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.

It is of great significance to conduct seismic hazard prediction in mitigating the damage caused by earthquakes in urban area. In this study, a geographic information system (GIS)-based seismic hazard prediction system for urban earthquake disaster prevention planning is developed, incorporating structural vulnerability analysis, program development, and GIS. The system is integrated with proven building vulnerability analysis models, data search function, spatial analysis function, and plotting function. It realizes the batching and automation of seismic hazard prediction and the interactive visualization of predicted results. Finally, the system is applied to a test area and the results are compared with results from previous studies, the precision of which was improved because the construction time of the building was taken into consideration. Moreover, the system is of high intelligence and minimal manual intervention. It meets the operating requirements of non-professionals and provides a feasible technique and operating procedure for large-scale urban seismic hazard prediction. Above all, the system can provide data support and aid decision-making for the establishment and implementation of urban earthquake disaster prevention planning.

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.

Seismic hazard prediction is essential for earthquake preparation in urban areas. Prediction methods based on refined the finite element method (FEM) models and time-history analysis require higher calculation, which is hard to apply universally. In this paper, by applying two simplified models and time-history analysis, the authors were able to develop a refined seismic hazard prediction system for urban buildings using MATLAB and OpenSees. The system is modular in design with a unified data interface to transfer data, where only the macro parameters of buildings are required to complete the computation work. Taking the campus of Tongji University as a test area, the system was applied to predict seismic hazard and compare it with the results of the vulnerability method by applying the seismic damage index to verify its reliability. The results show that the system can meet the needs of accuracy, efficiency, and visualization of seismic hazard prediction in urban areas.

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.

A basic framework of a GIS-based seismic hazard prediction system for urban earthquake disaster prevention planning is developed in this study, incorporating structural vulnerability analysis, program development, and GIS. The system is integrated with proven building vulnerability analysis models, data search function, spatial analysis function, and plotting function, realizing the batching and automation of seismic hazard prediction and the interactive visualization of predicted results. The system is applied to a test area and the results are compared with results from previous studies to verify that the system can provide data support and aid decisionmaking for the establishment and implementation of urban earthquake disaster prevention planning. Results from this study are essentially the same as the results of 2003 and slightly better than the results of 1993, which highlights the reliability of the fragility analysis method applied in this system.