A multiscale building detection method based on boundary preservation for remote sensing images: Taking the Yangbi M6.4 earthquake as an example

Dong, Zhe; Zhang, Mingming; Liu, Qingjie; Qi, Wen; Wang, Wei; Luo, Weier; Wu, Zhihong; Tang, Tao; Ji, Weizhen

doi:10.1016/j.nhres.2022.06.001

Cited by 8 publications

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“…In the specific study, Han et al [85] proposed a contextual scale-aware detector that enhanced the perception of small and weak objects by introducing context information and scale-aware modules. Dong et al [86] proposed a multi-scale building detection method based on boundary protection, which effectively solved the problems of missed and false detection in traditional methods. A quick and accurate approach to object detection in remote sensing images was proposed by Zhang et al [87] and is based on aerial optical sensors.…”

Section: Remote Sensing Image Object Detection Methods Based On Multi...mentioning

confidence: 99%

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A Review: Remote Sensing Image Object Detection Algorithm Based on Deep Learning

Bai,

2023

Electronics

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Target detection in optical remote sensing images using deep-learning technologies has a wide range of applications in urban building detection, road extraction, crop monitoring, and forest fire monitoring, which provides strong support for environmental monitoring, urban planning, and agricultural management. This paper reviews the research progress of the YOLO series, SSD series, candidate region series, and Transformer algorithm. It summarizes the object detection algorithms based on standard improvement methods such as supervision, attention mechanism, and multi-scale. The performance of different algorithms is also compared and analyzed with the common remote sensing image data sets. Finally, future research challenges, improvement directions, and issues of concern are prospected, which provides valuable ideas for subsequent related research.

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Section: Remote Sensing Image Object Detection Methods Based On Multi...mentioning

confidence: 99%

“…Dong et al [86] Accurate maintenance of building boundaries is emphasized, and the accuracy and robustness of detection are improved using a multi-scale strategy.…”

Section: Literature Paper Highlights Applicabilitymentioning

confidence: 99%

A Review: Remote Sensing Image Object Detection Algorithm Based on Deep Learning

Bai,

2023

Electronics

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“…The results showed that the object-oriented change detection method could extract damage conditions with high accuracy. The authors of [9] compared the roofs of buildings before and after the 2021 earthquake in Yangbi County, Dali Prefecture, and Yunnan Province. It was found that the investigation time to detect damage was significantly shorter than the manual investigation.…”

Section: Introductionmentioning

confidence: 99%

Proposal of a Disrupted Road Detection Method in a Tsunami Event Using Deep Learning and Spatial Data

Sakamoto

2023

Sustainability

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Tsunamis generated by undersea earthquakes can cause severe damage. It is essential to quickly assess tsunami-damaged areas to take emergency measures. In this study, I employ deep learning and develop a model using aerial photographs and road segment data. I obtained data from the aerial photographs taken after the Great East Japan Earthquake; the deep learning model used was YOLOv5. The proposed method based on YOLOv5 can determine damaged roads from aerial pictures taken after a disaster. The feature of the proposed method is to use training data from images separated by a specific range and to distinguish the presence or absence of damage related to the tsunami. The results show that the proposed method is more accurate than a comparable traditional method, which is constructed by labeling and learning the damaged areas. The highest F1 score of the traditional method was 60~78%, while the highest F1 score of the proposed method was 72~83%. The traditional method could not detect locations where it is difficult to determine the damage status from aerial photographs, such as where houses are not completely damaged. However, the proposed method was able to detect them.

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Benefit of structural access panels (SAP) for post-earthquake structural health monitoring

Luo,

MacRae,

Xue

et al. 2023

Bull Earthquake Eng

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This paper develops a method to quantify the benefit of using structural access panels (SAPs) placed in internal partition walls (IPWs) or ceilings near the dissipative elements of structures for post-earthquake structural health monitoring (SHM) without damaging the building non-skeletal elements (NSEs). The benefit of SAPS is considered for different levels of shaking considering the possibility of (i) no likely structural damage, (ii) likely building irreparability, or (iii) building NSE damage. The methodology is developed for shaking intensity scenarios with annual shaking probabilities of exceedance ranging from 1/25 to 1/10,000. Structural demands for different scenarios are initially obtained using time history analysis. The number of scenarios was increased by additionally generated demands assuming lognormal distributions of response. Monte-Carlo analysis was used to assess the likelihood of reaching particular decision states, considering uncertainty associated with achieving these states. Probabilistic (time-based) benefits of using SAPs are also considered. Additionally, buildings with both conventional IPWs and low-damage IPWs are considered. Finally, the methodology is applied to a realistic 6 storey eccentrically braced frame (EBF) structure. In the study, the scenario-based analysis indicated that the SAPs were not effective during very small earthquake events where the post-earthquake inspection will not be triggered, or during very large events where the building is externally observed to be irreparable. Furthermore, if the NSEs equipped with SAPs sustain significant damage, SAPs were not needed for inspection. As a result, the SAPs were more beneficial when installed in low-damage NSEs, as compared to conventional NSEs, especially during moderate to high shaking levels. For the casestudy building considered, the probabilistic loss study indicates the break-even time for SAPs to be economical, considering initial installation costs, and inspection savings related to direct damage and downtime losses, was less than 2 years. The benefit-to-cost ratio of SAPs for rapid and reliable post-earthquake inspection was up to 26 over the expected building life of 50 years.

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A multiscale building detection method based on boundary preservation for remote sensing images: Taking the Yangbi M6.4 earthquake as an example

Cited by 8 publications

References 0 publications

A Review: Remote Sensing Image Object Detection Algorithm Based on Deep Learning

A Review: Remote Sensing Image Object Detection Algorithm Based on Deep Learning

Proposal of a Disrupted Road Detection Method in a Tsunami Event Using Deep Learning and Spatial Data

Benefit of structural access panels (SAP) for post-earthquake structural health monitoring

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