Examples of using the Planetary Computer

Notebooks and documentation for AI-for-Earth-managed datasets on Azure

Tools for detecting wildlife in aerial images using active learning

Land Cover Mapping

.NET Standard, high performance, zero allocation AIS decoder, which can process millions of AIVDM/AIVDO sentences per second on a single core. Sponsored by endjin

AI for Earth Species Classification API

Planetary Computer SDK for Python

This is an API Framework for AI models to be hosted locally or on the AI for Earth API Platform (https://github.com/microsoft/AIforEarth-API-Platform).

Multi-species bioacoustic classification using deep learning algorithms

The AI for Earth API Platform is a distributed infrastructure designed to provide a secure, scalable, and customizable API hosting, designed to handle the needs of long-running/asynchronous machine learning model inference. It is to be used with the AI For Earth API Framework (https://github.com/microsoft/AIforEarth-API-Development).

Using machine learning to detect beluga whale calls in hydrophone recordings

Data catalog for the Microsoft Planetary Computer

Shared utility scripts for AI for Earth projects and team members

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.

Land cover mapping of the Orinoquía region in Colombia, in collaboration with Wildlife Conservation Society Colombia. An #AIforEarth project

A simple .NET AIS Receiver for capturing the Norwegian Coastal Administration AIS network data (available under Norwegian license for public data (NLOD)) and persisting in Microsoft Azure Blob Storage. Sponsored by endjin.

Code for the School of AI challenge "Explainable AI for Wildfire Forecasting", sponsored by Pi School to help NOA, the National Observatory of Athens, work with Explainable Deep Learning for Wildfire Forecasting.

A set of .NET Core libraries for converting AIS AIVDM/AIVDO sentences into different data formats. Sponsored by endjin.

Successes and limitations of pretrained YOLO detectors applied to unseen time-lapse images for automated pollinator monitoring