The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLVI-4/W6-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 125–132, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-125-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 125–132, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-125-2021

  18 Nov 2021

18 Nov 2021

GIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA

M. J. D. De Los Santos1 and J. A. Principe2 M. J. D. De Los Santos and J. A. Principe
  • 1National Graduate School of Engineering, University of the Philippines Diliman, Quezon City, Philippines
  • 2Dept. of Geodetic Engineering, University of the Philippines Diliman, Quezon City, Philippines

Keywords: LiDAR, building detection, earthquake, exposure and vulnerability, machine learning

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.