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Automatic Mapping of Physical Urban Problems Using Remotely Sensed Imagery
Abstract
While big cities are expected to exercise cost-effective, evidence-based planning, many are under reactive management, facing simultaneous problems and limited resources. This project develops a proof-of-concept workflow for the automatic monitoring of physical urban problems by combining remote sensing for detection and cartography for visualization. The example problem treated was the obstructive parking of vehicles on pavements as proxy for restricted urban mobility. Nine aerial images of UK urban areas were processed by a deep learning object detector of standard cars, achieving an F-score of 70.72%. Two large scale map reports of 200m wide areas were produced, featuring car detections and overlaps with topographic mapping features. Complementary analysis included the calculation of total detection window overlap per roadside pavement and its change with time. The proposed method combines uniform city-wide coverage with fast interpretation and can inspire the development of professional urban planning tools.
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