Examples of using the Planetary Computer

Land Cover Mapping

Tools for detecting wildlife in aerial images using active learning

To gain access, please finish setting up this repository now at: https://repos.opensource.microsoft.com/microsoft/wizard?existingreponame=SpeciesClassification&existingrepoid=169153301

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).

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

Shared utility scripts for AI for Earth projects and team members

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).

.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

Planetary Computer SDK for Python

Data catalog for the Microsoft Planetary Computer

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.

Using machine learning to detect beluga whale calls in hydrophone recordings

Multi-species bioacoustic classification using deep learning algorithms

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.

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

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.