Forest Disturbance Detection via Self-Supervised and Transfer Learning With Sentinel-1&2 Images
Rdvan Salih Kuzu,
Oleg Antropov,
Matthieu Molinier
et al.
Abstract:In this study, we examine the potential of leveraging self-supervised learning (SSL) and transfer learning methodologies for forest disturbance mapping using Earth Observation (EO) data. Our focus is on natural disturbances caused by windthrow and snowload damages. Particularly, we investigate the potential of knowledge distillation-based contrastive learning approaches to obtain comprehensive representations of forest structure changes using Copernicus Sentinel-1 and Sentinel-2 satellite imagery. Leveraging p… Show more
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