Prepoznavanje građevina pogođenih potresom temeljem korelacijske detekcije promjena obilježja teksture na SAR snimkama

The detection of building damage due to earthquakes is crucial for disaster management and disaster relief activities. Change detection methodologies using satellite images, such as synthetic aperture radar (SAR) data, have being applied in earthquake damage detection. Information contained within SAR data relating to earthquake damage of buildings can be disturbed easily by other factors. This paper presents a multitemporal change detection approach intended to identify and evaluate information pertaining to earthquake damage by fully exploiting the abundant texture features of SAR imagery. The approach is based on two images, which are constructed through principal components of multiple texture features. An independent principal components analysis technique is used to extract multiple texture feature components. Then, correlation analysis is performed to detect the distribution information of earthquake-damaged buildings. The performance of the technique was evaluated in the town of Jiegu (affected by the 2010 Yushu earthquake) and in the Kathmandu Valley (struck by the 2015 Nepal earthquake) for which the overall accuracy of building detection was 87.8% and 84.6%, respectively. Cross-validation results showed the proposed approach is more sensitive than existing methods to the detection of damaged buildings. Overall, the method is an effective damage detection approach that could support post-earthquake management activities in future events.Detekcija oštećenja građevina uzrokovanih potresom od presudne je važnosti za upravljanje rizicima od katastrofa i aktivnostima prilikom elementarnih nepogoda. Metodologije detekcije promjena, koristeći satelitske snimke kao što su podaci radara sa sintetičkim otvorom antene (SAR), korištene su u detekciji oštećenja od potresa. Informacije sadržane unutar SAR podataka, koje se odnose na oštećenja građevina uzrokovana potresom, mogu lako sadržavati šumove zbog drugih faktora. Ovaj rad prikazuje viševremenski pristup detekciji promjena kako bi se identificirale i procijenile informacije koje se odnose na oštećenja od potresa koristeći u potpunosti značajke teksture SAR snimaka. Pristup se temelji na dvije snimke koje su izrađene kroz glavne komponente višestrukih osobina tekstura. Neovisna analiza glavnih komponenti koristi se kako bi se izdvojile komponente višestrukih tekstura. Nakon toga provodi se korelacijska analiza kako bi se detektirale informacije o distribuciji građevina oštećenih potresom. Učinkovitost ove tehnike ispitana je u gradu Jiegu (kojega je 2010. godine pogodio potres Yushu) te u dolini Kathmandu (koju je 2015. godine pogodio potres Nepal), u kojoj je ukupna točnost detektiranja građevina bila 87,8%, odnosno 84,6%. Rezultati međusobne provjere valjanosti pokazali su da je predloženi pristup osjetljiviji od postojećih metoda za detektiranje oštećenih građevina. Općenito govoreći, metoda je učinkovit pristup detektiranja oštećenja koji može u budućnosti pružati potporu u aktivnostima upravljanja nakon potresa

earthquake damage mapping by using remotely sensed data the haiti case study

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-au-Prince, 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

Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap

Remote sensing data and methods are widely deployed in order to contribute to the assessment of numerous components of earthquake risk. While for earthquake hazardrelated investigations, the use of remotely sensed data is an established methodological element with a long research tradition, earthquake vulnerability–centred assessments incorporating remote sensing data are increasing primarily in recent years. This goes along with a changing perspective of the scientific community which considers the assessment of vulnerability and its constituent elements as a pivotal part of a comprehensive risk analysis. Thereby, the availability of new sensors systems enables an appreciable share of remote sensing first. In this manner, a survey of the interdisciplinary conceptual literature dealing with the scientific perception of risk, hazard and vulnerability reveals the demand for a comprehensive description of earthquake hazards as well as an assessment of the present and future conditions of the elements exposed. A review of earthquake-related remote sensing literature, realized both in a qualitative and quantitative manner, shows the already existing and published manifold capabilities of remote sensing contributing to assess earthquake risk. These include earthquake hazard-related analysis such as detection and measurement of lineaments and surface deformations in pre- and post-event applications. Furthermore, pre-event seismic vulnerability–centred assessment of the built and natural environment and damage assessments for post-event applications are presented. Based on the review and the discussion of scientific trends and current research projects, first steps towards a roadmap for remote sensing are drawn, explicitly taking scientific, technical, multi- and transdisciplinary as well as political perspectives into account, which is intended to open possible future research activities

Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions

Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace

Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions

Copyright © The Author(s) 2023. Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace.VM was supported by the Dutch Research Council (NWO), project OCENW.XS5.114. StEER and GHI Data collection was supported by the National Science Foundation (NSF) under Grant CMMI-1841667, the U.S. Geological Survey (USGS) and the U.S. Agency for International Development (USAID), under USGS Cooperative Agreement No. G21AC10343-00 and USAID Award AID-OFDA-T-16-00001, under lead investigator Janise Rodgers

Damage Proxy Map from Interferometric Synthetic Aperture Radar Coherence

A method, apparatus, and article of manufacture provide the ability to generate a damage proxy map. A master coherence map and a slave coherence map, for an area prior and subsequent to (including) a damage event are obtained. The slave coherence map is registered to the master coherence map. Pixel values of the slave coherence map are modified using histogram matching to provide a first histogram of the master coherence map that exactly matches a second histogram of the slave coherence map. A coherence difference between the slave coherence map and the master coherence map is computed to produce a damage proxy map. The damage proxy map is displayed with the coherence difference displayed in a visually distinguishable manner

Towards Automated Analysis of Urban Infrastructure after Natural Disasters using Remote Sensing

Natural disasters, such as earthquakes and hurricanes, are an unpreventable component of the complex and changing environment we live in. Continued research and advancement in disaster mitigation through prediction of and preparation for impacts have undoubtedly saved many lives and prevented significant amounts of damage, but it is inevitable that some events will cause destruction and loss of life due to their sheer magnitude and proximity to built-up areas. Consequently, development of effective and efficient disaster response methodologies is a research topic of great interest. A successful emergency response is dependent on a comprehensive understanding of the scenario at hand. It is crucial to assess the state of the infrastructure and transportation network, so that resources can be allocated efficiently. Obstructions to the roadways are one of the biggest inhibitors to effective emergency response. To this end, airborne and satellite remote sensing platforms have been used extensively to collect overhead imagery and other types of data in the event of a natural disaster. The ability of these platforms to rapidly probe large areas is ideal in a situation where a timely response could result in saving lives. Typically, imagery is delivered to emergency management officials who then visually inspect it to determine where roads are obstructed and buildings have collapsed. Manual interpretation of imagery is a slow process and is limited by the quality of the imagery and what the human eye can perceive. In order to overcome the time and resource limitations of manual interpretation, this dissertation inves- tigated the feasibility of performing fully automated post-disaster analysis of roadways and buildings using airborne remote sensing data. First, a novel algorithm for detecting roadway debris piles from airborne light detection and ranging (lidar) point clouds and estimating their volumes is presented. Next, a method for detecting roadway flooding in aerial imagery and estimating the depth of the water using digital elevation models (DEMs) is introduced. Finally, a technique for assessing building damage from airborne lidar point clouds is presented. All three methods are demonstrated using remotely sensed data that were collected in the wake of recent natural disasters. The research presented in this dissertation builds a case for the use of automatic, algorithmic analysis of road networks and buildings after a disaster. By reducing the latency between the disaster and the delivery of damage maps needed to make executive decisions about resource allocation and performing search and rescue missions, significant loss reductions could be achieved

Remote Sensing for Natural or Man-made Disasters and Environmental Changes

Disasters can cause drastic environmental changes. A large amount of spatial data is required for managing the disasters and to assess their environmental impacts. Earth observation data offers independent coverage of wide areas for a broad spectrum of crisis situations. It provides information over large areas in near-real-time interval and supplementary at short-time and long-time intervals. Therefore, remote sensing can support disaster management in various applications. In order to demonstrate not only the efficiency but also the limitations of remote sensing technologies for disaster management, a number of case studies are presented, including applications for flooding in Germany 2013, earthquake in Nepal 2015, forest fires in Russia 2015, and searching for the Malaysian aircraft 2014. The discussed aspects comprise data access, information extraction and analysis, management of data and its integration with other data sources, product design, and organisational aspects