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.
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Barrile, V., Bilotta, G.: Metodologie “Strutturali” su immagini Satellitari per l’analisi Urbana e Territoriale. In: Atti della XI Conferenza Nazionale ASITA, pp. 267–272. ASITA, Torino (2007)
Small, C.: Multiresolution analysis of urban reflectance. In: IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, pp. 15–19. IEEE, Rome (2001)
Pesaresi, M.: Texture analysis for urban pattern recognition using fine-resolution panchromatic satellite imagery. Geogr. Environ. Model. 4, 43–63 (2000)
Benediktsson, J.A., Pesaresi, M., Arnason, K.: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans. Geosci. Remote Sens. 41, 1940–1949 (2003)
Köppen, M., Ruiz-del-Solar, J., Soille, P.: Texture segmentation by biologically-inspired use of neural networks and mathematical morphology. In: Proceedings of the International ICSC/IFAC Symposium on Neural Computation, pp. 23–25. ICSC Academic Press, Wien (1998)
Serra, J.: Image Analysis and Mathematical Morphology. Theoretical Advances, vol. 2. Academic Press, New York (1998)
Bianchin, A., Pesaresi, M.: Approccio strutturale all’analisi di immagine per la descrizione del territorio: una esplorazione degli strumenti di morfologia matematica. Atti del V Convegno Nazionale A.I.T., pp. 25–29. AIT, Milano (1992)
Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M.: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 58, 239–258 (2004)
Barrile, V., Bilotta, G.: Object-oriented analysis applied to high resolution satellite data. WSEAS Trans. Signal Process. 4, 68–75 (2008)
Soille, P., Pesaresi, M.: Advances in mathematical morphology applied to geoscience and remote sensing. IEEE Trans. Geosci. Remote Sens. 40, 2042–2055 (2002)
Shackelford, A.K., Davis, C.H.: A hierarchical fuzzy classification approach for high resolution multispectral data over urban areas. IEEE Trans. Geosci. Remote Sens. 9, 1920–1932 (2003)
Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65, 2–16 (2010)
Barrile, V., Bilotta, G.: An application of object-oriented analysis to very high resolution satellite data on small cities for change detection. In: Proceedings of 3rd WSEAS Conference on Remote Sensing, pp. 98–103. WSEAS, Venice (2007)
Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991)
Zhang, Q., et al.: A new road extraction method using Sentinel-1 SAR images based on the deep fully convolutional neural network. Eur. J. Remote Sens. 52(1), 572–582 (2019). https://doi.org/10.1080/22797254.2019.1694447
Zhang, X., Zhang, C., Li, H., Luo, Z.: A road extraction method based on high resolution remote sensing image. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 International Conference on Geomatics in the Big Data Era (ICGBD), Guilin, Guangxi, China, 15–17 November 2019, vol. XLII-3/W10 (2019)
Bilotta, G.: Metodologie avanzate applicate allo studio dell’uso della terra. Cartographica 12, 21–24 (2005)
Barrile, V., Armocida, G., Bilotta, G.: Sistema integrato per il rilievo e la gestione del catasto delle aree incendiate. In: Atti della XII Conferenza Nazionale ASITA, pp. 293–298. ASITA, L’Aquila (2008)
Barrile, V., Bilotta, G., Meduri, G. M.: Individuazione di discariche mediante segmentazione del dato satellitare. In: Atti della XVI Conferenza Nazionale ASITA, pp. 137–142. ASITA, Vicenza (2012)
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Barrile, V., Bilotta, G., Fotia, A., Bernardo, E. (2020). Road Extraction for Emergencies from Satellite Imagery. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_55
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