Deep Learning With Explicit Geographic Coordinate Embedding for Improved Remote Sensing Image Classification

Remote sensing imagery inherently contains geographic coordinate information, and the spatial distribution of land-cover types often exhibits pronounced regional heterogeneity. However, most deep learning approaches in remote sensing still follow computer vision paradigms, treating images as ordinary pixel matrices while ignoring their intrinsic geographic attributes, which limits both representation capacity and spatial generalization. To address this issue, we propose a spatially enhanced deep learning framework that explicitly incorporates geographic reference information. Specifically, the geographic coordinates of image centers are introduced as additional input channels into convolutional neural networks through a simple embedding mechanism, enabling the model to adaptively capture regional variations while maintaining a unified and backbone-agnostic architecture. Based on the EuroSAT dataset, we construct a new benchmark, termed Geo-EuroSAT, by embedding coordinate information and conducting systematic comparative experiments under multiple training-validation split strategies. Both ResNet-34 and EfficientNet-B3 backbones are evaluated to verify the general applicability of the proposed method. Experimental results demonstrate that the coordinate-aware models consistently outperform conventional baselines across all settings (p <0.005), with overall accuracy improvements ranging from 0.4% to 1.8%. The gains are especially pronounced under limited training samples and for vegetation-related classes such as pasture and herbaceous vegetation. These findings indicate that explicitly integrating geographic reference into deep networks via a lightweight, backbone-agnostic embedding provides a principled and generalizable way to enhance large-scale land-cover classification and geographic process modeling.