University of Twente Student Theses


Characterisation of built-up area using artificial intelligence and open-source data for assessment of hazard exposure

Bhuyan, Kushanav (2021) Characterisation of built-up area using artificial intelligence and open-source data for assessment of hazard exposure.

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Abstract:Accurate elements-at-risk data (EaR) are one of the most important components to estimate the loss to both natural and anthropogenic hazards, particularly because of the potential increased exposure to these hazards, due to rapid urbanisation and poorly planned development strategies in hazardous regions. Therefore, it is important to not only map elements-at-risk but also to characterise them with attributes that are relevant for risk assessment. Mapping of building EaR includes the footprint information and their characteristics; however, acquiring them is difficult because of the following factors: lack of data accessibility, missing attribute data of buildings, data incompleteness and positional accuracy error, and many others. Major developments have taken place in the collaborative mapping of buildings, using platforms like OpenStreetMap. However, many areas in the world still lack this data. Therefore, the mapping of buildings footprints and their conversion into usable EaR maps is a challenge. To address these issues, we designed a semi-automated workflow that caters to the development of buildings EaR database by (1) detecting buildings footprints using a ResU-Net deep learning (DL) model and (2) characterising the footprints using building morphological metrics and open-source auxiliary data at a homogeneous block level. Based on our results, the building EaR footprints were detected with over 76% F1-score using the DL model and later classify them into building occupancy types like residential, commercial, industrial etc. Another major investigation that we examined is the transferability of the workflow in a different study area, which addresses the reproducibility of the method. After obtaining the final building EaR maps, we assessed the exposure of the building EaR by spatially overlaying the EaR maps over the flood susceptibility maps to understand how the building function and the occupants are affected. Our study has a huge significance, chiefly in (1) generating a building EaR database in data-scarce regions as a first approach (which were previously not explored), (2) transferring the methodology over a different test area and achieving good results, and for future applications in (3) linking the building occupancy types to hazard vulnerability and the subsequent hazard risk, and (4) serving projects and policy developments of regions for risk assessment, disaster risk mitigation and risk reduction.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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