techniques

Why we included this project

Dozens of deep learning papers and code repos are scattered across the web, and this collection does the useful work of gathering the ones that matter for satellite and aerial imagery into a single searchable index. It covers the usual tasks like classification, segmentation, object detection, and change detection, but also reaches into time series, cloud removal, SAR, and newer large vision-language and foundational models, each entry pointing to the actual papers, datasets, and runnable code. A researcher can quickly survey what already exists before proposing something new, while an engineer scoping an earth observation problem can jump straight to the technique that fits. Because the maintainers keep it current, the links reflect what people are actually using rather than a frozen snapshot of the literature. Teams new to remote sensing also get a practical on-ramp that names the standard datasets and tools they will actually encounter.