In this paper we present an integrated approach to COSMO-SkyMed image analysis and classification exploiting integration of different data of the regions of interest, namely urban forestry areas, wide urban parks. The aim is to provide a methodology for exploiting complex data structures built upon multi resolution grids gathering together with optical and X-SAR images, also historical land exploitation and meteorological data, records of human habits, and several other information sources. Although these data are specifically gathered to built a fire susceptibility map, the method is quite general. Indeed, the contribution of the model and its novelty relies manly on the definition of a learning schema lifting different factors and aspects of the event to be identified (here fire causes), including physical, social and behavioral ones, to the design of a fire susceptibility map, of a specific urban forestry. The outcome is an integrated geospatial database providing an infrastructure that merges cartography, heterogeneous data and complex analysis, in so establishing a digital environment where users and tools are interactively connected in an efficient and flexible way. © 2011 IEEE.
Integrating X-SAR images and anthropic factors for fire susceptibility assessment / Canale, Silvia; DE SANTIS, Alberto; Iacoviello, Daniela; PIRRI ARDIZZONE, Maria Fiora; Sagratella, Simone. - STAMPA. - (2011), pp. 818-821. (Intervento presentato al convegno 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 tenutosi a Vancouver, BC, Canada nel 24 July 2011 through 29 July 2011) [10.1109/igarss.2011.6049256].
Integrating X-SAR images and anthropic factors for fire susceptibility assessment
CANALE, Silvia;DE SANTIS, Alberto;IACOVIELLO, Daniela;PIRRI ARDIZZONE, Maria Fiora;SAGRATELLA, SIMONE
2011
Abstract
In this paper we present an integrated approach to COSMO-SkyMed image analysis and classification exploiting integration of different data of the regions of interest, namely urban forestry areas, wide urban parks. The aim is to provide a methodology for exploiting complex data structures built upon multi resolution grids gathering together with optical and X-SAR images, also historical land exploitation and meteorological data, records of human habits, and several other information sources. Although these data are specifically gathered to built a fire susceptibility map, the method is quite general. Indeed, the contribution of the model and its novelty relies manly on the definition of a learning schema lifting different factors and aspects of the event to be identified (here fire causes), including physical, social and behavioral ones, to the design of a fire susceptibility map, of a specific urban forestry. The outcome is an integrated geospatial database providing an infrastructure that merges cartography, heterogeneous data and complex analysis, in so establishing a digital environment where users and tools are interactively connected in an efficient and flexible way. © 2011 IEEE.File | Dimensione | Formato | |
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