The Combined Use of Airborne Remote Sensing Techniques within a GIS Environment for the Seismic Vulnerability Assessment of Urban Areas: An Operational Application

Abstract

Abstract:The knowledge of the topographic features, the building properties, and the road infrastructure settings are relevant operational tasks for managing post-crisis events, restoration activities, and for supporting search and rescue operations. Within such a framework, airborne remote sensing tools have demonstrated to be powerful instruments, whose joint use can provide meaningful analyses to support the risk assessment of urban environments. Based on this rationale, in this study, the operational benefits obtained by combining airborne LiDAR and hyperspectral measurements are shown. Terrain and surface digital models are gathered by using LiDAR data. Information about roads and roof materials are provided through the supervised classification of hyperspectral images. The objective is to combine such products within a geographic information system (GIS) providing value-added maps to be used for the seismic vulnerability assessment of urban environments. Experimental results are gathered for the city of Cosenza, Italy.

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

“…In addition, building roof offsets between adjacent buildings have been used as a proxy to detect pancake collapse, in which the building is characterized by an intact roof but collapsed floors [37]. Building material has been used for vulnerability [62,63] and resilience assessments [64][65][66]. Building material detection from RS data is typically based on the interpretation of rooftop colors from aerial or satellite images without considering other materials of building (e.g., building wall materials).…”

“…Vulnerability, Resilience VHR images, Urban map [61][62][63][64][65][66] Several proxies rely on geometric and morphological characteristics of built-up components to extract detailed information about structural deformations of buildings. They are used to compute the damage ratio of buildings.…”

Seismic vulnerability assessment at urban scale using data mining and GIScience technology: application to Urumqi (China)

“…Recently, simplified macro-scale methods have been adopted to reduce the costs of the in situ field surveys and improve the efficiency seismic vulnerability and risk assessments at the urban scale. Such initiatives seek to simplify the visual screening stage by considering only the key parameters whose contributions to the seismic vulnerability are significant (Gu eguen et al 2007) or by employing remote sensing and geographic information system (GIS) methods (M€ uck et al 2012;Wieland et al 2012;Geiß and Taubenb€ ock 2013;Kaushik and Dasgupta 2013;Geiß et al 2014;Moradi et al 2014;Geiß et al 2015a;Geiß et al 2015b;Su et al 2015;Costanzo et al 2016;Dhar et al 2016;Klotz et al 2016;Ghorbanzadeh et al 2017). Data mining methods have also been developed to ascertain the best proxy that links the features of building, which are easily assessed using remote sensing and civil engineering methods, with their seismic vulnerabilities (Şen 2010, 2011Chen et al 2012;Siraj et al 2014;Wu H et al 2014;Riedel et al 2015;Campostrini et al 2017;Ghorbanzadeh et al 2017;Guettiche et al 2017).…”

Estimación de exposición y vulnerabilidad sísmica empleando técnicas de aprendizaje automático con datos tomados por sensores remotos: aplicaciones en Lorca (España) y Puerto Príncipe (Haití)

“…La pregunta surge por las diferentes aproximaciones que se pueden encontrar en la literatura, donde la mayoría de los autores toman las muestras de manera aleatoria (e.g Geiss et al, 2016;Riedel et al, 2014;Mück et al, 2013) y muy pocos los hacen de forma manual (e.g Wieland et al, 2012a). Generalmente hacen numerosas pruebas de entrenamiento de modelos con números de muestras que van desde varias decenas a cientos (Matsuka et al, 2012;Costanzo et al, 2016), pero rara vez optan por modelos entrenados con pocas muestras. En este estudio, se han probado ocho datasets con diferente configuración en cuanto al número y tipo de muestras, al proceso de selección de estas y al equilibrio entre clases para clasificar los patrones urbanos en la estratificación de Puerto Príncipe (sección 5.2.2.1).…”

“…Authors usually compare the performance of several training datasets of different sizes (e.g. Matsuka et al, 2012;Costanzo et al, 2016), but small training sets are rarely preferred. In this study, eight datasets have been compared for the stratification of the study area in Port Prince (section 5.2.2.1).…”