22 datasets

NASA Earthdata CMR2023 · collection

This is a synthetic dataset that can be used by users that are interested in benchmarking methods of explainable artificial intelligence (XAI) for geoscientific applications. The dataset is specifically inspired from a climate forecasting setting (seasonal timescales) where the task is to predict regional climate variability given global climate information lagged in time. The dataset consists of

NASA Earthdata CMR2023 · collection

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 a severe lack of training data.

NASA Earthdata CMR2023 · collection

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, as well as a cloud/cloud shadow mask for Sentinel-2. The dataset was constructed by Cloud to S

NASA Earthdata CMR2023 · collection

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 cover classes based on its annual time series. These classes are w

NASA Earthdata CMR2023 · collection

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. The dataset includes rasterized labeled field boundaries and time series satellite imagery from Planet's NICFI program. Plane

NASA Earthdata CMR2023 · collection

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 cover classes based on its annual time series. These classes

NASA Earthdata CMR2023 · collection

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 seven land cover classes based on its annual time series. These

NASA Earthdata CMR2023 · collection

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 size of 1.69 TB. The image tr

NASA Earthdata CMR2023 · collection

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 a severe lack of training data.

NASA Earthdata CMR2023 · collection

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 grid, and provided as raster files. Funding for this dataset is provided by Luth

NASA Earthdata CMR2023 · collection

This dataset was produced as part of the [Radiant Earth Spot the Crop Challenge](https://zindi.africa/hackathons/radiant-earth-spot-the-crop-hackathon). 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 satellites.

NASA Earthdata CMR2023 · collection

This dataset was produced as part of the [Crop Type Detection competition](https://zindi.africa/competitions/iclr-workshop-challenge-2-radiant-earth-computer-vision-for-crop-recognition) at the [Computer Vision for Agriculture (CV4A) Workshop](https://www.cv4gc.org/cv4a2020/) at the ICLR 2020 conference. The objective of the competition was to create a machine learning model to classify fields by

NASA Earthdata CMR2023 · collection

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 cover classes based on its annual time series. These classes are

NASA Earthdata CMR2023 · collection

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 cover classes based on its annual time series. These classes are water

NASA Earthdata CMR2023 · collection

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 into land cover

NASA Earthdata CMR2023 · collection

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 this goal in mind, this training dataset was generated as part of [crowdso

NASA Earthdata CMR2023 · collection

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 task.

NASA Earthdata CMR2023 · collection

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, and Rice. The dataset is split to train and test collections as part of the AgriF

NASA Earthdata CMR2023 · collection

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 areas where specular reflection of the radar signal off o

NASA Earthdata CMR2023 · collection

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, wetter the vegetation elements, and vice versa. Live fuel moistur

NASA Earthdata CMR2023 · collection

The 'Eyes on the Ground' project ([lacunafund.org](https://lacunafund.org/ag2020awards/)) is a collaboration between ACRE Africa, the International Food Policy Research Institute (IFPRI), and the Lacuna Fund, to create a large machine learning (ML) dataset of smallholder farmer's fields based upon previous work within the Picture Based Insurance framework (Ceballos, Kramer and Robles, 2019, [https

NASA Earthdata CMR2023 · collection

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 seven land cover classes based on its annual time series. These