  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.
