SOURCE-LINKED INTELLIGENCE

SmarT sub-pixel URban flood Mapping from open earth observation and crowdsourcing

CORDIS · observation · Publication date unknown

ata can be expensive and inaccessible. Open RS represents a not fully exploited potential limited by the relatively coarse resolution. Moreover, recent advances in computer vision techniques based on deep learning (DL) and novel observational opportunities can provide valuable information on both flood extent and depth in combination with RS. STURM aims to advance urban flood knowledge by combining open RS Sentinel imagery and crowdsourcing (semantic and visual data) using DL with the ambition of overcoming the constraints of spatial resolution and limited information. STURM leverages free and newly available opportunistic observing systems providing a globally consistent, open-source-based, smart method for improved multi-source observations of hydroclimatic hazardous events in urban areas. The research objectives are to assess and accurately map urban flood extent and depth with enhanced spatial resolution (sub-pixel mapping and measurements from street-level images) and validate the methodology against real disaster events. STURM’s novel data fusion paradigm suits the demand to fi

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recordType

award

status

SIGNED

region

EU

value

184082

unit

EUR