After earthquakes, international and national organizations must overcome many challenges in rescue operations. Among these, the knowledge of the territory and of the roads is fundamental for international aid. The maps that volunteers make are a valuable asset, showing the roads in the area affected by the seismic events, a knowledge which is necessary to bring rescue. This was very helpful during many earthquakes as in Haiti (on 2010-01-12) and in Nepal (on 2015-04-25) to support the humanitarian organizations. Many volunteers can contribute remotely to mapping little known or inaccessible regions with crowdsourcing actions, by tracing maps from satellite imagery or aerial photographs even if staying far from the affected site. This research, still in progress, aims at experiencing quickly obtaining roads through the so-called Object Based Image Analysis (OBIA), by extracting it from satellite data, semi-automatically or automatically, with a segmentation that starts from concepts of Mathematical Morphology. We compared it with a classification in ENVI and, using an algorithm in GIS, we verified the goodness of the method. The good results obtained encourage further research on fast techniques for map integration for humanitarian emergencies moreover the results were implemented on open street map.

Road Extraction for Emergencies from Satellite Imagery / Barrile, V.; Bilotta, G.; Fotia, A.; Bernardo, E.. - 12252:(2020), pp. 767-781. ( 20th International Conference on Computational Science and Its Applications, ICCSA 2020 ita 2020) [10.1007/978-3-030-58811-3_55].

Road Extraction for Emergencies from Satellite Imagery

Barrile V.;Fotia A.;
2020-01-01

Abstract

After earthquakes, international and national organizations must overcome many challenges in rescue operations. Among these, the knowledge of the territory and of the roads is fundamental for international aid. The maps that volunteers make are a valuable asset, showing the roads in the area affected by the seismic events, a knowledge which is necessary to bring rescue. This was very helpful during many earthquakes as in Haiti (on 2010-01-12) and in Nepal (on 2015-04-25) to support the humanitarian organizations. Many volunteers can contribute remotely to mapping little known or inaccessible regions with crowdsourcing actions, by tracing maps from satellite imagery or aerial photographs even if staying far from the affected site. This research, still in progress, aims at experiencing quickly obtaining roads through the so-called Object Based Image Analysis (OBIA), by extracting it from satellite data, semi-automatically or automatically, with a segmentation that starts from concepts of Mathematical Morphology. We compared it with a classification in ENVI and, using an algorithm in GIS, we verified the goodness of the method. The good results obtained encourage further research on fast techniques for map integration for humanitarian emergencies moreover the results were implemented on open street map.
2020
Inglese
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
20th International Conference on Computational Science and Its Applications, ICCSA 2020
12252
767
781
15
978-3-030-58810-6
978-3-030-58811-3
Springer Science and Business Media Deutschland GmbH
GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
Esperti anonimi
2020
ita
Internazionale
Mathematical morphology
OBIA
Satellite imagery
Segmentation
No
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Barrile, V.; Bilotta, G.; Fotia, A.; Bernardo, E.
273
Road Extraction for Emergencies from Satellite Imagery / Barrile, V.; Bilotta, G.; Fotia, A.; Bernardo, E.. - 12252:(2020), pp. 767-781. ( 20th International Conference on Computational Science and Its Applications, ICCSA 2020 ita 2020) [10.1007/978-3-030-58811-3_55].
4
none
info:eu-repo/semantics/conferenceObject
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/121600
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