Low-Cost Deep Learning for Building Detection with Application to Informal Urban Planning
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ijgi-15-00036.pdf
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5.62 MB
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Adobe PDF
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Author(s)
Gonzรกlez, Lucas
Toutouh, Jamal
Nesmachnow, Sergio
Date Issued
January 9, 2026
Journal
ISPRS International Journal of Geo-Information
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Gonzรกlez, L.; Toutouh, J.; Nesmachnow, S. Low-Cost Deep Learning for Building Detection with Application to Informal Urban Planning. ISPRS Int. J. Geo-Inf. 2026, 15, 36.
Version
Final published version
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 (๐ผโข๐โข๐) of 0.9101. A soft-voting ensemble of the best U-Net models further increases performance, reaching a best ensemble ๐ผโข๐โข๐ 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
http://dx.doi.org/10.3390/ijgi15010036
