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

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

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 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

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

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