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Deep Learning for Detecting Building Setback Rule Violations in Urban Planning: A Case Study on Bangladesh
arXiv:2611.00037v1 Announce Type: cross Abstract: Dhaka's rapid urban growth has made building setback violations common, narrowing footpaths and blocking emergency access. RAJUK, responsible for building control in the Dhaka Metropolitan Area, enforces setback rules mainly through manual field inspection, which scales poorly. We present a systematic comparison of four machine learning approaches for classifying building setback conditions from street-level photographs: negative (compliant), moderate (borderline), and positive (violation). We built a labeled dataset of 372 geo-tagged images from sixteen Dhaka neighborhoods and expanded it with 5,968 augmented copies for training. We trained and evaluated a multi-layer perceptron (MLP), a Random Forest ensemble on frozen ResNet50 embeddings, a support vector machine on frozen ResNet18 embeddings, and a fine-tuned ResNet18 CNN. On a 1,275-image test set, the fine-tuned CNN achieved 65.4% accuracy (macro-F1 0.50), followed by Random Forest at 59.9% (macro-F1 0.44) and SVM at 58.2% (macro-F1 0.45). All models classify the compliant class reliably but struggle with the borderline moderate class due to visual ambiguity. These results establish reference performance levels for automated setback-compliance screening in a South Asian regulatory setting.
Abstract:Dhaka's rapid urban growth has made building setback violations common, narrowing footpaths and blocking emergency access. RAJUK, responsible for building control in the Dhaka Metropolitan Area, enforces setback rules mainly through manual field inspection, which scales poorly. We present a systematic comparison of four machine learning approaches for classifying building setback conditions from street-level photographs: negative (compliant), moderate (borderline), and positive (violation). We built a labeled dataset of 372 geo-tagged images from sixteen Dhaka neighborhoods and expanded it with 5,968 augmented copies for training. We trained and evaluated a multi-layer perceptron (MLP), a Random Forest ensemble on frozen ResNet50 embeddings, a support vector machine on frozen ResNet18 embeddings, and a fine-tuned ResNet18 CNN. On a 1,275-image test set, the fine-tuned CNN achieved 65.4% accuracy (macro-F1 0.50), followed by Random Forest at 59.9% (macro-F1 0.44) and SVM at 58.2% (macro-F1 0.45). All models classify the compliant class reliably but struggle with the borderline moderate class due to visual ambiguity. These results establish reference performance levels for automated setback-compliance screening in a South Asian regulatory setting.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2611.00037 [cs.CV]
(or arXiv:2611.00037v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.00037 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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