DeepFeatures: Remote sensing beyond spectral indices

Terrestrial surface processes exhibit distinctive spectral signatures captured by optical satellites. Despite the development of over two hundred spectral indices (SIs), current studies often narrow their focus to individual SIs, overlooking the broader context of land surface processes. This project seeks to understand the holistic features of Sentinel-2 based SIs and their relationships with human impact and overall land surface dynamics. To address this, we propose an AI-driven approach that synthesises SIs derived from Sentinel data through dimension reduction, yielding interpretable latent variables describing the system comprehensively. Our goals are to (i) reduce the number of SIs and (ii) compute a few latent variables representing spatio-temporal dynamics, which culminate in a Feature Data Cube. This fully descriptive cube reduces computational costs, facilitating diverse applications. We plan to demonstrate its efficacy in land cover classification, standing deadwood detection, and terrestrial gross primary production estimation. The presentation outlines the project's implementation strategy, confronts methodological challenges, and extends an invitation to the remote sensing and machine learning community to collaborate on pressing environmental challenges. The project DeepFeatures is funded by ESA’s AI4Science activity. Website: https://rsc4earth.de/project/deepfeatures/