Hybrid CNN Vision Transformer Framework with Grad-CAM and SHAP Analysis for Urban Change Detection

To track land use and land cover transformation in Makkah, techniques that allow steep relief, spectral confusion, and dense sacred–commercial mosaics, and can be justified in terms of planning, should be used. Satellite images are tedious and prone to uneven labeling on mixed-pixel boundaries, particularly in urban regions and Haram borders. Using multi-temporal Landsat-8 data (2013 and 2024), a hybrid deep learning architecture comprising U-Net, DenseNet201, and a Vision Transformer was trained. U-Net retained the geometry of the boundaries, DenseNet201 reinforced feature transfer across heterogeneous textures, and the transformer modeled long-range context. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to incorporate interpretability during spatial attention mapping, and Shapley Additive exPlanations (SHAP) during spectral topographic attribution, after which paired class-level statistical tests were performed. Modern residential increased from 15% to 20% (180 million to 240 million m2); roads from 5% to 10% (60 million to 120 million m2); industrial facilities from 3% to 5% (36 million to 60 million m2). The vegetation expanded by 1 to 5% (an addition of 48 million m2), and agriculture declined by 2 to 1% (a loss of 12 million m2). Its tension with urban development and preservation of productive land was growing. The proposed U-Net–DenseNet201–ViT hybrid system achieved over 98% overall accuracy on the test data for both study years, with kappa coefficients of 0.978 and 0.981 for 2013 and 2024, respectively. Grad-CAM identified attention focused on development fronts and transport corridors, whereas SHAP identified SWIR, thermal response, and slope as the main drivers. Significant class-level gains were statistically validated (p < 0.01), confirming an interpretable and auditable account of land transformation in Makkah.