microsoft / arcticseals

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.
MIT License
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aiforearth

Overview

This is the workspace for the a collaboration between Microsoft AI for Earth, NOAA Fisheries, and the University of Washington, aimed at automating the detection of arctic wildlife in aerial imagery.

Imagery

Imagery is now available publicly on lila.science, an open data repository for labeled images related to conservation biology.

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Labels

The data directory contains the following label/metadata files:

Each record in the CSV files refers to a hotspot that the NOAA thermal detection system picked up and that was classified by a human into either "Animal" (true positive) or "Anomaly" (false positive). Each hotspot is unique (no duplicates). The column schema is as follows:

Raw Hotspot Data

In the data directory there is also a raw.csv (14,910 records) containing all hotspot detections from the NOAA 2016 survey flights (includes more seals but also more types of animals, more anomalies, hotspots marked as duplicates, etc.).

Code

The project is meant to accomodate many different approaches, frameworks, languages, etc. Linux is the primary supported dev environment, though some GUI tools are Windows-only.

Organization

Team members are welcome to add whatever code you like to this repo, but please follow these guidelines:

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.