Machine learning for comparative urban planning at scale: An aviation case study

Abstract

Aviation is in flux, experiencing 5.4% yearly growth over the last two decades. However, with COVID-19 aviation was hard hit. This, along with its contribution to global warming, has led to louder calls to limit its use. This situation emphasizes how urban planners and technologists could contribute to understanding and responding to this change. This paper explores a novel workflow of performing image-based machine learning (ML) on satellite images of over 1.000 world airports that were algonthmically collated using European Space Agency Sentinel2 API. From these, the top 350 United States airports were analyzed with land use parameters extracted around the airport using computer vision, which were mapped against their passenger footfall numbers. The results demonstrate a scalable approach to identify how easy and beneficial it would be for certain airports to expand or contract and how this would impact the surrounding urban environment in terms of pollution and congestion. The generic nature of this workflow makes it possible to potentially extend this method to any large infrastructure and compare and analyze specific features across a large number of images while being able to understand the same feature through time. This is critical in answering key typology-based urban design challenges at a higher level and without needing to perform on-ground studies, which could be expensive and time-consuming.