Searching for the built environment

Clustering built environment typologies to find spatial patterns and areas of deprivation using remote sensing techniques

Master thesis (2023)

Authors

S.A. Olde Technology, Policy and Management

Contributors

T. Verma Policy Analysis - TPM (supervisor 1)
S. van Cranenburgh Transport and Logistics - TPM (supervisor 2)
N.Y. Aydin System Engineering - TPM (supervisor 2)
Faculty
Technology, Policy and Management
Copyright: © 2023 Stephan Olde
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Published Date

27-02-2023

Language

English

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Faculty

Technology, Policy and Management

Abstract

This research uses high resolution satellite images in combination with an unsupervised Convolutional Neural Network Autoencoder to identify features that can be used to cluster different built environment typologies. Previous remote sensing research uses ground truth data which for some areas is not available or needs manually labeled training data. This research attempts to circumvent the issue of information scarcity in order to create a methodology that can be applied on any city as long as satellite images are available. From the resulting clusters, clusters can be selected which represent areas with high levels of deprivation which in turn can help identifying the deprived areas.