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 neighborhoods. 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 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.
Integration of EO-based vulnerability estimation into EO-based seismic damage assessment: a case study on L’Aquila, Italy, 2009 earthquake
DELL'ACQUA, FABIO;LANESE, IGOR;POLLI, DIEGO ALDO
2013-01-01
Abstract
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 neighborhoods. 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 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
