Multi-Scale Context Fusion for Pixel-Level Naturalness Mapping Using Sentinel-2 Imagery

As the impact of modern human activities on ecosystems intensifies, it becomes increasingly important to accurately assess this influence. Earth Observation, particularly through satellite imagery, serves as a key environmental conservation tool, providing a comprehensive overhead perspective for monitoring our planet’s ecosystems. This study formulates a pixel-wise regression task, guided by a novel set of naturalness annotations that quantify the modern human pressure on a landscape at the pixel level. We introduce a tailored framework that integrates geographical and contextual priors. These priors are represented by coordinate information and broader contextual information surrounding the immediate patch. Our approach improves the deep neural network’s capability to estimate naturalness from satellite imagery, enabling a deeper comprehension that promotes the safeguarding of our natural habitats.