Spatially Aware Deep Learning for Microclimate Prediction from High-Resolution Geospatial Imagery

ArXi:2603.13273v1 Announce Type: new Microclimate models are essential for linking climate to ecological processes, yet most physically based frameworks estimate temperature independently for each spatial unit and rely on simplified representations of lateral heat exchange. As a result, the spatial scales over which surrounding environmental conditions influence local microclimates remain poorly quantified. Here, we show how remote sensing can help quantify the contribution of spatial context to microclimate temperature predictions.