Abstract
This paper is focused on the improvement and further validation of a recently proposed approach for the joint use of radar satellite imagery of an area affected by a major disaster and ancillary data. The study was carried out at different sites on imagery of two different earthquakes occurred one in the Mediterranean coast of Algeria on May 21st, 2003, which severely affected the city of Boumerdes, and one in the Pacific Coast of Peru on August, 15th, 2007. The combination of different radar‐extracted features results in very fuzzy classification of the damage patterns, far less detailed than what available using optical imagery. However, focused results using the above‐mentioned ancillary data provide enough detail and precision to be comparable with them. In particular, quantized damage level at the block level is achieved at enough detail using ALOS/PALSAR data and thus validates the original idea.
Highlights
One of the most important issues in disaster damage detection is time, and map timeliness is as important as precision
This paper is focused on the improvement and further validation of a recently proposed approach for the joint use of radar satellite imagery of an area affected by a major disaster and ancillary data
The need of rapid damage pattern estimate requires information extraction about damages using quick and possibly efficient approaches suited to the decided spatial scale and the available data
Summary
One of the most important issues in disaster damage detection is time, and map timeliness is as important as precision. As a consequence of that, every kind of data available is used in order to provide information to emergency operators [1] To this aim, remotely sensed imagery can be instrumental, as well as geographical information system (GIS) layers, printed maps, and historical datasets. SAR data are becoming widely available with more and more fine spatial resolution, and with larger usability for urban area management. This improvement allows a better match between the growing requests for focused analysis in these areas (due to the concentration of population) with the enhanced availability of dataset. Approaches to disaster management in urban areas using SAR data are very limited, due to the problems in data interpretation and the lack of automated or semiautomated tools
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