Low-Cost Deep Learning for Building Detection with Application to Informal Urban Planning

Abstract

This article studies the application of deep neural networks for automatic building detection in aerial RGB images. Special focus is put on accuracy robustness in both well-structured and poorly planned urban scenarios, which pose significant challenges due to occlusions, irregular building layouts, and limited contextual cues. The applied methodology considers several CNNs using only RBG images as input, and both validation and transfer capabilities are studied. U-Net-based models achieve the highest single-model accuracy, with an Intersection over Union (𝐼⁢𝑜⁢𝑈) of 0.9101. A soft-voting ensemble of the best U-Net models further increases performance, reaching a best ensemble 𝐼⁢𝑜⁢𝑈 of 0.9665, improving over state-of-the-art building detection methods on standard benchmarks. The approach demonstrates strong generalization using only RGB imagery, supporting scalable, low-cost applications in urban planning and geospatial analysis.