Radiant MLHub
agriculture
arc
attribution benchmark
building footprints
cloud
crop type
drone
era5
flood detection
goes
image classification
land cover
landsat 8
live fuel moisture
marine debris
maxar
naip
nlcd
object detection
off-nadir
perspective images
planetscope
regression
road network
sar
segmentation
sentinel-1
sentinel-2
synthetic data
tamsat
tropical storm
wildfire
worldview-2
worldview-3
xai

2019 Mali CropType Training Data

This dataset produced by the NASA Harvest team includes crop types labels from ground referencing matched with time-series of Sentinel-2 imagery during the growing season. Ground reference data are collected using an ODK app. Crop types include Maize, Millet, Rice and Sorghum. Labels are vectorized over the Sentinel-2 read more...
agriculture
crop type
segmentation
sentinel-2

A crop type dataset for consistent land cover classification in Central Asia

Land cover is a key variable in the context of climate change. In particular, crop type information is essential to understand the spatial distribution of water usage and anticipate the risk of water scarcity and the consequent danger of food insecurity. This applies to arid regions such as the read more...
agriculture
crop type
segmentation

A Fusion Dataset for Crop Type Classification in Germany

This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Brandenburg, Germany. There are nine crop types in this dataset from years 2018 and 2019: Wheat, Rye, Barley, Oats, Corn, Oil Seeds, Root Crops, read more...
agriculture
crop type
planetscope
sar
segmentation
sentinel-1
sentinel-2

A Fusion Dataset for Crop Type Classification in Western Cape, South Africa

This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Western Cape, South Africa. There are five crop types from the year 2017: Wheat, Barely, Canola, Lucerne/Medics, Small grain grazing. The AOI is split read more...
agriculture
crop type
planetscope
sar
segmentation
sentinel-1
sentinel-2

AgriFieldNet Competition Dataset

This dataset contains crop types of agricultural fields in four states of Uttar Pradesh, Rajasthan, Odisha and Bihar in northern India. There are 13 different classes in the dataset including Fallow land and 12 crop types of Wheat, Mustard, Lentil, Green pea, Sugarcane, Garlic, Maize, Gram, Coriander, Potato, Bersem, read more...
agriculture
crop type
segmentation
sentinel-2

Chesapeake Land Cover

This dataset contains high-resolution aerial imagery from the USDA NAIP program, high-resolution land cover labels from the Chesapeake Conservancy, low-resolution land cover labels from the USGS NLCD 2011 dataset, low-resolution multi-spectral imagery from Landsat 8, and high-resolution building footprint masks from Microsoft Bing, formatted to accelerate machine learning research read more...
building footprints
land cover
landsat 8
naip
nlcd
segmentation

Cloud to Street - Microsoft flood dataset

The C2S-MS Floods Dataset is a dataset of global flood events with labeled Sentinel-1 & Sentinel-2 pairs. There are 900 sets (1800 total) of near-coincident Sentinel-1 and Sentinel-2 chips (512 x 512 pixels) from 18 global flood events. Each chip contains a water label for both Sentinel-1 and Sentinel-2, read more...
cloud
flood detection
sar
segmentation
sentinel-1
sentinel-2

CV4A Kenya Crop Type Competition

This dataset was produced as part of the Crop Type Detection competition at the Computer Vision for Agriculture (CV4A) Workshop at the ICLR 2020 conference. The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing read more...
agriculture
crop type
segmentation
sentinel-2

Dalberg Data Insights Crop Type Uganda

This dataset contains crop types and field boundaries along with other metadata collected in a campaign run by Dalberg Data Insights in the end of September 2017, as close as possible to the harvest period of 2017. GeoODKapps were used to collect approximately four points per field to get read more...
agriculture
crop type
segmentation
sentinel-2

Great African Food Company Crop Type Tanzania

This dataset contains field boundaries and crop types from farms in Tanzania. Great African Food Company used Farmforce app to collect a point within each field, and recorded other properties including area of the field. Radiant Earth Foundation team used the point measurements from the ground data collection and read more...
agriculture
crop type
segmentation
sentinel-2

LandCoverNet Africa

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Africa contains data across Africa, which accounts for ~1/5 of the global dataset. Each pixel is identified as one of the seven land read more...
land cover
landsat 8
segmentation
sentinel-1
sentinel-2

LandCoverNet Asia

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Asia contains data across Asia, which accounts for ~31% of the global dataset. Each pixel is identified as one of the seven land read more...
land cover
landsat 8
segmentation
sentinel-1
sentinel-2

LandCoverNet Australia

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Australia contains data across Australia, which accounts for ~7% of the global dataset. Each pixel is identified as one of the seven land read more...
land cover
landsat 8
segmentation
sentinel-1
sentinel-2

LandCoverNet Europe

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Europe contains data across Europe, which accounts for ~9.5% of the global dataset. Each pixel is identified as one of the seven land read more...
land cover
landsat 8
segmentation
sentinel-1
sentinel-2

LandCoverNet North America

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet North America contains data across North America, which accounts for ~13% of the global dataset. Each pixel is identified as one of the read more...
land cover
landsat 8
segmentation
sentinel-1
sentinel-2

LandCoverNet South America

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet South America contains data across South America, which accounts for ~13% of the global dataset. Each pixel is identified as one of the read more...
land cover
landsat 8
segmentation
sentinel-1
sentinel-2

Marine Debris Archive (MARIDA)

Marine Debris Archive (MARIDA) is a marine debris-oriented dataset on Sentinel-2 satellite images. It also includes various sea features (clear & turbid water, waves, etc.) and floating materials (Sargassum macroalgae, ships, natural organic material, etc) that co-exist. MARIDA is primarily focused on the weakly supervised pixel-level semantic segmentation read more...
marine debris
segmentation
sentinel-2

Marine Debris Dataset for Object Detection in Planetscope Imagery

Floating marine debris is a global pollution problem which leads to the loss of marine and terrestrial biodiversity. Large swaths of marine debris are also navigational hazards to ocean vessels. The use of Earth observation data and artificial intelligence techniques can revolutionize the detection of floating marine debris read more...
marine debris
planetscope
segmentation

NASA Flood Extent Detection

This dataset contains synthetic aperture radar (SAR) raster imagery for various flood events acquired from the European Space Agencys Sentinel-1A and Sentinel-1B missions, providing C-Band dual-polarized imagery that spans geographical areas of interest in the United States and Bangladesh. The main emphasis was on the labeling of open water read more...
flood detection
sar
segmentation
sentinel-1

Open Cities AI Challenge Dataset

This dataset was developed as part of a challenge to segment building footprints from aerial imagery. The goal of the challenge was to accelerate the development of more accurate, relevant, and usable open-source AI models to support mapping for disaster risk management in African cities [Read more about the read more...
building footprints
segmentation

PlantVillage Crop Type Kenya

This dataset contains field boundaries and crop type information for fields in Kenya. PlantVillage app is used to collect multiple points around each field and collectors have access to basemap imagery in the app during data collection. They use the basemap as a guide in collecting and verifying the read more...
agriculture
crop type
segmentation
sentinel-2

ramp Building Footprint Training Dataset - Accra, Ghana

This chipped training dataset is over Accra and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
object detection
segmentation

ramp Building Footprint Training Dataset - Barishal, Bangladesh

This chipped training dataset is over Barishal and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Bentiu, South Sudan

This chipped training dataset is over Bentiu and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Chittagong, Bangladesh

This chipped training dataset is over Chittagong and parts of the Kutupalong Refugee Camp and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Cox's Bazar, Bangladesh

This chipped training dataset is over Cox's Bazaar and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Dar es Salaam, Tanzania

This chipped training dataset is over Dar es Salaam and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was read more...
building footprints
object detection
segmentation

ramp Building Footprint Training Dataset - Dhaka, Bangladesh

This chipped training dataset is over Dhaka and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Hpa-an, Myanmar

This chipped training dataset is over Hpa-an and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Jashore, Bangladesh

This chipped training dataset is over Jashore and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Karnataka, India

This chipped training dataset is over Karnataka and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Les Cayes, Haiti

This chipped training dataset is over Les Cayes and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Lubumbashi, Democratic Republic of the Congo

This chipped training dataset is over Lubumbashi and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 2 dataset, meaning it has NOT been thoroughly reviewed and improved. This dataset was produced read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Manjama, Sierra Leone

This chipped training dataset is over Manjama and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Mesopotamia, St. Vincent

This chipped training dataset is over Mesopotamia and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Muscat, Oman

This chipped training dataset is over Muscat and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Mzuzu, Malawi

This chipped training dataset is over Mzuzu and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Nairobi, Kenya

This chipped training dataset is over Nairobi and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 2 dataset, meaning it has NOT been thoroughly reviewed and improved. This dataset was produced read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - N'Djamena, Chad

This chipped training dataset is over N'Djamena and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 2 dataset, meaning it has NOT been thoroughly reviewed and improved. This dataset was produced read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Paris, France

This chipped training dataset is over Paris and includes 30cm high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 or smaller pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset read more...
building footprints
object detection
segmentation
worldview-3

ramp Building Footprint Training Dataset - Shanghai, China

This chipped training dataset is over Shanghai and includes 30cm high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 or smaller pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset read more...
building footprints
object detection
segmentation
worldview-3

ramp Building Footprint Training Dataset - Sylhet, Bangladesh

This chipped training dataset is over Sylhet and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 2 dataset, meaning it has NOT been thoroughly reviewed and improved. This dataset was produced read more...
building footprints
maxar
object detection
segmentation

ramp Building Footprint Training Dataset - Wa, Ghana

This chipped training dataset is over Wa and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in read more...
building footprints
maxar
object detection
segmentation

Semantic Segmentation of Crop Type in Ghana

Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and read more...
agriculture
crop type
planetscope
sar
segmentation
sentinel-1
sentinel-2

Semantic Segmentation of Crop Type in South Sudan

Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and read more...
agriculture
crop type
planetscope
sar
segmentation
sentinel-1
sentinel-2

SEN12TS: A SAR and Multispectral Dataset for Land Cover Classification

The SEN12TS dataset contains Sentinel-1, Sentinel-2, and labeled land cover image triplets over six agro-ecologically diverse areas of interest: California, Iowa, Catalonia, Ethiopia, Uganda, and Sumatra. Using the Descartes Labs geospatial analytics platform, 246,400 triplets are produced at 10m resolution over 31,398 256-by-256-pixel unique spatial tiles for a total read more...
land cover
segmentation
sentinel-1
sentinel-2

Sentinel-2 Cloud Cover Segmentation Dataset

In many uses of multispectral satellite imagery, clouds obscure what we really care about - for example, tracking wildfires, mapping deforestation, or monitoring crop health. Being able to more accurately remove clouds from satellite images filters out interference, unlocking the potential of a vast range of use cases. With read more...
cloud
segmentation
sentinel-2

Smallholder Cashew Plantations in Benin

This dataset contains labels for cashew plantations in a 120 km^2 area in the center of Benin. Each pixel is classified for Well-managed plantation, Poorly-managed plantation, No plantation and other classes. The labels are generated using a combination of ground data collection with a handheld GPS device, and final read more...
agriculture
crop type
segmentation
sentinel-2

South Africa Crop Type Competition

This dataset was produced as part of the Radiant Earth Spot the Crop Challenge. The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 and Sentinel-1 read more...
agriculture
crop type
segmentation
sentinel-1
sentinel-2

SpaceNet 1

The commercialization of the geospatial industry has led to an explosive amount of data being collected to characterize our changing planet. One area for innovation is the application of computer vision and deep learning to extract information from satellite imagery at scale. CosmiQ Works, Radiant Solutions and NVIDIA have read more...
building footprints
segmentation
worldview-3

SpaceNet 2

The commercialization of the geospatial industry has led to an explosive amount of data being collected to characterize our changing planet. One area for innovation is the application of computer vision and deep learning to extract information from satellite imagery at scale. CosmiQ Works, Radiant Solutions and NVIDIA have read more...
building footprints
segmentation
worldview-3

SpaceNet 3

The commercialization of the geospatial industry has led to an explosive amount of data being collected to characterize our changing planet. One area for innovation is the application of computer vision and deep learning to extract information from satellite imagery at scale. CosmiQ Works, Radiant Solutions and NVIDIA have read more...
road network
segmentation
worldview-3

SpaceNet 4

The commercialization of the geospatial industry has led to an explosive amount of data being collected to characterize our changing planet. One area for innovation is the application of computer vision and deep learning to extract information from satellite imagery at scale. CosmiQ Works, Radiant Solutions and NVIDIA have read more...
building footprints
off-nadir
segmentation
worldview-3

SpaceNet 5

Determining optimal routing paths in near real-time is at the heart of many humanitarian, civil, military, and commercial challenges. This statement is as true today as it was two years ago when the SpaceNet Partners announced the SpaceNet Challenge 3 focused on road network detection and routing. In a read more...
road network
segmentation
worldview-3

SpaceNet 6

Synthetic Aperture Radar (SAR) is a unique form of radar that can penetrate clouds, collect during all- weather conditions, and capture data day and night. Overhead collects from SAR satellites could be particularly valuable in the quest to aid disaster response in instances where weather and cloud cover can read more...
building footprints
off-nadir
sar
segmentation
worldview-2

SpaceNet 7

The SpaceNet 7 Multi-Temporal Urban Development Challenge aims to help address this deficit and develop novel computer vision methods for non-video time series data. In this challenge, participants will identify and track buildings in satellite imagery time series collected over rapidly urbanizing areas. The competition centers around a new read more...
building footprints
planetscope
segmentation

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