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.

I. INTRODUCTION The advancement in deep learning (DL) techniques has led to a notable increase in the number and size of annotated datasets in a variety of domains, with remote sensing (RS) being no exception [1]. Also, an increase in earth observation (EO) missions and easy access to globally available and free geodata have opened up new research opportunities. Although numerous RS datasets have been published in the past years [2]-[6], most of them addressed tasks concerning man-made environments such as building footprint extraction and road network classification, leaving the environmental and ecology-related sub-areas of remote sensing underrepresented. The ESA WorldCover map legend is given below the figure. In this community, the classification task can be machine learning model in the form of deep neural networks. While some methods frame the RS-related classification (usually called semantic segmentation by tasks within the context of perturbation-seeking generative the computer vision community) the task outputs denselyannotated adversarial networks [14], some others made use of uncertainty prediction maps on a pixel scale by separating the estimation applied to deep ensembles [15] and self-attention input into distinct and semantically coherent segments.

A global audience of a half-billion gamers have gotten to know the axolotl, which largely cluster in the canals around Mexico City and look like little dragons with a goofy smile. The videogame "Fortnite" trotted out axolotl characters in 2020, and "Minecraft" followed suit last summer. Roblox, a platform with millions of user-made games, has dozens of axolotl-centric ones, including "Axolotl Tycoon" and "Axolotl Paradise." Axolotls appear in "Adopt Me!," one of the most-played games on Roblox. All of the exposure has spawned axolotl memes, YouTube videos, coloring books and nonfungible tokens.