The built year and structure of individual buildings are crucial factors for estimating and assessing potential earthquake and tsunami damage. Recent advances in sensing and analysis technologies allow the acquisition of high-resolution street view images (SVIs) that present new possibilities for research and development. In this study, we developed a model to estimate the built year and structure of a building using omnidirectional SVIs captured using an onboard camera. We used geographic information system (GIS) building data and SVIs to generate an annotated built year and structure dataset by developing a method to automatically combine the GIS data with images of individual buildings cropped through object detection. Furthermore, we trained a deep learning model to classify the built year and structure of buildings using the annotated image dataset based on a deep convolutional neural network (DCNN) and a vision transformer (ViT). The results showed that SVI accurately predicts the built year and structure of individual buildings using ViT (overall accuracies for structure = 0.94 [3 classes] and 0.96 [2 classes] and for age = 0.68 [6 classes] and 0.90 [3 classes]). Compared with DCNN-based networks, the proposed Swin transformer based on ViT architectures effectively improves prediction accuracy. The results indicate that multiple high-resolution images can be obtained for individual buildings using SVI, and the proposed method is an effective approach for classifying structures and determining building age. The automatic, accurate, and large-scale mapping of the built year and structure of individual buildings can help develop specific disaster prevention measures.

Remote sensing image semantic segmentation of land use, benefitted from the development of deep learning and consequently made considerable progress in terms of inferencing accuracy and speed. However, the effective training of semantic segmentation models for remote sensing imagery necessitates extensively detailed pixel-level annotations, and gathering such data is both time-intensive and laborious. Thus, this study implemented low-rank adaptation on a stable diffusion algorithm to learn the distribution of the pixel-level annotations in case of the LoveDA dataset. Consequently, the annotation-image pairs were used to train the remote sensing image generator based on stable diffusion guided by ControlNet. We proposed a stable diffusion based approach, which can generate image-annotation pairs from scratch. The generated annotation and image pairs achieved a high accuracy of 0.520 mean intersection-over-union on LoveDA dataset, which is close to the original data training result of 0.539 mIoU. Furthermore, the mixed training using generated and original data achieved 0.542 mIoU, thereby demonstrating the data augmentation function of our approach. This study provided a solution for the high-cost pixel-level annotation issue, and thus, exhibited the potential of artificial intelligence generated content.

People flow trend estimation is crucial to traffic and urban safety planning and management. However, owing to privacy concerns, the collection of individual location data for people flow statistical analysis is difficult; thus, an alternative approach is urgently needed. Furthermore, the trend in people flow is reflected in streetscape factors, yet the relationship between them remains unclear in the existing literature. To address this, we propose an end-to-end deep-learning approach that combines street view images and human subjective score of each street view. For a more detailed people flow study, estimation and analysis were implemented using different time and movement patterns. Consequently, we achieved a 78% accuracy on the test set. We also implemented the gradient-weighted class activation mapping deep learning visualization and L1 based statistical methods and proposed a quantitative analysis approach to understand the land scape elements and subjective feeling of street view and to identify the effective elements for the people flow estimation based on a gradient impact method. In summary, this study provides a novel end-to-end people flow trend estimation approach and sheds light on the relationship between streetscape, human subjective feeling, and people flow trend, thereby making an important contribution to the evaluation of existing urban development.

Recent advances in deep learning have improved automated building footprint extraction from satellite imagery, yet nationwide deployment remains challenging due to strong regional variation in building appearance and the high cost of manual annotations required for model generalization. We present a modular weakly supervised framework that leverages noisy OpenStreetMap (OSM) building footprints as scalable supervision for large-area building extraction. The proposed system integrates (i) a lightweight annotation correction module that improves the geometric consistency of OSM-derived training labels, (ii) a teacher–student fine-tuning strategy that progressively refines pseudo-labels to enhance cross-region generalization, and (iii) a city-scale evaluation module with an optimized matching algorithm for reliable footprint-level assessment. We demonstrate the practicality of the framework through a nationwide experiment in Japan, producing approximately 49.6 million building footprints in 33.5 days at a total cloud cost of $4,470. On our Japan benchmark, the resulting model achieves an F1-score of 0.83 using only 2% of the data for fine-tuning. Across regions, an notation correction improves bounding-box IoU by 20% and in creases the average F1-score by approximately 0.12 compared to using raw OSM footprints. Moreover, the weakly supervised fine tuning reaches about 90% of the fully supervised performance on the same benchmark without additional manual labeling. Overall, our work provides a reproducible and cost-effective solution for improving generalization in nationwide building footprint extraction. The extracted footprints and configurations are publicly released at https://github.com/sekilab/Japan footprint dataset.