Conference Agenda

This study examines how street-view scenes relate to urban perception using a scene-graph-driven modeling method. Each image is parsed into subject–predicate–object triplets; entity appearance from a CNN backbone and relation semantics from a Transformer detector are fused at node level via a learnable gate. A relation-aware graph neural network performs message passing and attentive readout to predict six perception dimensions (beautiful, boring, depressing, lively, safe, wealthy). Taking Place Pulse 2.0 dataset as benchmark, we convert pairwise votes to binary labels per dimension with standard train/validation/test splits. Experiments compare the graph approach against CNN+SVM and Transformer+SVM baselines under identical protocols. Results show consistently higher accuracy across all six dimensions, with notable gains for beautiful and wealthy. Gradient and integrated-gradient analyses offer node- and edge-level attributions, highlighting elements such as trees, facades, and overhead wires. The method balances accuracy with clarity, and the results point to practical cues that can support human-centered urban design.

Road surface conditions decline due to heavy traffic volumes, severe weather, and recurring utility works, yet still, many road agencies still rely on manual windshield surveys and semi-automated inspections. Not only are these methods time-consuming, but also difficult to scale and labour-intensive. With the help of recent advances in deep learning and the widespread availability of built-in vehicle dashcams, they offer new opportunities for low-cost, automated pavement assessments. This contribution presents a mobile, dashcam-based framework for detecting road-surface defects using the latest YOLOv11, which is combined with geolocation tagging for spatial visualization.

To test out our YOLOv11 training model, we conducted the initial dataset at the University of the Fraser Valley campus and manually annotated it to identify crack fillings, crosswalk markings, speed bumps, lane markings, and other surface conditions. This was just a prototype, which would later be trained to detect all road conditions, such as gravel, potholes, and uneven roads, as well. To address variations in lighting and motion, augmentation techniques were applied. YOLOv11 acquired a mean average precision above 90% across all tested categories.

This prototype demonstrates a practical, low-cost approach for real-time pavement monitoring. Future work includes expanding data collection, developing an operational dashboard for road authorities, having exact GPS coordinates pinned on maps with damaged road images, and evaluating model performance across different data sources, including models trained through Google Images. By producing actionable geospatial information, this system supports more efficient maintenance workflows and offers a scalable pathway for municipalities seeking to modernize road-condition assessment.

Global population is projected to increase by about 70% by 2050, with a growing proportion of people living in urban areas. This trend highlights the importance of accurately assessing urban expansion. Automatic building detection from remotely sensed imagery using deep learning (DL) has demonstrated considerable potential for applications, including sustainable urban planning and infrastructure monitoring. However, the inherent black-box nature of DL models limits their transparency and reduces trust in model-driven decisions. Although various Explainable Artificial Intelligence (XAI) approaches have been proposed to highlight image regions influencing model predictions, qualitative visual inspection alone is insufficient for reliably evaluating the credibility of these explanations. This study evaluates several XAI techniques for building footprint extraction using a U-Net model trained on a refined Massachusetts Buildings Dataset. The segmentation model achieved precision, recall, F1-score, IoU, and overall accuracy values of 89.68%, 85.69%, 87.53%, 79.03%, and 94.35%, respectively. To investigate the model’s decision-making process, three explanation methods, namely Saliency, GradientSHAP, and GuidedGradCAM, were applied. The quality of the generated explanations was then quantitatively assessed using 16 evaluation metrics. Beyond single-image analysis, a dataset-level evaluation was conducted using 547 image patches containing building coverage greater than 20%. The results indicate that GuidedGradCAM produces more consistent and reliable explanations. Furthermore, dataset-level analysis using dense-building samples provides a statistically more robust representation of overall model behaviour compared to evaluations based on individual images. These findings highlight the importance of quantitative assessment in validating the interpretability of DL models for building footprint extraction.

Urban rooftops represent a critical vertical resource for sustainable development, yet comprehensive assessment of their utilization patterns and available capacity remains constrained by inadequate datasets and limited algorithmic capabilities. This study introduces the Urban Rooftop Utilization Dataset (URUD), the first multi-city, pixel-level semantic segmentation dataset encompassing 1,560 high-resolution satellite images from four Chinese cities. URUD establishes eight semantic categories including a novel "available area" class to address ambiguous regions that existing classification schemes fail to capture. The study further proposes MaskRoof, a transformer-based deep learning framework specifically designed for fine-grained rooftop analysis. The model integrates two task-specific modules, Hierarchical Zoom-in Attention (HZA) and Prior-Guided Cross-Attention (PGCA), to address challenges of small-scale target detection and class imbalance. Experimental results demonstrate that MaskRoof achieves superior performance with 94.46% accuracy and 47.29% mIoU, outperforming existing segmentation architectures. Application to Shanghai's outer ring area reveals that 60.74% of rooftop space remains available for utilization, with significant spatial heterogeneity across building types. Industrial and warehouse structures retain substantially greater unutilized areas compared to office and residential buildings. These findings provide quantitative evidence for differentiated urban planning strategies and demonstrate the framework's capability for large-scale rooftop potential assessment in complex urban environments.

Real-time defect detection in solar farms is critical for profitability and safety. This paper compares state-of-the-art (SOTA) object detection architectures for deployment on edge computing platforms for the purpose of thermal PV defect detection using UAV imagery. We systematically evaluated Closed-Set (YOLOv10, YOLOv12, RT-DETR, RF-DETR) and Open-Vocabulary (YOLO-World, OWL-ViT) models on a public thermal dataset. Our results highlight two leading candidates. The transformer-based RF-DETR sets a new accuracy record at 82.6% mAP@0.50, driven by its self-supervised backbone, but its inference speed is low (12.6 FPS). Conversely, the CNN-based YOLO-World integrates language semantics to reach a competitive 78.1% mAP@0.50 while operating at a real-time speed of 31.3 FPS. We conclude that both RF-DETR and YOLO-World are promising for embedded thermal fault detection. The final selection will depend on on-platform inference performance.