Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data
Not on the map. Why?
Abstract (arXiv)
The presence of people in an urban area throughout the day -- often called 'urban vitality' -- is one of the qualities world-class cities aspire to the most, yet it is one of the hardest to achieve. Back in the 1970s, Jane Jacobs theorized urban vitality and found that there are four conditions required for the promotion of life in cities: diversity of land use, small block sizes, the mix of economic activities, and concentration of people. To build proxies for those four conditions and ultimately test Jane Jacobs's theory at scale, researchers have had to collect both private and public data from a variety of sources, and that took decades. Here we propose the use of one single source of data, which happens to be publicly available: Sentinel-2 satellite imagery. In particular, since the first two conditions (diversity of land use and small block sizes) are visible to the naked eye from satellite imagery, we tested whether we could automatically extract them with a state-of-the-art deep-learning framework and whether, in the end, the extracted features could predict vitality. In six Italian cities for which we had call data records, we found that our framework is able to explain on average 55% of the variance in urban vitality extracted from those records.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 62 references that are in the map. First posted on arXiv 28 Jan 2021.
Builds on
15 of 62 references.
References · 15
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Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2022Computer vision-based analysis of buildings and built environments: A systematic review of current approachescited by 3
- 2021Cross-Region Building Counting in Satellite Imagery using Counting Consistencycited by 2
- 2025Point of Interest Recommendation: Pitfalls and Viable Solutionscited by 0
- 2026UrbanWell: Benchmarking Multimodal Large Language Models for Spatio-Temporal Urban Wellbeing Analyticscited by 0
- 2026UrbanWell: Benchmarking Multimodal Large Language Models for Spatio-Temporal Urban Wellbeing Analytics14 Jun 2026
- 2025Point of Interest Recommendation: Pitfalls and Viable Solutions18 Jul 2025
- 2022Computer vision-based analysis of buildings and built environments: A systematic review of current approaches1 Aug 2022
- 2021Cross-Region Building Counting in Satellite Imagery using Counting Consistency26 Oct 2021