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The rapid and accurate detection of damaged buildings after an earthquake are critical for emergency response. Given the difference in the textures of damaged parts and those of the original buildings, damaged buildings can be accurately detected through textural heterogeneity. However, quantitatively detecting damaged buildings using such heteroge...
Citations
... These studies aimed to develop a comprehensive dataset by incorporating data from seismic and tsunami monitoring networks. Other studies have demonstrated the important role of sensors in estimating the impacts and losses caused by earthquakes (Zhang et al. 2020;Xia et al. 2021) and tsunamis (Li and Goda 2022;Virtriana et al. 2023). These studies highlighted the significance of utilizing sensor data, such as groundmotion recordings and wave measurements, to improve the accuracy and effectiveness of post-event impact and loss estimation. ...
This article presents a novel approach to estimate multi-hazard loss in a post-event situation, resulting from cascading earthquake and tsunami events with machine learning for the first time. The proposed methodology combines the power of random forest (RF) with data that are simulated at seismic and tsunami monitoring locations. The RF model is well-suited for predicting highly nonlinear multi-hazard loss because of its nonparametric regression and ensemble learning capabilities. The study targets the cities of Iwanuma and Onagawa in Tohoku, Japan, where seismic and tsunami monitoring networks have been deployed. To encompass a diverse range of future multi-hazard loss estimation, an RF model is constructed based on 4000 simulated earthquake events with peak ground velocity and tsunami wave amplitude captured at ground-motion monitoring sites and offshore wave monitoring sensors, respectively. The incorporation of 10 ground-motion monitoring sites and five offshore wave monitoring sensors significantly enhances the model’s forecasting power, leading to a notable 60% decrease in mean squared error and 20% increase in the R2 value compared to scenarios where no monitoring sensors are utilized. By harnessing the capabilities of RF and leveraging detailed sensing data, RF achieves R2 values over 90%, which can contribute to enhanced disaster risk management.
... Some alternative measuring techniques exist for the surveys [12][13][14][15][16][17][18][19][20]: these methods don't always match the needs of the application in emergencies, which include ease to use, potential for automation, ability to work at a safe distance, high acquisition and processing speeds, high accuracy and resolution. Given that it satisfies the requirements outlined [21], the TLS seems appropriate for usage in emergency situations. ...
... Reference [80] showed the effectiveness of airborne LiDAR DEM in mapping and differentiating aspects of the complex tectonic geomorphology of the Meilongshan Fault in the densely forested areas of southern Taiwan. Reference [81] used terrestrial LiDAR DEM to construct a post-disaster building damage model using data from the Yushu, China, and Port-au-Prince, Haiti, earthquakes that both occurred in 2010. Reference [82] combined DInSAR and LiDAR to create a robust 3D coseismic displacement map for the Mw 6.9 earthquake that occurred in Fukushima, Japan in 2011. ...
... Several further methods of improving damage detection from satellite images were covered in the literature. Reference [81] proposed a novel method based on multiscale adaptive feature fusion, which detects damage using textual heterogeneity. Reference [178] refined the existing You Only Look Once, version 3 (YOLOv3) object detection method and was successfully applied to collapsed building detection. ...
The level of destruction caused by an earthquake depends on a variety of factors, such as magnitude, duration, intensity, time of occurrence, and underlying geological features, which may be mitigated and reduced by the level of preparedness of risk management measures. Geospatial technologies offer a means by which earthquake occurrence can be predicted or foreshadowed; managed in terms of levels of preparation related to land use planning; availability of emergency shelters, medical resources, and food supplies; and assessment of damage and remedial priorities. This literature review paper surveys the geospatial technologies employed in earthquake research and disaster management. The objectives of this review paper are to assess: (1) the role of the range of geospatial data types; (2) the application of geospatial technologies to the stages of an earthquake; (3) the geospatial techniques used in earthquake hazard, vulnerability, and risk analysis; and (4) to discuss the role of geospatial techniques in earthquakes and related disasters. The review covers past, current, and potential earthquake-related applications of geospatial technology, together with the challenges that limit the extent of usefulness and effectiveness. While the focus is mainly on geospatial technology applied to earthquake research and management in practice, it also has validity as a framework for natural disaster risk assessments, emergency management, mitigation, and remediation, in general.
... Zhang et al. [19] proposed a method of automatically extracting house damage information from post-quake high resolution optical remote sensing imagery using the multiscale fusion of spectral and textural features. In their experiment, they first enhanced the textural and spectral features of the images at the pixel level. ...
... 3D reconstruction techniques of buildings, based on Unmanned Aerial Vehicle (UAV) tilt photography, have the advantages of multi-angle and three-dimensional, but they are very time-consuming and high levels of accuracy are not easy to achieve [18]- [21]. Approaches are proposed, for identifying intact and collapsed buildings via multi-scale morphological profiles with multi-structuring elements from post-earthquake satellite imagery, or using Synthetic Aperture Radar (SAR) techniques; however, these methodologies do not examine in detail the structural characteristics of individual buildings [22]- [24]. ...
This work would like to provide a preliminary contribution to the draft of standard procedures for the adoption of Total Stations by rescuers in emergency situations, so as to offer a reliable and effective support to their assessment activities. In particular, some considerations will be made regarding the effect of the number and positioning of monitoring points on the tilt determination of a building façade, in order to set up simplified procedures, which are quick and easy to implement in emergency situations, at the same time guaranteeing the reliability of the results. Two types of building will be taken into account as test cases, which have different characteristics in terms of height, distance and angle with respect to the Total Station. Some considerations will be made about the aspects to be explored in future work, for the calibration of the method as a whole and the definition of all the steps of a procedure for the evaluation of the safety of a building.
... For example, building shadow information extracted from single high-resolution optical images has been used for the detection of buildings [11,12] and estimation of building height [13][14][15][16][17][18][19][20]. Change detection techniques at pixel and object levels have been used to detect buildings exhibiting significant radiometric, texture, and/or geometric differences between pre-and post-event image acquisitions [1,[21][22][23][24]. It should be noted that the utilization of shadow changes is complicated by other factors that impact shadow appearance, namely, differences in solar viewing elevation and azimuth of the two images in question. ...
After a major earthquake in a dense urban area, the spatial distribution of heavily damaged buildings is indicative of the impact of the event on public safety. Timely assessment of the locations of severely damaged buildings and their damage morphologies using remote sensing approaches is critical for search and rescue actions. Detection of damaged buildings that did not suffer collapse can be highly challenging from aerial or satellite optical imagery, especially those structures with height-reduction or inclination damage and apparently intact roofs. A key information cue can be provided by a comparison of predicted building shadows based on pre-event building models with shadow estimates extracted from post-event imagery. This paper addresses the detection of damaged buildings in dense urban areas using the information of building shadow changes based on shadow simulation, analysis, and image processing in order to improve real-time damage detection and analysis. A novel processing framework for the rapid detection of damaged buildings without collapse is presented, which includes (a) generation of building digital surface models (DSMs) from pre-event LiDAR data, (b) building shadow detection and extraction from imagery, (c) simulation of predicted building shadows utilizing building DSMs, and (d) detection and identification of shadow areas exhibiting significant pre- and post-event differences that can be attributed to building damage. The framework is demonstrated through two simulated case studies. The building damage types considered are those typically observed in earthquake events and include height-reduction, over-turn collapse, and inclination. Total collapse cases are not addressed as these are comparatively easy to be detected using simpler algorithms. Key issues are discussed including the attributes of essential information layers and sources of error influencing the accuracy of building damage detection.
