segmentation and semantic segmentation with different classes, including:
   - **Instance Segmentation Masks**: To identify individual fields.
   - **Semantic Segmentation Masks**:
     - Two-class masks: Background and polygon (field).
     - Three-class masks: Background, polygon (field), and boundaries.

4. **Spectral Richness**: The dataset includes RGB (Red, Green, Blue) and NIR (Near-Infrared) spectral bands from Sentinel-2 images.

5. **Temporal Richness**: The dataset includes multi-date imagery to capture different stages of the growing season. Two images with distinct contrast differences were selected to represent these stages. To determine the date ranges for these images, the USDA Crop Calendar was initially referenced and then refined by selecting periods with minimal cloud cover and optimal contrast between the two images.

6. **Comprehensive Data Splits**: The dataset is carefully divided into training, validation, and test sets to ensure accurate evaluation of model performance. For each country, larger tiles are divided into smaller chips measuring 1536x1536 m². To prevent data leakage due to spatial autocorrelation, a blocked random splitting strategy is used. Chips are grouped into 3×3 blocks, with 80% allocated to training, 10% to validation, and 10% to testing.

7. **Metadata and Documentation**: The metadata and documentation provide crucial information to help users effectively interpret and utilize the dataset. It includes key details about the country of focus, temporal data collection windows, grid structures, and the year of collection.

   1. **Country**: The geographic region the dataset focuses on.
   2. **Crop Types**: Crop types used to filter the polygons in the dataset, with specific keywords used for filtering.
   3. **Seasons**: Date ranges defining the temporal windows for data collection.
   4. **Year of Collection**: The year when the polygon boundaries were captured.
   5. **Grids**: Larger grids from which smaller chips are derived, with some grids spatially separated to optimize area coverage.

---

## Dataset Directory Structure

```
Fields of The World
├── README.md                      -> This File
├── ftw-sources.pmtiles            -> File to visualize the source field boundaries on https://fieldsofthe.world/map/
├── austria                        -> Country Folder
│   ├── label_masks                -> Labels Folder
│   │   ├── instance               -> Instance Segmented Masks (Label) (Masks in .tif Format)
│   │   ├── semantic_2class        -> Semantic Segmented Masks (Label) (Masks in .tif Format) -> Contains 2 Classes (0-Background, 1-Polygon)
│   │   └── semantic_3class        -> Semantic Segmented Masks (Label) (Masks in .tif Format) -> Contains 3 Classes (0-Background, 1-Polygon, 2-Boundaries)
│   ├── s2_images                  -> Images Folder (Contains image chips)
│   │   ├── window_a               -> Window A images (Images in .tif Format)
│   │   └── window_b               -> Window B images (Images in .tif Format)
│   ├── chips_austria.parquet      -> Chips file in geoparquet format, contains split details (Each chips belongs in one of Train/Val/Test split)
│   └── data_config_austria.json   -> Contains meta data about the bigger grids for the dataset, crop types, dates for temporal windows.
├── austria.zip                    -> Country Zip Folder, this contains all the files in the country directory.
└── checksum.md5                   -> Checksum MD5 file containing all the individual files checksum hashes.
..... Continues for all the countries in the same format.
```

---

## Dataset Information

| Country        | Year of Validity | Parcel Counts | Chips | Train Split | Validation Split | Test Split | Source Polygons                                                                                | Source Data License                                                                                |
| -------------- | ---------------- | ------------- | ----- | ----------- | ---------------- | ---------- | ----------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Austria        | 2021             | 196101        | 6686  | 5304        | 637              | 745        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-austria/description/)     | CC-BY-4.0                                                                                |
| Belgium        | 2021             | 63431         | 1941  | 1554        | 189              | 198        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-belgium/description/)     | [No restrictions on public access](https://metadata.vlaanderen.be/srv/dut/catalog.search#/metadata/d869db4a-73f4-470e-b5ca-a0f7cd3ab585)                                                      |
| Brazil         | 2020             | 1854          | 1607  | 1289        | 130              | 188        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-brazil/description/)      | CC-BY-4.0                                                                                |
| Cambodia       | 2021             | 318088        | 344   | 274         | 36               | 34         | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-cambodia/description/)    | CC-BY-4.0                                                                                |
| Corsica        | 2021             | 5360          | 2472  | 1974        | 240              | 258        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-corsica/description/)     | CC-BY-2.0                                                                                |
| Croatia        | 2023             | 157481        | 3482  | 2778        | 351              | 353        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-croatia/description/)     | [Open Data](https://www.apprrr.hr/wp-content/uploads/2024/01/Otvoreni-podaci-APPRRR.docx)                                                                                |
| Denmark        | 2021             | 37677         | 3560  | 2868        | 360              | 332        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-denmark/description/)     | CC0-1.0                                                                                |
| Estonia        | 2021             | 26695         | 6713  | 5348        | 681              | 684        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-estonia/description/)     | CC-3.0                                                                                |
| Finland        | 2021             | 57323         | 5665  | 4527        | 550              | 588        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-finland/description/)     | CC-BY-4.0                                                                                |
| France         | 2020             | 55342         | 3744  | 2988        | 360              | 396        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-france/description/)      | [Open Licence](https://geoservices.ign.fr/sites/default/files/2021-07/DC_DL_RPG_1-0.pdf)                                                                                |
| Germany        | 2018/2019        | 4598          | 686   | 306         | 30               | 350        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-germany/description/)     | DL-DE/BY-2-0                                                                                |
| India          | 2016             | 10013         | 2002* | 1281        | 300              | 399        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-india/description/)       | CC-BY-4.0                                                                                |
| Kenya          | 2022             | 874           | 391   | 316         | 20               | 55         | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-kenya/description/)       | GPL-2.0-or-later                                                                                |
| Latvia         | 2021             | 44964         | 6938  | 5529        | 668              | 741        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-latvia/description/)      | CC-BY-NC-4.0                                                                                |
| Lithuania      | 2021             | 61424         | 5258  | 4208        | 522              | 528        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-lithuania/description/)   | [Non-commercial use only](https://www.geoportal.lt/metadata-catalog/catalog/search/resource/details.page?uuid=%7B7AF3F5B2-DC58-4EC5-916C-813E994B2DCF%7D)                                     |
| Luxembourg     | 2022             | 29018         | 808   | 643         | 81               | 84         | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-luxembourg/description/)  | CC0-1.0                                                                                |
| Netherlands    | 2022             | 43169         | 3879  | 3110        | 381              | 388        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-netherlands/description/) | CC0-1.0                                                                                |
| Portugal       | 2021             | 5040          | 86    | 64          | 12               | 10         | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-portugal/description/)    | CC-BY-NC-4.0                                                                                |
| Rwanda         | 2021             | 1532          | 70    | 57          | 6                | 7          | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-rwanda/description/)      | CC-BY-4.0                                                                                |
| Slovakia       | 2021             | 14242         | 4073  | 3275        | 390              | 408        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-slovakia/description/)    | CC0-1.0                                                                                |
| Slovenia       | 2021             | 67488         | 2177  | 1733        | 216              | 228        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-slovenia/description/)    | CC-BY-4.0                                                                                |
| South Africa   | 2018             | 6568          | 747   | 590         | 72               | 85         | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-southafrica/description/) | CC-BY-NC-SA-4.0                                                                                |
| Spain          | 2020             | 258465        | 2440  | 2019        | 202              | 219        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-spain/description/)       | CC-BY-4.0                                                                                |
| Sweden         | 2021             | 39718         | 4760  | 3802        | 442              | 516        | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-sweden/description/)      | [No restrictions on public access](https://www.geodata.se/geodataportalen/srv/swe/catalog.search;jsessionid=6C2D281619D69AC2356E1BD4C1923A3A#/metadata/df439ba5-014e-44ec-86cb-ddb9e5ba306c)  |
| Vietnam        | 2021             | 120913        | 288   | 229         | 36               | 23         | [Link](https://beta.source.coop/repositories/kerner-lab/fields-of-the-world-vietnam/description/)     | CC-BY-4.0                                                                                |

_\*India has a total of 2,002 chips available. Of these, 22 chips are marked as `'none'` for the split column as per the original data curator. Thus, 1,980 chips have been used in the train/validation/test splits in India._

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

providers: role [producer, processor] for 'Kerner Lab' is an AI guess — Inferred as producer/processor from dataset URL pattern (kerner-lab); institutional affiliation not stated in README.. Verify before flipping to _state: reviewed.


Fieldscapes: Field Boundaries for Austria

This dataset is a subset of the dataset obtained from the Paying Agency Agrarmarkt of Austria. The subset was created for inclusion in the Fieldscapes project.

fieldscapes
agriculture
field boundaries


Fieldscapes: Field Boundaries for Belgium

This dataset is a subset of the dataset obtained from the Agriculture and Sea Fisheries Agency of Belgium. The subset was created for inclusion in the Fieldscapes project.

fieldscapes
field boundaries
agriculture


Fieldscapes: Field Boundaries for Brazil

This dataset is a subset of the supplementary data of a paper LEM+ dataset: for agricultural remote sensing applications. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Cambodia

This dataset contains the same polygons from AI4SmallFarms by the University of Twente Research Information. It was adapted for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Corsica

This dataset is a subset of the dataset obtained from the Republique Francaise - Geoservices of France. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Croatia

This dataset is a subset of the dataset obtained from the Agency for Payments in Agriculture, Fisheries, and Rural Development of Croatia.


Fieldscapes: Field Boundaries for Denmark

Danish field boundaries from the Land Parcel Identification System (LPIS), representing approximately 2.8 million hectares of agricultural land organized into geographically coherent field blocks. Each field block includes attributes such as area, block type, and eligibility status for EU support schemes. Valid for 2021.

field-boundaries agriculture lpis land-parcels land-use

Dataset files (1 x .parquet)

Source Cooperative product page

## README.md
# Field boundaries for Denmark (FieldScapes)

Danish Land Parcel Identification System (LPIS) data collection or (field blocks/ 'Markblokke') is managed by The Danish Agency for Agriculture.

The field block map is a digital field map, with agricultural areas gathered in field blocks. A field block is a geographically coherent unit consisting of agricultural land. The boundaries of the field blocks typically follow permanent divisions in the landscape. The map is used in the administration of cases linked to the geographical determination of cultivation areas, primarily by the EU's area-based support schemes. The field block map is continuously updated on the basis of orthophotos and reports from farmers and authorities. The field block map covers approx. 2.8 million hectares of agricultural land. Each field block is identified with a field block number, area, type. A field block means a continuous area on which one or more farmers grow one or more different crops. The map is updated every night with changes from case processing.

_The field boundaries are valid for the year 2021_

- **Original Data Provider:** [The Danish Agency for Agriculture](https://geodata-info.dk/srv/eng/catalog.search#/metadata/d91b2c99-d9b0-4e6d-b323-20ac80548186)
- **Converted by:** Kerner Lab (ASU)
- **License:** CC0-1.0
- **Projection:** EPSG:4326

---

## Columns

| Property               | Data Type |
| ---------------------- | --------- |
| id                     | string    |
| determination_datetime | datetime  |
| area                   | float     |
| eligible_area          | float     |
| ineligible_area        | float     |
| deduction              | float     |
| support_proc           | float     |
| block_type             | string    |


Fieldscapes: Field Boundaries for Estonia

This dataset is a subset obtained from the Agricultural Registers and Information Agency in EuroCrops. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Finland

This dataset is a subset of the dataset from the Finnish Food Authority of Finland. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for France

This dataset is a subset of the dataset obtained from the Services and Payments Agency(ASP) of France. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Germany

This is a subset of the dataset obtained from the Fusion Competition. It was adapted for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for India

This dataset contains the same polygons from "Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision" paper. It was adapted for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Kenya

This dataset contains the same polygons from ECAAS Project. It was adapted for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Latvia

This is a subset of the entire dataset provided by the Rural Support Service of Latvia. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Lithuania

This dataset is a subset of the dataset obtained from Eurocrops adapted from the National Paying Agency under the Ministry of Agriculture. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Luxembourg

This dataset is a subset of the dataset obtained from the Agricultural Technical Services Administration. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Netherlands

This dataset is a subset of the dataset obtained from the Netherlands Enterprise Agency. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Portugal

This dataset is a subset of the original data source from SIP - INSTALLMENT IDENTIFICATION SYSTEM website. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Romania (deleteMe)

This dataset is a subset of the dataset from Eurocrops and was acquired by the Ministry of Regional Development and Public Administration. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Rwanda

This dataset contains field boundaries for smallholder farms in eastern Rwanda for the 2021 growing season for the purpose of conducting the Rwanda Field boundary detection Challenge. It was adapted for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Slovakia

This dataset is a subset of the dataset from Agricultural Payment Agency by the INSPIRE dataset. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Slovenia

This dataset is a subset of the dataset from the Ministry of Agriculture, Forestry and Food and provided by Agency of the Republic of Slovenia for Agricultural Markets and Rural Development. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for South Africa


Fieldscapes: Field Boundaries for Spain

This dataset is a subset of the dataset obtained from the SIGPAC Nevarra. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Sweden

This dataset is a subset of the dataset obtained from the Swedish Agency for Agriculture. The subset was created for inclusion in the Fieldscapes project.


Fieldscapes: Field Boundaries for Vietnam

This dataset contains the same polygons from AI4SmallFarms by the University of Twente Research Information. It was adapted for inclusion in the Fieldscapes project.


Fields Of The World (FTW) Deprecated - TO BE DELETED

Fields Of The World is a machine learning benchmark dataset for instance segmentation of agricultural field boundaries.


Field Boundaries (Delineate Anything)

This dataset contains national-scale agricultural field boundary delineations generated using the DelAnyFlow methodology, which is a resolution-agnostic, deep-learning approach for the automated extraction of field boundaries from satellite imagery. The products consist of topologically consistent, non-overlapping vector polygons of individual agricultural fields derived from Sentinel-2 imagery with resolutions of 5 m.
Currently, the dataset includes complete coverage of Ukraine for 2024, with 4.35 million delineated fields at 5 m resolution. These boundaries were generated using the DelAny instance segmentation model trained on the FBIS-22M dataset (22.9 million field instances across multiple resolutions from 0.25m to 10m).
The delineation products are analysis-ready and designed for operational applications including crop type classification, yield estimation, agricultural statistics, subsidy auditing, and food security monitoring. They demonstrate boundary completeness and quality compared to existing operational products, particularly for smallholder and fragmented agricultural systems (0.25-1 hectare fields).


GIS Data by Karnataka Geographic Information System

Administrative boundary datasets for Karnataka state covering multiple administrative levels including districts, taluks, gram panchayats, and electoral boundaries. Data compiled from remote sensing and GIS sources by the Karnataka State Remote Sensing Applications Centre (KSRSAC).

administrative-boundaries district-boundaries electoral-boundaries remote-sensing

Dataset files (94 x .parquet)

Source Cooperative product page

## README.md
# Karnataka Geographic Information System

Karnataka State Remote Sensing Applications Centre (KSRSAC) was established in the year 1986 and designated as the Nodal Agency for implementation of Remote Sensing (including Photogrammetry) and GIS programmes in the State. The KSRSAC will be the repository of the information / data / maps collected by the Centre or by any Government Department, Statutory Board or any Undertaking through GIS and Remote Sensing. The nodal agency will create GIS on base maps and general / common features and provide full services on Remote Sensing and GIS to all the Line Departments. The nodal agency will work out the planning / architecture of Remote Sensing and GIS implementation / feasibility for new proposals / coding / standardization / training as well as to organize periodic workshops to bring more interaction and co-ordination among the Line Departments.

You can get more information at [here](https://kgis.ksrsac.in/kgis/home.aspx)

## About data

This data contains some of the datasets such as
- Karnataka State Boundary
- District Boundaries
- Taluk Boundaries
- Hobli Boundaries
- GP Boundary
- Assembly Boundaries
- Parliamentary Boundaries

We'll be adding more datasets into this in the future

## Maintainer

[Krishna Lodha](https://krishnaglodha.com/), Founder of [Rotten Grapes](https://rottengrapes.tech/) is the maintainer of this data, if needed, you can reach out over [email](https://rottengrapes.tech/)

Karnataka Geographic Information System

This repo contains data set published by Karnataka Geographic Information System on their official portal https://kgis.ksrsac.in/kgis/downloads.aspx


High-resolution drone satellite imagery of the Kenol section of Murang'a, Kenya, with pixel-level labels for building footprint and field boundary segmentation. Images are 512×512 RGBA tiles in Cloud Optimized GeoTIFF format. Labels encode building footprints (class 1), field boundaries (class 2), overlapping areas (class 3), and background (class 0).

field-boundaries remote-sensing agriculture building-footprints segmentation drone-imagery

Dataset files (4148 x .tif)

Source Cooperative product page

## README.md
## About the Dataset
This dataset provides high-resolution satellite imagery collected via drones in the Kenol section of Murang'a, Kenya. The dataset includes both raw drone imagery and labeled imagery intended for building footprint and field boundary segmentation tasks.

- **Provided by**: [@ksa](https://beta.source.coop/ksa/)
- **Author**: Kenya Space Agency

## Data Format

The dataset is structured in Cloud Optimized GeoTIFF format, which enables efficient access to large geospatial datasets over the web.

## Usage

The dataset is suitable for training and evaluating models for field boundary and building footprint segmentation tasks. The labels are provided in the `Labels` directory, with building footprints labeled with a class value of 1, and field boundaries labeled with a class value of 2.
In the case where building footprint is within field boundary, it has a class value of 3.
Every other item not either of the two has a class value of 0.

## Image Details

- **Image Dimensions**: 512 x 512 pixels
- **Image Channels**: RGBA

## License

This dataset is provided under the [Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/). Please read the [LICENSE](LICENSE) file for more details.

## Download Dataset
This dataset can be dowloaded via the `ACCESS DATA` tab. Labels can be found within the `labels` sub-directory and `imagery` can be found within the imagery sub-directory

## Citation

If you find this dataset useful in your work, please consider citing it:
````
@dataset{kenol-section,
  author = {Kenya Space Agency},
  title = {Kenya Building Footprints and Field Boundaries Dataset: This dataset contains satellite imagery collected from drones in Kenol section, Murang'a Kenya. Dataset contains the drone imagery and labelled imagery},
  year = {2023},
  publisher = {Kenya Space Agency},
  howpublished = {\url{https://beta.source.coop/ksa/kenol-section}},
}
````


METAR Observations 2024

**Testing** This repository will contain a GeoParquet-formatted file consisting of hourly worldwide METAR observations.


Annual NLCD Land Cover data

Annual National Land Cover Database (NLCD) land cover classification rasters for the conterminous United States, distributed as Cloud-Optimized GeoTIFFs. Produced by USGS EROS, the dataset provides yearly land cover maps derived from Landsat imagery covering the lower 48 states. Files are mirrored from the USGS requester-pays S3 bucket.

land-cover remote-sensing cloud-optimized-geotiff earth-observation time-series landsat satellite-imagery

Dataset files (240 x .tif)

Source Cooperative product page

providers: role [producer, licensor] for 'USGS EROS' is an AI guess — USGS EROS is the named source of the data; licensor role inferred from government agency authorship — no explicit license terms stated.. Verify before flipping to _state: reviewed.


Potential Above-Ground Combustion in Boreal and Arctic North America for SSP585

This dataset contains potential above-ground carbon emissions (g/m2) from wildfire combustion. It's primary purpose is for fire mangers to use this map to identify areas with high carbon vulnerability to wildfires for protection as a climate solution strategy to mitigate potential carbon losses. This dataset was created using a ML model trained on field observations in boreal and arctic Canada and Alaska, spatial features such as annual land-cover, tree canopy cover, above-ground biomass, and CMIP6 projected climate variables from SSP-585.


ftw-confidence-layers


test

Just for random tests


Copernicus DEM GLO-30


Sentinel-2 L1C


Major TOM Elliot-Pretrain

A unified multi-modal Earth Observation pre-training dataset combining Sentinel-2, Landsat 8/9, Copernicus DEM, and ESA WorldCover on a global 10 km grid. 250,000 tiles, TACO v3 format.


MajorTOM Burst

ELLIOT-Pretrain is a multi-modal Earth Observation dataset for pre-training AI foundation models, built on the MajorTOM global 10 km grid. It provides co-registered 10.56 km tiles across four sensors (Sentinel-2 L1C, Landsat 8/9 OLI-TIRS, Copernicus DEM GLO-30, ESA WorldCover) in three temporal regimes: monotemporal (250k tiles), monthly time series (12.5k × 12 timesteps), and burst/rapid-event (16.7k × 6 timesteps at ~5-day cadence). Tile locations were selected via hierarchical spherical k-means clustering to maximise global environmental diversity.

taco majortom sentinel-2 landsat-8 earth-observation remote-sensing global-coverage elliot multitemporal time-series burst change-detection land-use landsat foundation-model

Image and Signal Processing Group (ISP), Universitat de Valencia

European Commission - Horizon Europe (ELLIOT, Grant 101214398)

Dataset files (1808324 x .tif)

Source Cooperative product page

## README.md
# Major TOM ELLIOT-Pretrain

[![Source Cooperative](https://img.shields.io/badge/Source_Cooperative-elliot--pretrain-blue)](https://source.coop/major-tom/elliot-pretrain)
[![TACO v3](https://img.shields.io/badge/format-TACO_v3-green)](https://github.com/asterisk-labs/taco/blob/main/spec/main.md)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1_hJWZ9rvBcIIrG5zHD1xsz11LZIJfwxB)
[![License](https://img.shields.io/badge/license-CC--BY--SA--4.0-orange)](https://creativecommons.org/licenses/by-sa/4.0/)

ELLIOT-Pretrain is a Major TOM expansion focused on **fast pre-training** of multi-modal AI foundation models on Earth Observation data.

* Built on the original [MajorTOM](https://github.com/ESA-PhiLab/Major-TOM) global 10 km grid
* 10.56 km tiles with harmonised 10 m resolution across all sensors for fast access
* Four co-registered modalities in every tile, optical, thermal, elevation, and land cover
* Three sub-datasets covering complementary temporal regimes

Tile locations were selected using hierarchical spherical k-means clustering over [AlphaEarth Foundation](https://huggingface.co/Major-TOM/AlphaEarth-Foundations) embeddings to maximise global environmental diversity. The dataset follows the [TACO v3 specification](https://github.com/asterisk-labs/taco/blob/main/spec/main.md).

## Sub-datasets

| Part | Name | Tiles | Timesteps | Cadence | Purpose |
|------|------|-------|-----------|---------|---------|
| A | Monotemporal | 250,000 | 1 | n/a | Global coverage, spatial diversity |
| B | Monthly | 12,500 | 12 | monthly | Seasonal dynamics, phenology |
| C | Burst | 16,666 | 6 | ~5-day | Rapid events, floods, fires, landslides |

All three parts share the same four sensors.

| Modality | Source | Resolution | Bands |
|----------|--------|------------|-------|
| Sentinel-2 L1C | ESA Copernicus | 10 m | 13 spectral |
| Landsat 8/9 OLI-TIRS | USGS | 30 m | 11 (9 OLI + 2 TIRS) |
| Copernicus DEM GLO-30 | ESA / TanDEM-X | 30 m | 1 (elevation) |
| ESA WorldCover | ESA | 10 m | 1 (land cover class) |

## Quick Start

Pick a tile index from Part A (monotemporal) and visualize all four modalities.

```python
import numpy as np
import rasterio
import matplotlib.pyplot as plt

BASE = "https://data.source.coop/major-tom/elliot-pretrain"
IDX = 42  # change this to explore different tiles

MODS = ["s2", "l8", "dem", "lc"]
fig, axes = plt.subplots(1, 4, figsize=(20, 5))

for j, mod in enumerate(MODS):
    url = f"/vsicurl/{BASE}/monotemporal/DATA/{IDX}/{mod}/data.tif"
    with rasterio.open(url) as src:
        if mod in ("s2", "l8"):
            rgb = src.read([4, 3, 2]).astype(np.float32)
            axes[j].imshow(np.clip(rgb / 3000, 0, 1).transpose(1, 2, 0))
        elif mod == "dem":
            axes[j].imshow(src.read(1), cmap="terrain")
        else:
            axes[j].imshow(src.read(1), cmap="tab20")
    axes[j].set_title(mod)
    axes[j].axis("off")

plt.tight_layout()
plt.show()
```

## Reproducible Example

Full notebook covering all three parts, metadata queries with filtering, and a streaming PyTorch DataLoader with parallel fetching.

**[Open in Colab](https://colab.research.google.com/drive/1_hJWZ9rvBcIIrG5zHD1xsz11LZIJfwxB)**

## Dataset Structure

```
elliot-pretrain/
├── monotemporal/          # Part A, 250k tiles, 1 timestep
│   ├── COLLECTION.json
│   ├── METADATA/
│   │   ├── collection.parquet
│   │   ├── sample__s2.parquet
│   │   ├── sample__l8.parquet
│   │   ├── sample__dem.parquet
│   │   └── sample__lc.parquet
│   └── DATA/{tile_id}/
│       ├── s2/data.tif
│       ├── l8/data.tif
│       ├── dem/data.tif
│       └── lc/data.tif
├── monthly/               # Part B, 12.5k tiles, 12 timesteps
│   ├── COLLECTION.json
│   ├── METADATA/          # same 5 parquet files
│   └── DATA/{tile_id}/
│       ├── s2/img_00.tif ... img_11.tif
│       ├── l8/img_00.tif ... img_11.tif
│       ├── dem/main.tif
│       └── lc/main.tif
└── burst/                 # Part C, 16.7k tiles, 6 timesteps (~5-day)
    ├── COLLECTION.json
    ├── METADATA/          # same 5 parquet files
    └── DATA/{tile_id}/
        ├── s2/img_00.tif ... img_05.tif
        ├── l8/img_00.tif ... img_05.tif
        ├── dem/main.tif
        └── lc/main.tif
```

Join key across metadata files is `internal:parent_id`, which points back to `internal:current_id` in `collection.parquet`.

## Citation

```bibtex
@inproceedings{francis2024majortom,
  title={Major TOM: Expandable Datasets for Earth Observation},
  author={Francis, Alistair and Czerkawski, Mikolaj},
  booktitle={IGARSS 2024},
  pages={2935--2940},
  year={2024},
  doi={10.1109/IGARSS53475.2024.10640760}
}
```

## License

CC-BY-SA-4.0

## Acknowledgements

ELLIOT-Pretrain has been made possible thanks to [Asterisk Labs](https://asterisk.coop), the [ELLIOT](https://elliot-earth.eu) project (European Commission, Horizon Europe, Grant 101214398), and the [Image and Signal Processing Group (ISP)](https://isp.uv.es) at Universitat de Valencia.

providers: role [producer] for 'ESA Copernicus' is an AI guess — Upstream data provider for Sentinel-2 and Copernicus DEM; role inferred from sensor table. Verify before flipping to _state: reviewed.

providers: role [producer] for 'USGS' is an AI guess — Upstream data provider for Landsat 8/9; role inferred from sensor table. Verify before flipping to _state: reviewed.

providers: role [licensor] for 'ELLIOT Project (European Commission Horizon Europe)' is an AI guess — Funding body acknowledged; licensor role inferred from grant association. Verify before flipping to _state: reviewed.


ESA WorldCover 2021


Index

The Major TOM Index is a global metadata catalog covering over 5 million 10 km tiles aligned to the Sentinel-2 MGRS grid at 10 m resolution. Each tile is enriched with terrain, climate, soil, socioeconomic, and administrative attributes derived from public Earth Engine datasets. Five GeoParquet files provide the complete global grid, land-only subsets, sensor-linked records for Sentinel-2 and Landsat 8/9 with Google Earth Engine image IDs, and a curated ELLIOT subset with split assignments for Earth observation foundation model research.

remote-sensing earth-observation sentinel-2 landsat satellite-imagery foundation-model geoparquet global-coverage

elliot.parquet

[admin_code] Sentinel-2 MGRS tile code

[placename] Country name

[placename] State or province name

[placename] District or county name

geometry

[geometry] WKB tile footprint geometry for spatial queries

global.parquet

[admin_code] Sentinel-2 MGRS tile code

[placename] Country name

[placename] State or province name

[placename] District or county name

land.parquet

[admin_code] Sentinel-2 MGRS tile code

[placename] Country name

[placename] State or province name

[placename] District or county name

land_l8.parquet

[placename] Country name

[placename] State or province name

[placename] District or county name

land_s2.parquet

[placename] Country name

[placename] State or province name

[placename] District or county name

Source Cooperative product page

## README.md
# Index

[![Source Cooperative](https://img.shields.io/badge/Source_Cooperative-majortom--index-blue)](https://source.coop/major-tom/index)
[![License](https://img.shields.io/badge/license-CC--BY--4.0-orange)](https://creativecommons.org/licenses/by/4.0/)

The Major TOM Index is a global metadata catalog for the [Major TOM](https://github.com/ESA-PhiLab/Major-TOM) grid at 10 km resolution. It provides a single entry point to discover, filter, and select tiles across sensors, locations, and time without downloading any imagery.

The index covers over 5 million tiles spanning the entire Earth. Each tile corresponds to a 1056 × 1056 px patch (10.56 × 10.56 km) aligned to Sentinel-2 MGRS tiles at 10 m resolution. Every tile is enriched with terrain, climate, soil, socioeconomic, and administrative attributes derived from public Earth Engine datasets.

**What can you do with this index?**

- **Find tiles by location.** Filter by country, state, MGRS tile code, or bounding box using the GeoParquet geometry column.
- **Select tiles by environmental criteria.** Want arid, high-elevation tiles? Filter by `climate:precipitation < 200` and `terrain:elevation > 3000`.
- **Stratify sampling for training sets.** Use the enrichment columns to build geographically and environmentally balanced splits for foundation model pretraining.
- **Link to imagery.** The `land_s2` and `land_l8` files include sensor-specific image IDs (`s2:id_gee`, `l8:id_gee`) that point directly to the source products in Google Earth Engine.
- **Use the ELLIOT splits.** The `elliot.parquet` file provides pre-built monotemporal and temporal splits designed for multi-sensor, multi-temporal EO research.

All files are self-contained GeoParquet with ZSTD compression, sorted by `majortom:code_1000km` → `majortom:code_100km` → `id` for efficient spatial predicate pushdown.

## Schema

Columns are organized into namespaces. Each namespace groups related attributes.

### Grid (`majortom:`)

Tile identity and spatial reference within the Major TOM grid system.

| Column | Type | Description |
|--------|------|-------------|
| `id` | string | Unique tile identifier (e.g. `MT10_770U_395R`). |
| `majortom:code_100km` | string | Parent 100 km grid cell. Used for spatial grouping. |
| `majortom:code_1000km` | string | Parent 1000 km grid cell. Used for coarse-level partitioning. |
| `majortom:crs` | string | Native UTM CRS of the tile (e.g. `EPSG:32647`). |
| `majortom:mgrs_tile` | string | MGRS tile code (e.g. `47WNS`). Links to Sentinel-2 tiling grid. |
| `majortom:mgrs_n` | uint8 | Number of overlapping MGRS tiles (1 after deduplication). |
| `majortom:mgrs_candidates` | list\<string\> | All candidate MGRS tiles before deduplication. |
| `majortom:footprint_pct` | float | Percentage of tile covered by the assigned MGRS tile. |
| `majortom:geotransform` | list\<int32\> | Snapped affine geotransform [originX, scaleX, shearX, originY, shearY, scaleY]. |
| `majortom:geotransform_raw` | list\<double\> | Original (unsnapped) affine geotransform. |

### STAC (`stac:`)

Spatial and temporal reference following STAC conventions. Present in `land_s2` and `land_l8` only, where it replaces the `majortom:` grid columns.

| Column | Type | Description |
|--------|------|-------------|
| `stac:crs` | string | Coordinate reference system. |
| `stac:geotransform` | list\<int64\> | Affine geotransform for the image patch. |
| `stac:tensor_shape` | list\<int32\> | Shape of the image tensor [bands, height, width]. |
| `stac:time_start` | int64 | Acquisition start time (Unix timestamp). |
| `stac:time_end` | int64 | Acquisition end time (Unix timestamp). |

### Sentinel-2 (`s2:`)

Sensor metadata for the assigned Sentinel-2 image. Present in `land_s2` only.

| Column | Type | Description |
|--------|------|-------------|
| `s2:id_gee` | string | Google Earth Engine image ID. Use this to fetch the actual imagery. |
| `s2:product_id` | string | ESA product identifier. |
| `s2:spacecraft` | string | Spacecraft name (Sentinel-2A or Sentinel-2B). |
| `s2:processing_baseline` | string | Processing baseline version. |
| `s2:orbit_number` | uint16 | Relative orbit number. |
| `s2:mean_solar_azimuth` | float | Mean solar azimuth angle, averaged across all bands and detectors (degrees). |
| `s2:mean_solar_zenith` | float | Mean solar zenith angle, averaged across all bands and detectors (degrees). |
| `s2:mean_view_azimuth` | float | Mean viewing azimuth angle from band B8 (degrees). |
| `s2:mean_view_zenith` | float | Mean viewing zenith angle from band B8 (degrees). |
| `s2:reflectance_conversion` | float | Reflectance conversion factor (U correction). |

> **Note on solar vs viewing angles.** The sun has a single position relative to the scene, so ESA provides one solar azimuth and one solar zenith averaged across all bands. Viewing angles are different: Sentinel-2 uses a pushbroom sensor where each spectral band has its own detector array in the focal plane, each observing from a slightly different angle. That is why GEE provides per-band viewing angles (`MEAN_INCIDENCE_*_ANGLE_B1` through `_B12`). We use band B8 (NIR, 10 m) as the reference because it is at native 10 m resolution and sits near the center of the focal plane, making it a representative proxy for the viewing geometry of the 10 m and 20 m bands.

### Landsat 8/9 (`l8:`)

Sensor metadata for the assigned Landsat image. Present in `land_l8` only.

| Column | Type | Description |
|--------|------|-------------|
| `l8:id_gee` | string | Google Earth Engine image ID. Use this to fetch the actual imagery. |
| `l8:product_id` | string | USGS product identifier. |
| `l8:spacecraft` | string | Spacecraft name (Landsat 8 or Landsat 9). |
| `l8:collection_number` | uint8 | USGS Collection number. |
| `l8:collection_category` | string | Collection category (T1, T2, RT). |
| `l8:processing_software` | string | Processing software version. |
| `l8:wrs_path` | uint16 | WRS-2 path number. |
| `l8:wrs_row` | uint16 | WRS-2 row number. |
| `l8:cloud_cover` | float | Scene cloud cover percentage. |
| `l8:sun_azimuth` | float | Sun azimuth angle (degrees). |
| `l8:sun_elevation` | float | Sun elevation angle (degrees). |
| `l8:earth_sun_distance` | float | Earth-Sun distance (astronomical units). |
| `l8:image_quality_oli` | uint8 | OLI image quality score. |
| `l8:roll_angle` | float | Spacecraft roll angle (degrees). |

### Terrain (`terrain:`)

| Column | Type | Range | Description |
|--------|------|-------|-------------|
| `terrain:elevation` | float | ~-420 to 8,849 (m) | Mean elevation in meters from the [Copernicus GLO-30 DEM](https://gee-community-catalog.org/projects/glo30/), a 30 m resolution Digital Surface Model derived from TanDEM-X radar satellite data (2011 to 2015). Includes buildings, infrastructure, and vegetation. Uses the EGM2008 vertical datum. |

### Climate (`climate:`)

| Column | Type | Range | Description |
|--------|------|-------|-------------|
| `climate:precipitation` | float | 0+ (mm/year) | Mean annual precipitation estimated from [GPM](https://gpm.nasa.gov/) (Global Precipitation Measurement) satellite data, aggregated as a long-term annual mean. |
| `climate:temperature` | float | ~-40 to 50 (°C) | Mean annual land surface temperature estimated from [MODIS LST](https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1) satellite data, aggregated as a long-term annual mean. |

### Soil (`soil:`)

Surface-layer soil properties from the [OpenLandMap](https://openlandmap.org/) dataset, derived from machine learning predictions on global soil survey data at 250 m resolution.

| Column | Type | Range | Description |
|--------|------|-------|-------------|
| `soil:clay` | float | 0 to 100 (%) | Clay content weight fraction at 0 cm depth. [Source](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02). |
| `soil:sand` | float | 0 to 100 (%) | Sand content weight fraction at 0 cm depth. [Source](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02). |
| `soil:carbon` | float | 0+ (g/kg) | Soil organic carbon content at 0 cm depth. [Source](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02). |
| `soil:bulk_density` | float | 0+ (kg/m³) | Fine-earth bulk density at 0 cm depth. [Source](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02). |
| `soil:ph` | float | ~3 to 10 | Soil pH in water at 0 cm depth. [Source](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02). |

### Socioeconomic (`socio:`)

| Column | Type | Range | Description |
|--------|------|-------|-------------|
| `socio:gdp` | float | 0+ (USD) | GDP per capita at purchasing power parity (PPP, constant 2021 USD) for the year 2022. From the [Kummu et al. (2025)](https://doi.org/10.1038/s41597-025-04487-x) gridded dataset, downscaled to admin-2 level (43,501 units) at 5 arc-min resolution. [GEE catalog](https://gee-community-catalog.org/projects/gridded_gdp_hdi/). |
| `socio:population` | float | 0+ (people) | Estimated number of people per grid cell from the Meta [High Resolution Settlement Layer](https://dataforgood.facebook.com/dfg/tools/high-resolution-population-density-maps) (HRSL). Uses satellite imagery and census data at ~30 m resolution. |
| `socio:human_modification` | float | 0.0 to 1.0 | Cumulative degree of human modification of terrestrial ecosystems from the [Global Human Modification v3](https://doi.org/10.1038/s41597-025-04892-2) (Theobald et al. 2025). Combines the spatial footprint and intensity of 13 stressors across five categories: settlement, agriculture, transportation, mining/energy, and electrical infrastructure. 0 = no modification, 1 = fully modified. 300 m resolution. [GEE catalog](https://gee-community-catalog.org/projects/ghm-v3/). |
| `socio:cisi` | float | 0.0 to 1.0 | [Critical Infrastructure Spatial Index](https://doi.org/10.1038/s41597-022-01218-4) (Nirandjan et al. 2022). Aggregates OpenStreetMap data on 39 types of critical infrastructure across seven systems: transportation, energy, telecommunication, waste, water, education, and health. 0 = no infrastructure, 1 = highest density. 0.10° resolution. [GEE catalog](https://gee-community-catalog.org/projects/cisi/). |

### Administrative (`admin:`)

Human-readable administrative boundary names resolved from rasterized boundary datasets.

| Column | Type | Description |
|--------|------|-------------|
| `admin:country` | string | Country name. Tiles over ocean/lakes are labeled `Ocean/Sea/Lakes`. |
| `admin:state` | string | State or province name. |
| `admin:district` | string | District or county name. |

### Other

| Column | Type | Description |
|--------|------|-------------|
| `geometry` | binary (WKB) | Tile geometry. All files include GeoParquet metadata for spatial queries. |
| `split` | string | ELLIOT split assignment: `monotemporal` or `temporal`. Present in `elliot.parquet` only. |

## Files

| File | Rows | Columns | Size | Description |
|------|-----:|--------:|-----:|-------------|
| `global.parquet` | 5,055,204 | 26 | 146 MB | Every 10 km tile on Earth. The complete grid with all enrichment columns. |
| `land.parquet` | 2,767,104 | 26 | 91 MB | Tiles covered by land-observing sensors (Sentinel-2 and Landsat). Same schema as global. |
| `land_s2.parquet` | 2,547,253 | 34 | 127 MB | Land tiles with a Sentinel-2 image assigned. Adds `stac:` and `s2:` sensor metadata. |
| `land_l8.parquet` | 2,255,537 | 38 | 97 MB | Land tiles with a Landsat 8/9 image assigned. Adds `stac:` and `l8:` sensor metadata. |
| `elliot.parquet` | 279,166 | 27 | 14 MB | ELLIOT subset with monotemporal and temporal split assignments. Same enrichment as global plus `split` column. |

### Namespace availability per file

| Namespace | global | land | land_s2 | land_l8 | elliot |
|-----------|:------:|:----:|:-------:|:-------:|:------:|
| `majortom:` | ✓ | ✓ | | | ✓ |
| `stac:` | | | ✓ | ✓ | |
| `s2:` | | | ✓ | | |
| `l8:` | | | | ✓ | |
| `terrain:` | ✓ | ✓ | ✓ | ✓ | ✓ |
| `climate:` | ✓ | ✓ | ✓ | ✓ | ✓ |
| `soil:` | ✓ | ✓ | ✓ | ✓ | ✓ |
| `socio:` | ✓ | ✓ | ✓ | ✓ | ✓ |
| `admin:` | ✓ | ✓ | ✓ | ✓ | ✓ |
| `split` | | | | | ✓ |
| `geometry` | ✓ | ✓ | ✓ | ✓ | ✓ |

## Quick Start

### DuckDB

```python
import duckdb

duckdb.sql("""
INSTALL spatial;
INSTALL httpfs;
LOAD spatial;
LOAD httpfs;

SELECT "admin:country", COUNT(*) as n_tiles
FROM 'https://data.source.coop/major-tom/index/land_s2.parquet'
WHERE "admin:country" IN ('Peru', 'Brazil', 'Colombia', 'Chile', 'Argentina')
GROUP BY "admin:country"
ORDER BY n_tiles DESC;
""").show()
```

### Pandas

```python
import pandas as pd
import requests
from io import BytesIO

BASE = "https://data.source.coop/major-tom/index"

# Load land tiles with Sentinel-2 metadata
r = requests.get(f"{BASE}/land_s2.parquet")
df = pd.read_parquet(BytesIO(r.content))

# Filter by country
peru = df[df["admin:country"] == "Peru"]
print(f"Peru: {len(peru):,} tiles")

# Get ELLIOT splits
r = requests.get(f"{BASE}/elliot.parquet")
elliot = pd.read_parquet(BytesIO(r.content))
print(elliot["split"].value_counts())
```

## ELLIOT Splits

The `elliot.parquet` file contains 279,166 tiles selected for the [ELLIOT project](https://elliot-ai.eu/) multi-temporal dataset extension. Tile locations were sampled using hierarchical spherical k-means (530 × 528 = 279,840 clusters) over [AlphaEarth Foundation](https://source.coop/asterisklabs/alphaearth) embeddings to ensure global environmental diversity.

The `split` column defines two subsets:

- **Monotemporal** (250,000 tiles). One cloud-free image per sensor per location. Designed for tasks where spatial coverage matters more than temporal depth: land cover classification, feature extraction, or pretraining foundation models on diverse global scenes.

- **Temporal** (29,166 tiles). Multiple observations per location across time. Designed for tasks that require temporal context: change detection, phenology tracking, seasonal compositing, or training models that learn from multi-temporal sequences. This subset is further divided into monthly cadence (12,500 tiles × 12 timesteps) and five-daily cadence (16,666 tiles × 6 timesteps).

## Citation

```bibtex
@inproceedings{Francis2024MajorTOM,
    author    = {Francis, Alistair and Czerkawski, Mikolaj},
    title     = {Major TOM: Expandable Datasets for Earth Observation},
    booktitle = {IGARSS 2024 - IEEE International Geoscience and Remote Sensing Symposium},
    year      = {2024},
    pages     = {2935--2940},
    doi       = {10.1109/IGARSS53475.2024.10640760}
}
```

## License

CC-BY-4.0

## Acknowledgements

The Major TOM Index has been made possible thanks to [Asterisk Labs](https://asterisk.coop), the [ELLIOT](https://elliot-earth.eu) project (European Commission, Horizon Europe, Grant 101214398), and the [Image and Signal Processing Group (ISP)](https://isp.uv.es) at Universitat de València.

providers: role [processor] for 'Asterisk Labs' is an AI guess — Acknowledged as contributor in README; specific processing role not described.. Verify before flipping to _state: reviewed.

providers: role [processor] for 'Image and Signal Processing Group (ISP), Universitat de València' is an AI guess — Acknowledged as contributor in README; specific processing role not described.. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [global.geometry (geometry), global.admin:country (placename), global.admin:state (placename), global.admin:district (placename), global.majortom:mgrs_tile (admin_code), land.geometry (geometry), land.admin:country (placename), land.admin:state (placename), land.admin:district (placename), land.majortom:mgrs_tile (admin_code), land_s2.geometry (geometry), land_s2.admin:country (placename), land_s2.admin:state (placename), land_s2.admin:district (placename), land_l8.geometry (geometry), land_l8.admin:country (placename), land_l8.admin:state (placename), land_l8.admin:district (placename), elliot.geometry (geometry), elliot.admin:country (placename), elliot.admin:state (placename), elliot.admin:district (placename), elliot.majortom:mgrs_tile (admin_code)]. Add bbox once a placename → bbox gazetteer is available.


Landsat8 TOA


Enhanced Vegetation Index

Gap-filled Enhanced Vegetation Index (EVI) raster dataset derived from MODIS MCD43D62, MCD43D63, and MCD43D64 v6.1 surface reflectance products at approximately 1 km and 5 km spatial resolution. EVI values are computed using the Huete et al. (2002) formula, with cloud-cover and missing-data gaps eliminated via the Weiss et al. (2014) interpolation method. Products are distributed as GeoTIFFs across 8-daily, monthly, annual, and synoptic temporal aggregations, each accompanied by multiple spatial and temporal summary statistics.

remote-sensing modis time-series global-coverage earth-observation environmental-monitoring satellite-imagery vegetation-index

Dataset files (9891 x .tif)

Source Cooperative product page

## README.md
The Enhanced Vegetation Index is derived from the MODIS [MCD43D62](https://lpdaac.usgs.gov/products/mcd43d62v061/), [MCD43D63](https://lpdaac.usgs.gov/products/mcd43d63v061/) and [MCD43D64](https://lpdaac.usgs.gov/products/mcd43d64v061/) v6.1 products. EVI is first calculated on the datasets using the formula from [Huete et al (2002)](https://doi.org/10.1016/S0034-4257(02)00096-2) and then the outputs are gap-filled using the approach outlined in [Weiss et al (2014)](https://www.sciencedirect.com/science/article/pii/S0924271614002512?via%3Dihub) to eliminate missing data caused by factors such as cloud cover.

The gap-filled 8-daily ~1km outputs are then aggregated temporally and spatially to produce monthly and annual ~5km products.

Directories are structured in the following way: `EVI_v061/{Resolution}/{TemporalSummary}`

And under that, filenames are structured by:
8-daily: `EVI_v061.{Year}.{JulianDay}.Data.{Resolution}.{SpatialSummary}.tif`
Monthly: `EVI_v061.{Year}.{Month}.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`
Annual: `EVI_v061.{Year}.Annual.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`
Synoptic: `EVI_v061.Synoptic.{Month-or-Overall}.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`

`Resolution` in this dataset is either `1km` or `5km`

Standard values for `TemporalSummary` are: `min`, `max`, `range`, `mean`, `SD`, `count`. In addition there are:
- `Data`: this indicated that no temporal aggregation has occurred
- `Balanced-mean`: This is specific to overall synoptic data, it represents the mean of the 12 monthly synoptic mean datasets as opposed to the overall mean of the (e.g. daily) data. This gives a weighting to help correct for seasonal bias that could otherwise be present in areas where data are more often missing at certain times of year.

Standard values for `SpatialSummary` are: `min`, `max`, `range`, `sum`, `mean`, `SD`, `count`. In addition:
- `Data`: this indicates that no spatial aggregation has occurred


Land Surface Temperature

Land Surface Temperature (LST) derived from MODIS MOD11A2 v6.1 ~1 km 8-daily composites, covering daytime LST, nighttime LST, and diurnal temperature difference. Values are converted to degrees Celsius and gap-filled using the Weiss et al. (2014) method to remove cloud-cover artifacts from factors such as cloud cover. Products are aggregated to ~1 km (8-daily) and ~5 km (monthly and annual) resolutions with global spatial coverage, distributed as cloud-optimized GeoTIFFs.

remote-sensing modis earth-observation climate time-series global-coverage cloud-optimized-geotiff environmental-monitoring

Dataset files (29673 x .tif)

Source Cooperative product page

## README.md
Daytime and Nighttime Land Surface Temperature (LST) are derived from the ~1km MODIS [MOD11A2 v6.1](https://lpdaac.usgs.gov/products/mod11a2v061/) products. The 8-daily composites are converted to degrees Celsius and then gap-filled using the approach outlined in [Weiss et al (2014)](https://doi.org/10.1016/j.isprsjprs.2014.10.001) to eliminate missing data caused by factors such as cloud cover.

The gap-filled 8-daily ~1km outputs are then aggregated temporally and spatially to produce monthly and annual ~5km products.

Directories are structured in the following way: `{VariableName}/{Resolution}/{TemporalSummary}`

And under that, filenames are structured by:
8-daily: `{VariableName}.{Year}.{JulianDay}.Data.{Resolution}.{SpatialSummary}.tif`
Monthly: `{VariableName}.{Year}.{Month}.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`
Annual: `{VariableName}.{Year}.Annual.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`
Synoptic: `{VariableName}.Synoptic.{Month-or-Overall}.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`

`VariableName` options in this dataset are:
- `LST_Day_v061`: daytime Land Surface Temperature
- `LST_Night_v061`: nighttime Land Surface Temperature
- `LST_DiurnalDifference_v061`: difference between daytime and nighttime LSTs

`Resolution` in this dataset is either `1km` or `5km`

Standard values for `TemporalSummary` are: `min`, `max`, `range`, `mean`, `SD`, `count`. In addition there are:
- `Data`: this indicated that no temporal aggregation has occurred
- `Balanced-mean`: This is specific to overall synoptic data, it represents the mean of the 12 monthly synoptic mean datasets as opposed to the overall mean of the (e.g. daily) data. This gives a weighting to help correct for seasonal bias that could otherwise be present in areas where data are more often missing at certain times of year.

Standard values for `SpatialSummary` are: `min`, `max`, `range`, `sum`, `mean`, `SD`, `count`. In addition:
- `Data`: this indicates that no spatial aggregation has occurred


Tasselled Cap Brightness

Global Tasselled Cap Brightness (TCB) index derived from seven MODIS MCD43D v6.1 BRDF/Albedo spectral bands at 1 km and 5 km spatial resolution. TCB is computed using the Lobser & Cohen (2007) spectral transformation formula and gap-filled via the Weiss et al. (2014) method to remove cloud and missing-data artefacts. Composites are available at 8-daily, monthly, annual, and synoptic temporal resolutions with spatial and temporal summary statistics (min, max, mean, SD, range, count).

remote-sensing modis earth-observation satellite-imagery land-cover time-series global-coverage cloud-optimized-geotiff

Dataset files (9891 x .tif)

Source Cooperative product page

## README.md
Tasselled Cap Brightness is derived from the MODIS [MCD43D62](https://lpdaac.usgs.gov/products/mcd43d62v061/), [MCD43D63](https://lpdaac.usgs.gov/products/mcd43d63v061/), [MCD43D64](https://lpdaac.usgs.gov/products/mcd43d64v061/), [MCD43D65](https://lpdaac.usgs.gov/products/mcd43d65v061/), [MCD43D66](https://lpdaac.usgs.gov/products/mcd43d66v061/), [MCD43D67](https://lpdaac.usgs.gov/products/mcd43d67v061/) and [MCD43D68](https://lpdaac.usgs.gov/products/mcd43d68v061/) v6.1 products. TCB is first calculated on the datasets using the formula from [Lobser & Cohen (2007)](https://doi.org/10.1080/01431160701253303) and then the outputs are gap-filled using the approach outlined in [Weiss et al (2014)](https://doi.org/10.1016/j.isprsjprs.2014.10.001) to eliminate missing data caused by factors such as cloud cover.

Directories are structured in the following way: `TCB_v061/{Resolution}/{TemporalSummary}`

And under that, filenames are structured by:
8-daily: `TCB_v061.{Year}.{JulianDay}.Data.{Resolution}.{SpatialSummary}.tif`
Monthly: `TCB_v061.{Year}.{Month}.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`
Annual: `TCB_v061.{Year}.Annual.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`
Synoptic: `TCB_v061.Synoptic.{Month-or-Overall}.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`

`Resolution` in this dataset is either `1km` or `5km`

Standard values for `TemporalSummary` are: `min`, `max`, `range`, `mean`, `SD`, `count`. In addition there are:
- `Data`: this indicated that no temporal aggregation has occurred
- `Balanced-mean`: This is specific to overall synoptic data, it represents the mean of the 12 monthly synoptic mean datasets as opposed to the overall mean of the (e.g. daily) data. This gives a weighting to help correct for seasonal bias that could otherwise be present in areas where data are more often missing at certain times of year.

Standard values for `SpatialSummary` are: `min`, `max`, `range`, `sum`, `mean`, `SD`, `count`. In addition:
- `Data`: this indicates that no spatial aggregation has occurred


Tasselled Cap Wetness

Tasselled Cap Wetness (TCW) is a global raster dataset derived from MODIS MCD43D62–MCD43D68 v6.1 surface reflectance products at 1 km and 5 km spatial resolution. TCW values are computed using the Lobser & Cohen (2007) spectral transformation and gap-filled via Weiss et al. (2014) to remove cloud and missing-data artefacts. Products span 8-daily, monthly, annual, and synoptic temporal intervals with multiple statistical summaries.

remote-sensing modis earth-observation global-coverage time-series satellite-imagery hydrology land-cover

Dataset files (9891 x .tif)

Source Cooperative product page

## README.md
Tasselled Cap Wetness is derived from the MODIS [MCD43D62](https://lpdaac.usgs.gov/products/mcd43d62v061/), [MCD43D63](https://lpdaac.usgs.gov/products/mcd43d63v061/), [MCD43D64](https://lpdaac.usgs.gov/products/mcd43d64v061/), [MCD43D65](https://lpdaac.usgs.gov/products/mcd43d65v061/), [MCD43D66](https://lpdaac.usgs.gov/products/mcd43d66v061/), [MCD43D67](https://lpdaac.usgs.gov/products/mcd43d67v061/) and [MCD43D68](https://lpdaac.usgs.gov/products/mcd43d68v061/) v6.1 products. TCW is first calculated on the datasets using the formula from [Lobser & Cohen (2007)](https://doi.org/10.1080/01431160701253303) and then the outputs are gap-filled using the approach outlined in [Weiss et al (2014)](https://doi.org/10.1016/j.isprsjprs.2014.10.001) to eliminate missing data caused by factors such as cloud cover.

Directories are structured in the following way: `TCW_v061/{Resolution}/{TemporalSummary}`

And under that, filenames are structured by:
8-daily: `TCW_v061.{Year}.{JulianDay}.Data.{Resolution}.{SpatialSummary}.tif`
Monthly: `TCW_v061.{Year}.{Month}.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`
Annual: `TCW_v061.{Year}.Annual.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`
Synoptic: `TCW_v061.Synoptic.{Month-or-Overall}.{TemporalSummary}.{Resolution}.{SpatialSummary}.tif`

`Resolution` in this dataset is either `1km` or `5km`

Standard values for `TemporalSummary` are: `min`, `max`, `range`, `mean`, `SD`, `count`. In addition there are:
- `Data`: this indicated that no temporal aggregation has occurred
- `Balanced-mean`: This is specific to overall synoptic data, it represents the mean of the 12 monthly synoptic mean datasets as opposed to the overall mean of the (e.g. daily) data. This gives a weighting to help correct for seasonal bias that could otherwise be present in areas where data are more often missing at certain times of year.

Standard values for `SpatialSummary` are: `min`, `max`, `range`, `sum`, `mean`, `SD`, `count`. In addition:
- `Data`: this indicates that no spatial aggregation has occurred


Mapterhorn

Mapterhorn is a global collection of terrain tiles in PMTiles format covering the full Web Mercator projection extent. Elevation data is encoded in Terrarium format, enabling hillshading and 3D terrain rendering in interactive web maps. Source elevation data is compiled from multiple open-data providers.

digital-elevation-model elevation topography pmtiles global-coverage remote-sensing hillshade web-mapping

Dataset files (458 x .pmtiles)

Source Cooperative product page

## README.md
# Mapterhorn

Public terrain tiles for interactive web map visualizations

https://mapterhorn.com/

## Bulk Download

For a full list of PMTiles download URLs in JSON format see https://s3.us-west-2.amazonaws.com/us-west-2.opendata.source.coop/mapterhorn/mapterhorn/download_urls.json.

## License and Attribution

Mapterhorn uses the open-data sources listed on https://mapterhorn.com/attribution

For a full list in JSON format see https://s3.us-west-2.amazonaws.com/us-west-2.opendata.source.coop/mapterhorn/mapterhorn/attribution.json.

## Example: MapLibre GL JS

Live demo: https://output.jsbin.com/qamarorima/

```html
<!DOCTYPE html>
<html lang='en'>

<head>
    <meta charset="utf-8" />
    <script src="https://unpkg.com/maplibre-gl@5.6.0/dist/maplibre-gl.js"></script>
    <link href="https://unpkg.com/maplibre-gl@5.6.0/dist/maplibre-gl.css" rel="stylesheet" />
    <script src="https://unpkg.com/pmtiles@4.3.0/dist/pmtiles.js"></script>
    <meta name="viewport" content="initial-scale=1,maximum-scale=1,user-scalable=no" />
    <style>
        body {
            margin: 0;
            padding: 0;
        }

        #map {
            position: absolute;
            top: 0;
            bottom: 0;
            width: 100%;
        }
    </style>
</head>

<body>
    <div id="map"></div>
    <script type="text/javascript">

        const protocol = new pmtiles.Protocol({ metadata: true, errorOnMissingTile: true });
        maplibregl.addProtocol('mapterhorn', async (params, abortController) => {
            const [z, x, y] = params.url.replace('mapterhorn://', '').split('/').map(Number);
            const name = z <= 12 ? 'planet' : `6-${x >> (z - 6)}-${y >> (z - 6)}`;
            const url = `pmtiles://https://s3.us-west-2.amazonaws.com/us-west-2.opendata.source.coop/mapterhorn/mapterhorn/${name}.pmtiles/${z}/${x}/${y}.webp`;
            const response = await protocol.tile({ ...params, url }, abortController);
            if (response['data'] === null) throw new Error(`Tile z=${z} x=${x} y=${y} not found.`);
            return response;
        });

        const map = new maplibregl.Map({
            container: 'map',
            hash: 'map',
            style: {
                version: 8,
                sources: {
                    hillshadeSource: {
                        type: 'raster-dem',
                        tiles: ['mapterhorn://{z}/{x}/{y}'],
                        encoding: 'terrarium',
                        tileSize: 512,
                        attribution: '<a href="https://mapterhorn.com/attribution">© Mapterhorn</a>'
                    },
                },
                layers: [
                    {
                        id: 'hillshade',
                        type: 'hillshade',
                        source: 'hillshadeSource',
                    },
                ],
            },
        });
    </script>
</body>

</html>
```


Maxar Open Data Program (ARD)

Pre- and post-event high-resolution satellite imagery from Maxar's Open Data Program, covering global disaster events captured between 2010 and 2023. Imagery is generated using the Maxar ARD pipeline and tiled on an organized grid in analysis-ready, cloud-optimized formats. This repository mirrors the STAC catalog as GeoParquet for evaluation.

satellite-imagery remote-sensing disaster-response emergency-management damage-assessment ard

maxar-opendata.parquet

[geometry] Footprint of each satellite image tile in EPSG:4326

links

STRUCT(href VARCHAR, rel VARCHAR, "type" VARCHAR)[]

assets

STRUCT("data-mask" STRUCT(href VARCHAR, roles VARCHAR[], title VARCHAR, "type" VARCHAR), ms_analytic STRUCT("eo:bands" STRUCT(common_name VARCHAR, description VARCHAR, "name" VARCHAR)[], href VARCHAR, "proj:bbox" DOUBLE[], "proj:shape" BIGINT[], "proj:transform" DOUBLE[], roles VARCHAR[], title VARCHAR, "type" VARCHAR), pan_analytic STRUCT("eo:bands" STRUCT(description VARCHAR, "name" VARCHAR)[], href VARCHAR, "proj:bbox" DOUBLE[], "proj:shape" BIGINT[], "proj:transform" DOUBLE[], roles VARCHAR[], title VARCHAR, "type" VARCHAR), visual STRUCT("eo:bands" STRUCT(common_name VARCHAR, description VARCHAR, "name" VARCHAR)[], href VARCHAR, "proj:bbox" DOUBLE[], "proj:shape" BIGINT[], "proj:transform" DOUBLE[], roles VARCHAR[], title VARCHAR, "type" VARCHAR))

quadkey

[spatial_index] Quadkey tile identifier for the ARD tiling grid

Source Cooperative product page

## README.md
# Maxar Open Data Program Images (ARD Format)

Pre and post event high-resolution satellite imagery in support of emergency planning, risk assessment, monitoring of staging areas and emergency response, damage assessment, and recovery. These images are generated using the Maxar ARD pipeline, tiled on an organized grid in analysis-ready cloud-optimized formats.

Data is currently stored in the Amazon S3 bucket `arn:aws:s3:::maxar-opendata` in `us-east-1`.

A copy of the STAC catalog in GeoParquet format is mirrored here for evaluation.

See also:

https://registry.opendata.aws/maxar-open-data/
https://www.maxar.com/open-data

providers: role [host] for 'Amazon Web Services' is an AI guess — Hosts the primary maxar-opendata S3 bucket; role inferred from registry.opendata.aws listing. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [maxar-opendata.geometry (geometry), maxar-opendata.quadkey (spatial_index)]. Add bbox once a placename → bbox gazetteer is available.


Testing Diligence

A simple test repo to test downstream workflows. Testing [links](https://www.planet.com/data/stac/browser/?.language=en).


Krill super-swarm datasets

These are raw and processed fisheries acoustics data of observations of a super-swarm of Antarctic krill collected in the East Antarctic (63°S, 79.7°E). There are also whale sightings data and coordinates of the super-swarm boundary.


Mike test  again

unknown

Dataset files (1 x .tif)

Source Cooperative product page


new product

A small raster dataset in GeoTIFF format covering a southern-hemisphere extent (approximately 39°S to 90°S, global longitude). No README is available; content and subject matter are unknown.

Dataset files (2 x .tif)

Source Cooperative product page


testing-3

testing


test upload

practicing uploading data to source.


World Settlement Footprint


Unsupervised People's Speech

The People’s Speech Dataset is among the world’s largest English speech recognition corpus today that is licensed for academic and commercial usage under CC-BY-SA and CC-BY 4.0. It includes 30,000+ hours of transcribed speech in English languages with a diverse set of speakers. This open dataset is large enough to train speech-to-text systems and crucially is available with a permissive license.


Archive of Many Outdoor Scenes (AMOSv1)

A dataset containing hundreds of millions of images gathered from publicly available outdoor webcams.


FTW Foundation Model Features

Features extracted from the FTW dataset using Geospatial Foundation Models.


aq-icechunk-store