Radiant MLHub — Datasets

agriculture

arc

attribution benchmark

building footprints

cloud

crop type

drone

era5

field boundary

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...

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...

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...

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...

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...

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...

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...

Eyes on the Ground Image Data

The 'Eyes on the Ground' project (lacunafund.org) 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 read more...

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...

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...

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...

Semantic Segmentation of Crop Type in South Sudan

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...

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...

MLHUB

Contribute a Dataset / Model

support@radiant.earth

RADIANT EARTH

About

Blog

GitHub @radiantearth

LEGAL

End User License Agreement

CONNECT