An approach for detection of buildings and changes in buildings using orthophotos and point clouds: A case study of Van Erriş earthquake

Sarp, Gulcan; Erener, Arzu; Düzgün, Şebnem; Sahin, Kemal

doi:10.5721/eujrs20144735

Cited by 13 publications

(6 citation statements)

References 31 publications

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“…The increasing availability of open-source and automated LiDAR tools have also led to LiDAR's wider usage in the disaster risk studies. For instance, Sarp et al (2014) integrated high resolution orthophotos with a LiDAR-derived nDSM and conducted image classification to detect buildings and structural damages after the Van Erciş earthquake in Turkey. Similarly, Zhou, Gong, and Hu (2019) used LiDAR point clouds to detect building damages after a typhoon event.…”

Section: Lidar For Building Detectionmentioning

confidence: 99%

Gis-Based Rapid Earthquake Exposure and Vulnerability Mapping Using Lidar Dem and Machine Learning Algorithms: Case of Porac, Pampanga

Santos

Principe

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Disaster risk reduction and management (DRRM) not only requires a thorough understanding of hazards but also knowledge of how much built-up structures are exposed and vulnerable to a specific hazard. This study proposed a rapid earthquake exposure and vulnerability mapping methodology using the municipality of Porac, Pampanaga as a case study. To address the challenges and limitations of data access and availability in DRRM operations, this study utilized Light Detection and Ranging (LiDAR) data and machine learning (ML) algorithms to produce an exposure database and conduct vulnerability estimation in the study area. Buildings were delineated through image thresholding and classification of the normalized Digital Surface Model (nDSM) and an exposure database containing building attributes was created using Geographic Information System (GIS). ML algorithms such as Support Vector Machine (SVM), logistic regression, and Random Forest (RF) were then used to predict the model building type (MBT) of delineated buildings to estimate seismic vulnerability. Results showed that the SVM model yielded the lowest accuracy (53%) while logistic regression and RF models performed fairly (72% and 78% respectively) as indicated by their F-1 scores. To improve the accuracy of the exposure database and vulnerability estimation, this study recommends that the proposed building delineation process be further refined by experimenting with more appropriate thresholds or by conducting point cloud classification instead of pixel-based image classification. Moreover, ground truth MBT samples should be used as training data for MBT prediction. For future work, the methodology proposed in this study can be implemented when conducting earthquake damage assessments.

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Section: Lidar For Building Detectionmentioning

confidence: 99%

Gis-Based Rapid Earthquake Exposure and Vulnerability Mapping Using Lidar Dem and Machine Learning Algorithms: Case of Porac, Pampanga

Santos

Principe

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Deliverables of UAV surveys that include orthophotos, 3D point clouds, and digital surface models have wide applications. For example, orthophotos are very useful for manual or semi -automatic feature extraction for map creation or updating [11], as well as for change detection studies [11,12]. Urban change detection is important for city monitoring and disaster response, as well as the updating of maps and three -dimensional models [13,14].…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicles for Three‑dimensional Mapping and Change Detection Analysis

Gbopa

Ayodele

Okolie

et al. 2021

GaEE

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Unmanned Aerial Vehicles (UAVs), commonly known as drones are increasingly being used for three‑dimensional (3D) mapping of the environment. This study utilised UAV technology to produce a revised 3D map of the University of Lagos as well as land cover change detection analysis. A DJI Phantom 4 UAV was used to collect digital images at a flying height of 90 m, and 75% fore and 65% side overlaps. Ground control points (GCPs) for orthophoto rectification were coordinated with a Trimble R8 Global Navigation Satellite System. Pix4D Mapper was used to produce a digital terrain model and an orthophoto at a ground sampling distance of 4.36 cm. The change detection analysis, using the 2015 base map as reference, revealed a significant change in the land cover such as an increase of 16,306.7 m2 in buildings between 2015 and 2019. The root mean square error analysis performed using 7 GCPs showed a horizontal and vertical accuracy of 0.183 m and 0.157 m respectively. This suggests a high level of accuracy, which is adequate for 3D mapping and change detection analysis at a sustainable cost.

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“…Such datasets can provide the most accurate information about the geometric structural conditions of affected buildings [2,3]. Pairs of pre-and post-disaster DSMs, derived from stereo optical satellite images, have previously been exploited in this application [4][5][6]. The post-event data from highresolution multispectral and LiDAR sensors have been used in several tests of damaged building detection [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Rapid Detection of Damaged Buildings Using Pre-Event LiDAR Data and Shadow Change Information

2021

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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 detect 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.

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An approach for detection of buildings and changes in buildings using orthophotos and point clouds: A case study of Van Erriş earthquake

Cited by 13 publications

References 31 publications

Gis-Based Rapid Earthquake Exposure and Vulnerability Mapping Using Lidar Dem and Machine Learning Algorithms: Case of Porac, Pampanga

Gis-Based Rapid Earthquake Exposure and Vulnerability Mapping Using Lidar Dem and Machine Learning Algorithms: Case of Porac, Pampanga

Unmanned Aerial Vehicles for Three‑dimensional Mapping and Change Detection Analysis

A Novel Framework for Rapid Detection of Damaged Buildings Using Pre-Event LiDAR Data and Shadow Change Information

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