Automatic quantification of settlement damage using deep learning of satellite images

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dc.contributor.authorLu, Lili
dc.contributor.authorGuo, Weisi
dc.date.accessioned2021-10-27T14:02:21Z
dc.date.available2021-10-27T14:02:21Z
dc.date.issued2021-10-15
dc.description.abstractHumanitarian disasters and political violence cause significant damage to our living space. The reparation cost to homes, infrastructure, and the ecosystem is often difficult to quantify in real-time. Real-time quantification is critical to both informing relief operations, but also planning ahead for rebuilding. Here, we use satellite images before and after major crisis around the world to train a robust baseline Residual Network (ResNet) and a disaster quantification Pyramid Scene Parsing Network (PSPNet). ResNet offers robustness to poor image quality and can identify areas of destruction with high accuracy (92 %), whereas PSPNet offers contextualised quantification of built environment damage with good accuracy (84%). As there are multiple damage dimensions to consider (e.g. economic loss and fatalities), we fit a multi-linear regression model to quantify the overall damage. To validate our combined system of deep learning and regression modeling, we successfully match our prediction to the ongoing recovery in the 2020 Beirut port explosion. These innovations provide a better quantification of overall disaster magnitude and inform intelligent humanitarian systems of unfolding disasters.en_UK
dc.identifier.citationLu L, Guo W. (2021) Automatic quantification of settlement damage using deep learning of satellite images. In: 2021 IEEE international Smart Cities Conference (ISC2), 7-10 September 2021, Virtual Eventen_UK
dc.identifier.isbn978-1-6654-4920-5
dc.identifier.issn2687-8860
dc.identifier.urihttps://doi.org/10.1109/ISC253183.2021.9562867
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17211
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDeep learningen_UK
dc.subjectImage qualityen_UK
dc.subjectTechnological innovationen_UK
dc.subjectSatellitesen_UK
dc.subjectSmart citiesen_UK
dc.subjectPredictive modelsen_UK
dc.subjectReal-time systemsen_UK
dc.titleAutomatic quantification of settlement damage using deep learning of satellite imagesen_UK
dc.typeConference paperen_UK

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