Imagery

This repository contains a description of the dataset and how to use it. It also contains example code to get a working segmentation model up and running quickly using a small sample dataset.

This repository provides code for training and evaluating a convolutional neural network (CNN) to detect tree in urban environments with aerial imagery.

This workshop presents and exemplifies a subset of GRASS GIS toolsets for satellite imagery data processing and analysis in combination with other core modules and add-ons.

CoastSat is an open-source software toolkit that enables users to obtain time-series of shoreline position at any coastline worldwide from more than 30 years of publicly available satellite imagery.

Here are three deep learning models for predicting future satellite images from past ones using features such as precipitation and elevation maps.

Landsat has been collecting information about Earth since 1972. The USGS Landsat archive now spans 50 years with over 10.3 million scenes. Learn more about downloading Landsat data here.

The WorldStrat dataset includes nearly 10,000 km² of free high-resolution satellite imagery of unique locations: from agriculture to ice caps, from forests to multiple urbanization densities.

Here is a huge list of resources for performing deep learning on satellite and aerial imagery. The resources are updated regularly and could benefit both the research and developer communities.

This project maps tree extent at the ten-meter scale using open source artificial intelligence and satellite imagery. Learn more here.

This website provides a comprehensive and interactive catalog of reference benchmark datasets. Check it out here.