Earthquake damage mapping by using remotely sensed data: the Haiti case study

Abstract

Abstract. This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west-south-west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-auPrince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback-Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.

A multisensor approach for the 2016 Amatrice earthquake damage assessment

“…It is worth noticing that the damage classification (low, medium and high) is based on a relative scale. Romaniello et al (2016) have shown the correlation between this kind of damage classification and the collapse ratio, defined as the ratio between the number of collapsed buildings and the total number of buildings within a polygon. Therefore, high damage can indicate many collapses within a city block.…”

“…Previous studies, carried out during the European APhoRISM project, allowed us to identify the features having a good sensitivity to damage at object scale (Romaniello et al (2016)). Following the project outcomes, in the present work we used the NDI, and two quantities coming from the Information Theory, the KLD and the MI.…”

“…Each feature provides a qualitative assessment of the damage distribution within the town. In order to achieve a damage map useful in the rescue operations, the unsupervised algorithm FST (Romaniello et al (2016)) and based on these satellite features, has been employed; the scheme of the algorithm is shown in the Fig. 2.…”

Earthquake-Damaged Regions Detection From High Resolution Image Based on Super-Pixel Segmentation and Deep Learning

“…In recent decades, the devastating earthquake disaster has caused great loss to human life and property (Geiss and Taubenboeck, 2013;Romaniello et al 2017). It is very important to carry out post-earthquake disaster assessment to obtain the postearthquake disaster information timely and effectively (Romaniello et al 2017;Tong et al 2012). Building damage is often considered as the most important sign of post-earthquake disaster.…”

Combining TerraSAR-X and Landsat Images for Emergency Response in Urban Environments

“…More detailed information can be obtained via interferometric SAR (InSAR) data, which are based on the differences in the phase information of the returning signals of two temporal images [10]. To detect and assess the damage caused by natural disasters, most published studies use either InSAR coherence [11][12][13][14][15] or the SAR intensity correlation [3,16,17], or both [18][19][20]. The accurate mapping that is essential to the planning and execution of an emergency response to a natural disaster can be obtained by using very high resolution (VHR) SAR data, such as that generated by the TerraSAR-X satellite.…”