Geospatial Machine Learning
Combining geospatial data with machine learning to gather usable insights
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Turkana camp roof mapping
April 2024
This project automates the identification of buildings and solar panels in aerial imagery, aiding humanitarian mapping. In many developing regions, maps are often outdated or missing, hindering development planning and disaster response. Our work accelerates the creation of up-to-date maps,…
Damage Assessment Visualizer
November 2022
The Damage Assessment Visualizer leverages satellite imagery from a disaster region to visualize conditions of building and structures before and after a disaster. It includes a visual layer on top of the satellite images which predicts the extent of damage…
Satellite Imagery Labeling Tool
November 2022
This is a lightweight web-interface for creating and sharing vector annotations over satellite/aerial imagery scenes.
Poultry Barn Mapping
January 2022
A repository for training models from high-resolution aerial imagery and a dataset of predicted poultry barns across the United States.
Solar Farms Mapping
January 2022
The Solar Farms Mapping release is an artificial intelligence dataset for solar energy locations in India – a spatially explicit machine learning model to map utility-scale solar projects across India using freely available satellite imagery.
Building Damage Assessment CNN Siamese
December 2021
A Microsoft AI for Humanitarian Action study in collaboration with the NLRC 510 global initiative. In this study, we leverage high-resolution satellite imagery to conduct building footprint segmentation and train a classifier to assign each building’s damage severity level via…
Temporal Cluster Matching
December 2021
An implementation of the temporal cluster matching method for detecting change in structure footprints from time series of remotely sensed imagery.
TorchGeo
December 2021
TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data. The goal of this library is to make it simple: for machine learning experts to use geospatial data in their workflows, and…
Land Cover Orinoquia
September 2020
This AI for Earth project, in collaboration with the Wildlife Conservation Society Colombia (WCS Colombia) was developed to create up-to-date land cover maps of the Orinoquía region in Colombia. We used a land use and land cover (LULC) map that…