Advance sustainability
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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 In many developing regions, maps are often outdated or missing, hindering development planning and disaster response.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,…
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.
Landcover Orinoquia
December 2021
AI for Earth project, in collaboration with the Wildlife Conservation Society Colombia (WCS Colombia) 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 was manually produced using…
Bird Acoustics RCNN
December 2021
A deep learning approach towards the large-scale prediction and analysis of bird acoustics from 100 different bird species
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.
Aerial Wildlife Detection
November 2021
AIDE: Annotation Interface for Data-driven Ecology – Tools for detecting wildlife in aerial images using active learning
Pytorch-wildlife
September 2020
At the core of our mission is the desire to create a harmonious space where conservation scientists from all over the globe can unite. Where they’re able to share, grow, use datasets and deep learning architectures for wildlife conservation. We’ve…
Multi-species Bioacoustic Classification
September 2020
Multi-species bioacoustic classification using deep learning algorithms. With audio recordings collected from rainforests in Puerto Rico, we build a deep learning model that combines transfer learning and pseudo-labeling as a data augmentation technique to: 1) train a deep convolutional neural…