A fuzzy decision making system for building damage map creation using high resolution satellite imagery

Abstract

Recent studies have shown high resolution satellite imagery to be a powerful data source for post-earthquake damage assessment of buildings. Manual interpretation of these images, while being a reliable method for finding damaged buildings, is a subjective and time-consuming endeavor, rendering it unviable at times of emergency. The present research, proposes a new state-of-the-art method for automatic damage assessment of buildings using high resolution satellite imagery. In this method, at the first step a set of pre-processing algorithms are performed on the images. Then, extracting a candidate building from both pre- and post-event images, the intact roof part after an earthquake is found. Afterwards, by considering the shape and other structural properties of this roof part with its pre-event condition in a fuzzy inference system, the rate of damage for each candidate building is estimated. The results obtained from evaluation of this algorithm using QuickBird images of the December 2003 Bam, Iran, earthquake prove the ability of this method for post-earthquake damage assessment of buildings

UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning

“…Conversely, they are capable of addressing damage holistically, i.e. in its entirety, as expert knowledge can be well matched to a given level of ambiguity and uncertainty (Rastiveis et al, 2013). Automatic approaches developed to date have struggled to deal with uncertainty inherent in damage assessment, although approaches such as by Rastiveis et al (2013), who explored fuzzy decision making approaches, or by Li et al (2010), who studied urban damage detection incorporating support vector machines and spatial relations, have been trying to overcome this limitation.…”

“…The challenges and importance of structural damage assessment, in particular its critical role in efficient post-disaster response, have placed this discipline in the spotlight of the remote sensing community (Rastiveis et al, 2013). The information generated is primarily used by search and rescue (SAR) teams but is also valuable for many other stakeholders engaged in post disaster activities, such as those dealing with estimation of economic loses, recovery, or reconstruction (Barrington et al, 2011).…”

A Comparison Among Different Optimization Levels In 3d Multi-Sensor Models. A Test Case in Emergency Context: 2016 Italian Earthquake

“…Due to their scale, their geometric configuration and ultimately their intrinsic features, satellite imaging does not satisfy the requirements of details and information. (Lemonie et al, 2013;Rastiveis et al, 2013;Gerke, & Kerle, 2011). The first data acquisition phase, to be done as quickly as possible after a disaster, is ordinarily carried out in person with manty efforts by technicians in damaged sites and is a heavy time-consuming operation.…”

Object-Oriented Analysis of Satellite Images Using Artificial Neural Networks for Post-Earthquake Buildings Change Detection

“…The visual method is mostly based on the study of manual sampling. This method is time-consuming and requires large-scale manual investigations, manpower, and other resources (Rastiveis, Samadzadegan, et al, 2013). Therefore, in recent years, accurate, fast and efficient automatic methods as pixel-based and object-oriented have been developed and proposed to derive change information from satellite images.…”

“…Samadzadegan et al, proposed a method for determining collapsed buildings in Bam, by extracting spectral and textural features and using pre and post-earthquake image and ancillary data (Samadzadegan, Zoj et al 2010). Rastiveis et al, proposed a method based on fuzzy inference system for generating damaged map by using pre and post high resolution Quickbird images and ancillary data (Rastiveis, Samadzadegan et al 2013).…”