Read here about Microsoft's open source machine learning library, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.
Archive: Machine-learning
- Read here about PEARL - on open sourced platform that allows you to do fast AI-based land classification without writing a line of code.
- 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.
- Check out this workflow demo for generating cropland maps with machine learning and CropHarvest, a global dataset for crop-type classification. Link to end-to-end workflow resources included.
- 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.
- This self-paced course contains a mixture of lectures and hands-on exercises for novice data science or remote sensing practitioners.
- 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.
- This website provides a comprehensive and interactive catalog of reference benchmark datasets. Check it out here.
- Here you can find introductions and Jupyter Notebook examples on how to access Radiant MLHub API.
- 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.
