Introducing a new Python package named geospatial-ml that enables to install commonly used Python packages for geospatial analysis and machine learning with only one command.

geospatial-ml: A Python Package for Installing Common Geospatial Machine Learning Packages

Read here about PEARL - on open sourced platform that allows you to do fast AI-based land classification without writing a line of code.

Open Sourcing PEARL — Human-in-the-Loop AI for Land Cover Mapping

Read here about Microsoft's open source machine learning library, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

TorchGeo: Machine Learning Library for Geospatial Data

Similarity Search is a tool that uses self-supervised learning to locate similar images/data across space and time. It is intended to augment the process of dataset gathering for scientific studies.

NASA EarthData Similarity Search

The geeml Python package makes it easier to extract satellite data from Google Earth Engine using parallel processing and the Google Earth Engine high volume endpoint.

GEEML: Google Earth Engine Machine Learning

Transformers have achieved state of the art performance on remote sensing classification datasets and look set to make an impact on other common tasks such as segmentation and object detection.

Transformers in Remote Sensing

Check out this four-part series demonstrating how to use machine learning for detecting changes in land cover. Open source libraries and tools are used for this tutorial.

How to Detect Land Cover Changes Using Machine Learning

Here you can find introductions and Jupyter Notebook examples on how to access Radiant MLHub API.

Radiant MLHub Tutorials

This website provides a comprehensive and interactive catalog of reference benchmark datasets. Check it out here.

Catalog of Freely Available Earth Observation Reference Benchmark Datasets

The AiTLAS toolbox includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready earth observation datasets.

AiTLAS: AI Methods for Exploratory and Predictive Analysis of Satellite Images

This self-paced course contains a mixture of lectures and hands-on exercises for novice data science or remote sensing practitioners.

Free Course: Machine Learning for Earth Observation

Keep in touch with the latest in the ML4EO field and explore the latest tutorials and webinars designed to help you work efficiently with geospatial data and cloud-native practices.