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  • 1
    Publication Date: 2017-04-04
    Description: Remote sensing sensors for detecting urban damage and other surface changes due to earthquakes is gaining increasing interest. To this aim optical images can represent useful tools for this application thanks their very high ground geometric resolution, especially when more frequent revisit times will be feasible with the implementation of new missions and future possible constellations of satellites. Sub-meter resolution images at visible frequencies are able to provide information at the scale of a single building. This kind of information is extremely important if provided with sufficient timeliness to rescue teams. In this work, the earthquake that hit the ancient city of Bam, Iran, on December 26th, 2003 has been investigated. The urban area was very close to the epicenter of the seism which caused strong damage to the urban structures. Pre- and post-earthquake Quickbird panchromatic images have been used to show the capability of this data to map damage at building scale by means of segmentation approach based on the application of morphological operators. A validation process has been performed by comparing the map of damage levels at single building scale with a detailed ground-based damage map provided by in situ survey.
    Description: Published
    Description: Boston, Massachusetts, USA
    Description: 1.10. TTC - Telerilevamento
    Description: reserved
    Keywords: very high hesolution ; classification ; damage detection ; earthquake ; 04. Solid Earth::04.06. Seismology::04.06.10. Instruments and techniques
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Conference paper
    Location Call Number Limitation Availability
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  • 2
    Publication Date: 2017-04-04
    Description: In this paper, we investigate the performance of pulse-coupled neural networks (PCNNs) to detect the damage caused by an earthquake. PCNN is an unsupervised model in the sense that it does not need to be trained, which makes it an operational tool during crisis events when it is crucial to produce damage maps as soon as the post-event images are available. The damage map resulting from PCNN was validated at a block scale of 120x120m using ground truth obtained by a combination of ground survey and visual inspection of the before- and after-event images. The comparison showed agreement between the change measured by PCNN on block scale and the damage occurred.
    Description: Published
    Description: Honolulu, Hawaii, USA
    Description: 1.10. TTC - Telerilevamento
    Description: reserved
    Keywords: neural networks ; damage detection
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Conference paper
    Location Call Number Limitation Availability
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