mlsanityData to 7 Oct 2026

Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data

arXiv 2102.00848 ↗First posted 28 Jan 2021cs.CV cs.CY eess.IVcited by 4

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

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  5. 2019Towards Explainable Artificial Intelligencecited by 106
  6. 2019Geo-Aware Networks for Fine-Grained Recognitioncited by 44
  7. 2015Improving Image Classification with Location Contextcited by 33
  8. 2017Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scalecited by 23
  9. 2019Lightweight and Robust Representation of Economic Scales from Satellite Imagerycited by 15
  10. 2014The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the Citycited by 9
  11. 2014Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Datacited by 9
  12. 2017Using Deep Learning to Examine the Association between the Built Environment and Neighborhood Adult Obesity Prevalencecited by 8
3 more
  1. 2016The Death and Life of Great Italian Cities: A Mobile Phone Data Perspectivecited by 4
  2. 2014Measuring Urban Deprivation from User Generated Contentcited by 2
  3. 2019An unsupervised approach to Geographical Knowledge Discovery using street level and street network imagescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.

4 papers.