ArticleScientific reports2025
Domain adaptation using transformer models for automated detection of exterior cladding materials in street view images.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A YOLOv8 deep learning model for detecting labral injury via hip magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- Research on Object Detection in Cluttered Hospital Corridor Scenes with CSAWOA-YOLOv8.Biomimetics (Basel, Switzerland) · 2026Article
- YOLO-FSEP: An Improved YOLOv8n Algorithm for Sugar Orange Detection in Orchards.Sensors (Basel, Switzerland) · 2026Article
- Adversarial CAM Guidance for Chest X-Ray Classification: Reducing Framing Sensitivity with Mask Supervision.Biomimetics (Basel, Switzerland) · 2026Article
- YOLOv10-based multi-scale variant object detection for multi-category PPE non-compliance monitoring on construction sites.Scientific reports · 2026Article
- Keypoint-Based Forest Musk Deer Behavioral Recognition Method.Animals : an open access journal from MDPI · 2026Article
- LRD-DETR: A Lightweight RT-DETR-Based Model for Road Distress Detection.Sensors (Basel, Switzerland) · 2026Article
- Defect-Intent Ambiguity Addressing for Training-Free Deterministic PCB Defect Localization via Template Selection and Dissimilarity Mapping.Sensors (Basel, Switzerland) · 2026Article
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Abstract
Recent advances in deep learning have achieved impressive accuracy in various building analysis tasks using street view imagery). However, a major challenge lies in the large-scale, labeled datasets typically required—an obstacle driven by limited raw data access and labor-intensive annotations. To overcome this, the present study introduces a domain adaptation (DA) framework for classifying exterior cladding materials. Six categories are targeted: Brick, Concrete, Glass, Stone, Mixed, and Others. A fully labeled dataset from Scotland and a partially labeled dataset from London form the basis of the approach, which leverages transformer-based architectures, data augmentation, and hyperparameter optimization to boost accuracy. In evaluations on unseen data, an axial transformer trained with augmented data and optimized hyperparameters emerged as most effective, achieving class-specific accuracies of 88.43% (Brick), 73.71% (Concrete), 68.67% (Glass), 91.33% (Stone), 86.65% (Mixed), and 83.46% (Others), culminating in an overall accuracy of 82.04%. These findings illustrate the potential of the DA-based method to maintain strong performance, with further refinements suggested for future work. The paper subsequently explores additional applications of this proposed strategy.
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