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| Titel |
Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery |
| VerfasserIn |
M. J. Lato, R. Frauenfelder, Y. Bühler |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 12, no. 9 ; Nr. 12, no. 9 (2012-09-13), S.2893-2906 |
| Datensatznummer |
250011100
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| Publikation (Nr.) |
 copernicus.org/nhess-12-2893-2012.pdf |
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| Zusammenfassung |
| Snow avalanches in mountainous areas pose a significant threat to infrastructure (roads, railways,
energy transmission corridors), personal property (homes) and recreational areas as well as for lives
of people living and moving in alpine terrain. The impacts of snow avalanches range from delays and
financial loss through road and railway closures, destruction of property and infrastructure, to loss of
life. Avalanche warnings today are mainly based on meteorological information, snow pack
information, field observations, historically recorded avalanche events as well as experience and
expert knowledge. The ability to automatically identify snow avalanches using Very High
Resolution (VHR) optical remote sensing imagery has the potential to assist in the development of accurate,
spatially widespread, detailed maps of zones prone to avalanches as well as to build up data bases of
past avalanche events in poorly accessible regions. This would provide decision makers with
improved knowledge of the frequency and size distributions of avalanches in such areas. We used an
object–oriented image interpretation approach, which employs segmentation and classification
methodologies, to detect recent snow avalanche deposits within VHR panchromatic optical remote
sensing imagery. This produces avalanche deposit maps, which can be integrated with other spatial
mapping and terrain data. The object-oriented approach has been tested and validated against
manually generated maps in which avalanches are visually recognized and digitized. The accuracy
(both users and producers) are over 0.9 with errors of commission less than 0.05. Future research is
directed to widespread testing of the algorithm on data generated by various sensors and
improvement of the algorithm in high noise regions as well as the mapping of avalanche paths
alongside their deposits. |
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