Towards Trustworthy Urban Land Use Classification: A Synergistic Fusion of Deep Learning and Explainable Machine Learning with a Nanning Case Study

Yusheng Zheng, Xinying Huang, Huanmei Yao

While artificial intelligence (AI) has advanced urban land use classification, its application in high-stakes decision making, such as urban planning, demands not only high accuracy but also transparency and interpretability. This study evaluates the potential of Google Satellite Embeddings (GSE), a ready-to-use dataset of AI-generated numerical features that capture deep land cover characteristics, for land use classification in the central urban area of Nanning in 2022. A synergistic analytical framework was constructed by integrating the 64 high-dimensional features of GSE data with the feature attribution of Shapley Additive Explanations (SHAP), merging deep learning features with explainable machine learning. The results demonstrate that the XGBoost model (OA = 85.00% ± 2.24%) significantly outperformed the Random Forest (RF) model (OA = 81.87% ± 1.72%) overall. Key abstract features were successfully interpreted as comprehensible geographic semantics, with A51 and A36 corresponding to built-up intensity and vegetation cover, respectively. Moreover, XGBoost enabled more refined decisions than Random Forest (RF) due to its superior ability to distinguish between functionally distinct classes that have similar physical appearances. This framework provides a scalable and transferable analytical solution for the challenges of feature limitations and insufficient model transparency in urban land use classification.