NDLI: Integrating X-SAR images and anthropic factors for fire susceptibility assessment

Content Provider	IEEE Xplore Digital Library
Author	Canale, S. De Santis, A. Iacoviello, D. Pirri, F. Sagratella, S.
Copyright Year	2011
Description	Author affiliation: Sapienza Università di Roma, Dipartimento di Informatica e Sistemistica, Italy (Canale, S.; De Santis, A.; Iacoviello, D.; Pirri, F.; Sagratella, S.)
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.
Starting Page	818
Ending Page	821
File Size	1528491
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049256
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Ignition Humans Data models Soil Google Data mining
Content Type	Text
Resource Type	Article

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