Mapping Land Naturalness from Sentinel-2 using Deep Contextual and Geographical Priors
This work addresses the need for better environmental monitoring to combat climate change, but it appears incremental as it builds on existing methods with added priors.
The authors tackled the problem of mapping land naturalness from Sentinel-2 satellite data to monitor human impacts on ecosystems, and their multi-modal deep learning framework improved predictive performance, though no concrete numbers were provided.
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 observe and measure these effects has become crucial for understanding and combating climate change. Aiming to map land naturalness on the continuum of modern human pressure, we have developed 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, 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 in mapping land naturalness from Sentinel-2 data, a type of multi-spectral optical satellite imagery. Recognizing that our protective measures are only as effective as our understanding of the ecosystem, quantifying naturalness serves as a crucial step toward enhancing our environmental stewardship.