CNN-Based Classification of Façade Motifs in Market-Developed Housing: A Computational Approach to Tel Aviv’s 1980s–1990s Urban Fabric

This study applies deep learning to classify façade features in Tel Aviv’s market-developed apartment housing (1980s–1990s), a vast landscape typically excluded from architectural history due to its non-iconic character. We constructed a curated corpus of 877 expert-labeled high-resolution façade images and evaluated whether convolutional neural networks can detect historically meaningful patterns at urban scale. Focusing on the “staggered balcony” motif—linked to national regulation 5442/1992—we show that a ConvNeXt-Tiny model achieved robust classification performance (96.6% accuracy, 90.3% F1) after rigorous dataset curation and expert relabeling. Initial experiments on noisier data produced inconsistent results, underscoring the importance of domain expertise in operationalizing historical categories. Rather than treating machine learning as definitive classification, we present an iterative workflow where architectural historians use model outputs to refine categories, test morphological hypotheses, and identify overlooked variations. The findings demonstrate how CNN-based analysis can advance empirical research on non-iconic built environments and open methodological pathways for cultural heritage studies and digital architectural humanities.