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

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

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

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 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 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