Object oriented assessment of damage due to natural disaster using very high resolution images

ISBN 978-1-4244-1211-2International audienceA building damage assessment method applied to the case of the earthquake of Bam is proposed in this paper. It uses two very high resolution images and focuses on the footprints of the buildings. The need of an accurate registration of the buildings is demonstrated; a registration method that improved the damage assessment is proposed. It allows a classification performance of the buildings among four damage grades up to 78%. The impact of a lower accuracy of the buildings roofs segmentation is evaluated; we show that it mainly leads to a decrease of the capacity to identify the partial damage on buildings

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Assessing Building Vulnerability to Tsunami Hazard Using Integrative Remote Sensing and GIS Approaches

Risk and vulnerability assessment for natural hazards is of high interest. Various methods focusing on building vulnerability assessment have been developed ranging from simple approaches to sophisticated ones depending on the objectives of the study, the availability of data and technology. In-situ assessment methods have been widely used to measure building vulnerability to various types of hazards while remote sensing methods, specifically developed for assessing building vulnerability to tsunami hazard, are still very limited. The combination of remote sensing approaches with in-situ methods offers unique opportunities to overcome limitations of in-situ assessments. The main objective of this research is to develop remote sensing techniques in assessing building vulnerability to tsunami hazard as one of the key elements of risk assessment. The research work has been performed in the framework of the GITEWS (German-Indonesian Tsunami Early Warning System) project. This research contributes to two major components of tsunami risk assessment: (1) the provision of infrastructure vulnerability information as an important element in the exposure assessment; (2) tsunami evacuation modelling which is a critical element for assessing immediate response and capability to evacuate as part of the coping capacity analysis. The newly developed methodology is based on the combination of in-situ measurements and remote sensing techniques in a so-called “bottom-up remote sensing approach”. Within this approach, basic information was acquired by in-situ data collection (bottom level), which was then used as input for further analysis in the remote sensing approach (upper level). The results of this research show that a combined in-situ measurement and remote sensing approach can be successfully employed to assess and classify buildings into 4 classes based on their level of vulnerability to tsunami hazard with an accuracy of more than 80 percent. Statistical analysis successfully revealed key spatial parameters which were regarded to link parameters between in-situ and remote sensing approach such as size, height, shape, regularity, orientation, and accessibility. The key spatial parameters and their specified threshold values were implemented in a decision tree algorithm for developing a remote sensing rule-set of building vulnerability classification. A big number of buildings in the study area (Cilacap city, Indonesia) were successfully classified into the building vulnerability classes. The categorization ranges from high to low vulnerable buildings (A to C) and includes also a category of buildings which are potentially suitable for tsunami vertical evacuation (VE). A multi-criteria analysis was developed that incorporates three main components for vulnerability assessment: stability, tsunami resistance and accessibility. All the defined components were configured in a decision tree algorithm by applying weighting, scoring and threshold definition based on the building sample data. Stability components consist of structure parameters, which are closely related to the building stability against earthquake energy. Building stability needs to be analyzed because most of tsunami events in Indonesia are preceded by major earthquakes. Stability components analysis was applied in the first step of the newly developed decision tree algorithm to evaluate the building stability when earthquake strikes. Buildings with total scores below the defined threshold of stability were classified as the most vulnerable class A. Such the buildings have a high probability of being damaged after earthquake events. The remaining buildings with total scores above the defined threshold of stability were further analyzed using tsunami components and accessibility components to classify them into the vulnerability classes B, C and VE respectively. This research is based on very high spatial resolution satellite images (QuickBird) and object-based image analysis. Object-based image analysis is was chosen, because it allows the formulation of rule-sets based on image objects instead of pixels, which has significant advantages especially for the analysis of very high resolution satellite images. In the pre-processing stage, three image processing steps were performed: geometric correction, pan-sharpening and filtering. Adaptive Local Sigma and Morphological Opening filter techniques were applied as basis for the subsequent building edge detection. The data pre-processing significantly increased the accuracy of the following steps of image classification. In the next step image segmentation was developed to extract adequate image objects to be used for further classification. Image classification was carried out by grouping resulting objects into desired classes based on the derived object features. A single object was assigned by its feature characteristics calculated in the segmentation process. The characteristic features of an object - which were grouped into spectral signature, shape, size, texture, and neighbouring relations - were analysed, selected and semantically modelled to classify objects into object classes. Fuzzy logic algorithm and object feature separation analysis was performed to set the member¬ship values of objects that were grouped into particular classes. Finally this approach successfully detected and mapped building objects in the study area with their spatial attributes which provide base information for building vulnerability classification. A building vulnerability classification rule-set has been developed in this research and successfully applied to categorize building vulnerability classes. The developed approach was applied for Cilacap city, Indonesia. In order to analyze the transferability of this newly developed approach, the algorithm was also applied to Padang City, Indonesia. The results showed that the developed methodology is in general transferable. However, it requires some adaptations (e.g. thresholds) to provide accurate results. The results of this research show that Cilacap City is very vulnerable to tsunami hazard. Class A (very vulnerable) buildings cover the biggest portion of area in Cilacap City (63%), followed by class C (28%), class VE (6%) and class B (3%). Preventive measures should be carried out for the purpose of disaster risk reduction, especially for people living in such the most vulnerable buildings. Finally, the results were applied for tsunami evacuation modeling. The buildings, which were categorized as potential candidates for vertical evacuation, were selected and a GIS approach was applied to model evacuation time and evacuation routes. The results of this analysis provide important inputs to the disaster management authorities for future evacuation planning and disaster mitigation

Urban damage assessment using multimodal QuickBird images and ancillary data: the Bam and the Boumerdes earthquakes

International audienceRemote sensing has proved its usefulness for the crisis mitigation through situation report and damage assessment. Visual analysis of satellite images is conducted by analysts, however automatic or decision aid method are desired. We propose a semi-automatic damage assessment method based on a pair of very high spatial resolution (VHR) images and some ancillary data. It is applied to two disaster cases, for which the QuickBird images acquisition conditions differ. For each case, the two images also have very different viewing and illumination angles. Hence their comparison requires a preliminary registration; an automatic method adapted to VHR images is described. Then several change features are extracted from the buildings, and their relevance to assess damage on buildings is evaluated. Some textural features allow a damage assessment, but correlation coefficients are more efficient. Finally, a step toward the full automation of the method is done, skipping the supervision step of the classification process. We show the robustness of the global approach for both disaster cases with average performances closed to 75 % when 4 damage classes are discriminated, up to 90 % for a intact/damaged detection

Semi-automated workflow for natural disaster assessment : a case study of Banda Aceh, Indonesia

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe past decade has witnessed many natural disasters hitting highly populated areas causing billions of dollars in damage as well as many human casualties. During natural disasters, when attaining ground measurements are limited, remote sensing and geographical information systems (GIS) are useful tools for in-depth analysis of the affected area. This report will introduce a new semi-automatic workflow in which the road network will be used to break up the area into “blocks” and then zonal statistics will be applied to detect change based on the created blocks rather than the conventional methods of change detection; pixel by pixel and object oriented. This hybrid approach will take advantage of the simplicity and ease of applying pixel change detection methods on fixed objects or “blocks” to assess for damage. The change detection analysis results can then be used to map and quantify damage caused by natural disasters using pre and post Landsat imagery of the affected area. Multi-Criteria Analysis is performed on the damage map, proximity to roads, proximity to waterbodies and building size to find the most suitable locations for temporary housing sites. The image differencing of NDWI mean produced the highest overall accuracy of 71.70% among eleven bands/indices and the multi-criteria analysis successfully selected fourteen temporary housing center sites from a possible 114. When time is of essence with limited resources and GIS expertise on the field, local authorities can greatly benefit from a rapid generalized analysis that will provide a “bird-eye view” of the affected area to efficiently and effectively allocate emergency efforts within a short time frame

Assessment of high resolution SAR imagery for mapping floodplain water bodies: a comparison between Radarsat-2 and TerraSAR-X

Flooding is a world-wide problem that is considered as one of the most devastating natural hazards. New commercially available high spatial resolution Synthetic Aperture RADAR satellite imagery provides new potential for flood mapping. This research provides a quantitative assessment of high spatial resolution RADASAT-2 and TerraSAR-X products for mapping water bodies in order to help validate products that can be used to assist flood disaster management. An area near Dhaka in Bangladesh is used as a test site because of the large number of water bodies of different sizes and its history of frequent flooding associated with annual monsoon rainfall. Sample water bodies were delineated in the field using kinematic differential GPS to train and test automatic methods for water body mapping. SAR sensors products were acquired concurrently with the field visits; imagery were acquired with similar polarization, look direction and incidence angle in an experimental design to evaluate which has best accuracy for mapping flood water extent. A methodology for mapping water areas from non-water areas was developed based on radar backscatter texture analysis. Texture filters, based on Haralick occurrence and co-occurrence measures, were compared and images classified using supervised, unsupervised and contextual classifiers. The evaluation of image products is based on an accuracy assessment of error matrix method using randomly selected ground truth data. An accuracy comparison was performed between classified images of both TerraSAR-X and Radarsat-2 sensors in order to identify any differences in mapping floods. Results were validated using information from field inspections conducted in good conditions in February 2009, and applying a model-assisted difference estimator for estimating flood area to derive Confidence Interval (CI) statistics at the 95% Confidence Level (CL) for the area mapped as water. For Radarsat-2 Ultrafine, TerraSAR-X Stripmap and Spotlight imagery, overall classification accuracy was greater than 93%. Results demonstrate that small water bodies down to areas as small as 150m² can be identified routinely from 3 metre resolution SAR imagery. The results further showed that TerraSAR-X stripmap and spotlight images have better overall accuracy than RADARSAT-2 ultrafine beam modes images. The expected benefits of the research will be to improve the provision of data to assess flood risk and vulnerability, thus assisting in disaster management and post-flood recovery

Ontology-based semantic classification of satellite images: Case of major disasters

The International Charter 'Space and Major Disasters' is regularly activated during a catastrophic event and offers rescue teams comprehensive damage maps. Most of these maps are built by means of satellite image manual processing, which is often complex and demanding in terms of time and energy. Automatic processing supplies prompt treatment. Nevertheless, it usually presents a semantic gap handicap. The exploitation of ontologies to bridge the semantic gap has been widely recommended due to their quality of knowledge representation, expression, and discovery. In this work, we present an ontology-based semantic hierarchical classification method to undertake this problem. Ontology components are translated to image-based parameters and exploited to assist the classification process at two levels, and using 12 classes. The region of interest is selected from the first level and exhaustively analyzed and classified at the second level. The 2010 Haiti earthquake was selected as study area for this work. Experiments were performed using very high resolution multi-temporal QuickBird imagery and eCognition software

An object oriented approach for quantitative assessment of building damage in urban areas using very high resolution images

ISBN 1-4244-0712-5International audienceVery high resolution images are particularly well adapted to damage assessment methodology in urban area because on one hand it allows an analysis focused on the buildings solely through an object-oriented analysis, and on the other hand it permits a quantitative evaluation of this damage assessment using a visually established ground truth. We propose in this paper a method of damage assessment that uses these two benefits. First an original object oriented approach to register the images is presented. Then a simple and fast damage assessment method based on correlation is proposed and tested on the test-case of the earthquake of Bam in December 2003. Each building of a test-area is classified using Support Vector Machines. The performance of the method in each case is evaluated thanks to a manually constructed reference database that uses the European Macroseismic Scale. As a result, 75% of buildings are well classified among four different EMS damage grades