Climate change is increasing the number and the magnitude of wildfires, which become every year more severe. An accurate delineation of burned areas, which is often done through time consuming and inaccurate manual approaches, is of paramount importance to estimate the economic impact of such events. In this paper we introduce Burned Area Estimation through satellite tiles (BAE), an unsupervised algorithm that couples image processing techniques and an unsupervised neural network to automatically delineate the burned areas of wildfires from satellite imagery. We show its capabilities by performing an evaluation over past wildfires across European and non-European countries.

Unsupervised Burned Area Estimation through Satellite Tiles: A multimodal approach by means of image segmentation over remote sensing imagery / Farasin, Alessandro; Nini, Giovanni; Garza, Paolo; Rossi, Claudio. - ELETTRONICO. - 2466:(2019), pp. 1-10. ((Intervento presentato al convegno MACLEAN: MAChine Learning for EArth ObservatioN (workshop @ECML/PKDD2019) tenutosi a Wurzburg (DE) nel 20/09/2019.

Unsupervised Burned Area Estimation through Satellite Tiles: A multimodal approach by means of image segmentation over remote sensing imagery

Alessandro Farasin;Paolo Garza;Claudio Rossi
2019

Abstract

Climate change is increasing the number and the magnitude of wildfires, which become every year more severe. An accurate delineation of burned areas, which is often done through time consuming and inaccurate manual approaches, is of paramount importance to estimate the economic impact of such events. In this paper we introduce Burned Area Estimation through satellite tiles (BAE), an unsupervised algorithm that couples image processing techniques and an unsupervised neural network to automatically delineate the burned areas of wildfires from satellite imagery. We show its capabilities by performing an evaluation over past wildfires across European and non-European countries.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2750859