Diego Aldo Polli

SAR-Based Seismic Damage Assessment in Urban Areas: Scaling Down Resolution, Scaling Up Computational Performance

Earth Observation (EO) is becoming increasingly important in the support operations after a destructive event, as an earthquake can be. Remote sensing is proving to be very useful for identifying damage and planning support activities. Although the use of optical data because of easier visual interpretation is preferred-where possible-radar sensors can be a valuable tool for damage detection due to their extreme versatility and operability under all-weather conditions. The previous work of our research group has found a link between some selected texture measures computed on radar maps over single blocks of an urban area and the damage found in these neighborhoods. The study was conducted on 1-m resolution Spotlight images at high resolution (VHR) produced by COSMO-SkyMed, a valuable yet scarce resource. This paper reports on research aimed at probing whether and to what extent the method developed on 1-m resolution data can be transferred to more widely available, lower resolution ENVISAT ASAR data acquired in stripmap mode.

Earthquake damage assessment from post-event VHR radar data: From Sichuan, 2008 to Haiti, 2010

Earth Observation (EO) systems have demonstrated their usefulness in supporting intervention and relief actions in the event of extensive disasters. In the case of earthquakes, exploitation of EO data for damage assessment purposes has been proposed under various forms. Several EO-based damage maps have been presented after every event, mostly based on optical data and manual interpretation. Though, the use of weather-independent radar data and automated methods would significantly advance the exploitability of EO data in this context. Some interesting links have already been highlighted, between post-event texture measures and the damage averaged at block level, when Very High Resolution radar images of earthquake-stricken areas. With a record of at least three events, it is now time to wrap up some facts.

Integration of EO-based vulnerability estimation into EO-based seismic damage assessment: a case study on L’Aquila, Italy, 2009 earthquake

Remote sensing is proving very useful for identifying damage and planning support activities after an earthquake has stricken. Radar sensors increasingly show their value as a tool for damage detection, due to their shape-sensitiveness, their extreme versatility and operability, all weather conditions. The previous work of our research group, conducted on 1-m resolution spotlight images produced by COSMO-SkyMed, has led to the discovery of a link between some selected texture measures, computed on radar maps over single blocks of an urban area, and the damage found in these neighbourhoods. Texture-to-damage correlation was used to develop a SAR-based damage assessment method, but significant residual within-class variability makes estimations sometimes unreliable. Among the possible remedies, the injection of physical vulnerability data into the model was suggested. The idea here is to do so while keeping all the sources of data in the EO domain, by estimating physical vulnerability from the observation of high-resolution optical data on the area of interest. Although preliminary results seem to suggest that no significant improvement can be directly obtained on classification accuracy, there appears to be some link between estimated damage and estimated accuracy on which to build a more refined version of the damage estimator.

Mapping earthquake damage in VHR radar images of human settlements: Preliminary results on the 6 th April 2009, Italy case

Automated earthquake damage assessment from post-event only remotely sensed data is highly desirable, especially when new generation, Very High Resolution (VHR) spaceborne data is concerned, lacking extensive pre-event archives. Though, most damage assessment method either rely on human interpretation or on pre-post-event comparison. In this paper we illustrate some possible tracks for investigating damage assessment on post-event only data, focusing on the 6th April 2009 Abruzzi, Italy earthquake and on related COSMO/SkyMed acquisitions.

Fusion of Optical and SAR Data for Seismic Vulnerability Mapping of Buildings

Seismic risk depends not only on seismic hazard, but also on the vulnerability of exposed elements since it is important in providing the necessary information to policy and decision-makers in order to prevent and mitigate the loss in lives and property. Currently, the estimation of seismic vulnerability of buildings relies on accurate, complex models to be fed with large amounts of in situ data. A limited geographical scope is a natural consequence of such approach, while extensive assessment would be desirable when risk scenarios are concerned. Remote sensing might be fruitfully exploited in this case, if not for a gap between information required by current, accurate, data-hungry vulnerability models and information derivable from remotely sensed data. In this context, naturally the greatest amount of information should be collected, and data fusion is more a necessity than an option. Fusion between optical and radar data allows covering the widest range of information pieces; in this chapter we will describe how such information may be extracted and how it can be profitably fed to simplified seismic vulnerability models to assign a seismic vulnerability class to each building. Some examples of real cases will also be presented with a special focus on the test site of Messina, Italy, a notorious seismic-prone area, where an intensive campaign of data collection is in progress within our research group.

Collecting and Managing Building Data to Perform Seismic Risk Assessment - Palestine Case Study.

This paper describes a web interface with GIS functionality (WebGIS) that Eucentre (EUropean CENtre for Training and Research in Earthquake engineering) developed for SASPARM 2.0 (Support Action for Strengthening PAlestine capabilities for seismic Risk Mitigation) project [1].

Low-resolution urban area outlining in satellite SAR images

Urban areas represent a vital and highly dynamic environment, and monitoring their growth provides important input to decision and policy makers at large scale. Detection and outlining of urban areas from satellite sensors, though less precise, is faster than any on-site data collection. Various techniques have been proposed to classify urban areas, based on their typical features like textural features or backscatter intensity. In this paper a fuzzy-connectedness technique proposed for coastline detection, based on interferometric coherence and backscatter intensity, has been tested and adapted to urban area boundary detection. The modified method appear to be suitable for urban area extraction at ERS-like resolutions, for which long historical records are available allowing to reconstruct urban area growth in the past.