Radiant MLHub
satellite
agriculture
building footprints
crop type
drone
flood detection
goes
image classification
land cover
landsat 8
live fuel moisture
marine debris
naip
nlcd
off-nadir
planetscope
regression
road network
sar
segmentation
sentinel-1
sentinel-2
tropical storm
wildfire
worldview-2
worldview-3

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

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

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

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

BigEarthNet

BigEarthNet
BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2 image patches. To construct BigEarthNet, 125 Sentinel-2 tiles acquired between June 2017 and May 2018 over the 10 countries (Austria, Belgium, Finland, Ireland, Kosovo, Lithuania, Luxembourg, Portugal, Serbia, Switzerland) of Europe were initially selected. All the tiles read more...
image classification
land cover
sentinel-2

Chesapeake Land Cover

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

CV4A Kenya Crop Type Competition

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

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

Drone Imagery Classification Training Dataset for Crop Types in Rwanda

Drone Imagery Classification Training Dataset for Crop Types in Rwanda
RTI International (RTI) generated 2,611 labeled point locations representing 19 different land cover types, clustered in 5 distinct agroecological zones within Rwanda. These land cover types were reduced to three crop types (Banana, Maize, and Legume), two additional non-crop land cover types (Forest and Structure), and a catch-all Other read more...
agriculture
crop type
drone
image classification

Great African Food Company Crop Type Tanzania

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

LandCoverNet
LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-2 mission in 2018. Version 1.0 of the dataset 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
segmentation
sentinel-2

Marine Debris Dataset for Object Detection in Planetscope Imagery

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

Open Cities AI Challenge Dataset

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

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

Semantic Segmentation of Crop Type in Ghana

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

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

SEN12-FLOOD : A SAR and Multispectral Dataset for Flood Detection

SEN12-FLOOD : A SAR and Multispectral Dataset for Flood Detection
These last decades, Earth Observation brought quantities of new perspectives from geosciences to human activity monitoring. As more data became available, artificial intelligence techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for read more...
flood detection
image classification
sar
sentinel-1
sentinel-2

Smallholder Cashew Plantations in Benin

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

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

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

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

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

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

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

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

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

Tropical Cyclone Wind Estimation Competition

Tropical Cyclone Wind Estimation Competition
A collection of tropical storms in the Atlantic and East Pacific Oceans from 2000 to 2019 with corresponding maximum sustained surface wind speed. This dataset is split into training and test categories for the purpose of a competition [Read more about the read more...
goes
regression
tropical storm

Western USA Live Fuel Moisture

Western USA Live Fuel Moisture
This data contains manually collected live fuel moisture measurements in the western United States and remotely-sensed variables. Live fuel moisture represents the mass of water in live vegetation elements like leaves, needles, and twigs divided by its oven-dried mass. It is represented in percentages. Higher the live fuel moisture, read more...
landsat 8
live fuel moisture
sar
sentinel-1
wildfire