Mapping Land Naturalness from Sentinel-2 using Deep Contextual and Geographical Priors

In recent decades, the causes and consequences of climate change have accelerated, affecting our planet on an unprecedented scale. This change is closely tied to the ways in which humans alter their surroundings. As our actions continue to impact natural areas, using satellite images to see and measure these effects has become crucial for understanding and fighting climate change. Aiming to map land naturalness on the continuum of modern human pressure, we develop a multi-modal supervised deep learning framework that addresses the unique challenges of satellite data and the task at hand. We incorporate contextual and geographical priors. These priors are represented by corresponding coordinate information and broader contextual information including and surrounding the immediate patch to be predicted. Our framework improves the model's predictive performance to map land naturalness from a given Sentinel-2 data, a multi-spectral optical satellite imagery. Recognizing that our protective measures are as effective as our grasp of the ecosystem, quantifying naturalness serves as a crucial step towards enhancing our environmental stewardship.