A high-performance deep feature encoding network using GeoFusion-ChangeNet for land cover mapping and change detection in remote sensing imagery

Abstract

Remote sensing-based land cover classification and change detection are essential for ensuring environment, planning cities, and managing resources. Accurately extracting spatial and temporal information from high-resolution multi-temporal images remains challenging due to feature inconsistency, class imbalance, and limited integration of spatial–temporal representations in existing methods. Traditional deep learning approaches often fail to capture subtle variations, struggle to generalize across complex and heterogeneous landscapes, leading to reduced accuracy in change detection. To overcome these constraints, this research introduces an advanced deep learning framework, GeoFusion-ChangeNet, for high-resolution land cover mapping and automatic change detection. The proposed model integrates hierarchical convolutional feature encoder with a residual deep feature backbone to effectively capture rich multi-scale spatial features. An attention-based mechanism is included to improve feature representation by concentrating on informative regions while suppressing irrelevant information. Furthermore, a multi-temporal feature fusion method that combines feature concatenation and feature differencing is used to effectively capture temporal variations between input images. The proposed system is also deployed through an interactive interface to enable real-time visualization of classification and change detection results. The experimental results on the SECOND benchmark dataset achieved Accuracy of 95.00%, IoU of 86.00%, F1-Score of 93.00%, and Kappa Coefficient of 84.00%. In addition, cross-dataset evaluation on the LEVIR-CD benchmark achieved Accuracy of 93.12%, F1-Score of 90.61%, IoU of 82.83%, and Kappa Coefficient of 81.47%, indicating that GeoFusion-ChangeNet maintains stable performance under an additional remote sensing change-detection setting.