Imagery

This post shows you how to access public Google Earth Engine assets, view and analyze them, and save as a portable GeoTIFF format using only the terra R package.

Want to learn more about satellite imagery? Here you can find tutorials, user guides and examples of how you can use satellite imagery to analyze various phenomena and events around the globe.

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 article analyses the Airbus oil storage dataset which contains over 13,500 annotated POL (petroleum, oil and lubricant) storage objects. This dataset is used to train an oil storage detector.

Actinia is an open source REST API for scalable, distributed, high performance processing of geographical data that mainly uses GRASS GIS for computational tasks. Learn more about it here.

EOReader is a remote sensing open-source Python library reading optical and SAR constellations, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.

This is a single class dataset consisting of tiles of satellite imagery labeled with potential 'targets' to help with search and rescue.

Here is a long list of available benchmark datasets which are acquired using airborne/spaceborne imaging/radar sensors.

The ggplot2 R package does not natively support plotting raster data. This article presents a custom method for plotting RGB satellite imagery with ggplot2.

YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks.