Harnessing AI to help solve some of the world’s greatest challenges
TorchGeo is a PyTorch (opens in new tab) domain library, similar to torchvision (opens in new tab), 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…
AIDE: Annotation Interface for Data-driven Ecology – Tools for detecting wildlife in aerial images using active learning
We are publishing datasets we developed as part of our efforts with Microsoft’s Airband Initiative to help close the rural broadband gap. The data can be used for the purpose of analyzing, understanding, improving, or addressing problems related to broadband…
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 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…
Part of the AI for Earth Land Cover Mapping project, this repository holds both the frontend web-application and backend server that make up our “Land Cover Mapping” tool.
Using machine learning to detect beluga whale calls in hydrophone recordings. Of the five populations of beluga whales in Alaska, the Cook Inlet population is the smallest and has declined by about seventy-five percent since 1979. Listed as an endangered…
A deep learning project in cooperation with the NOAA Marine Mammal Lab to detect & classify arctic seals in aerial imagery to understand how they’re adapting to a changing world.
