Imperial College London
Portrait Picture

ProfessorSamirBhatt

Faculty of Medicine, School of Public Health

Professor of Statistics and Public Health
 
 
 
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Contact

 

+44 (0)20 7594 5029s.bhatt

 
 
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Location

 

G32ASt Mary's Research BuildingSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Suel:2021:10.1016/j.rse.2021.112339,
author = {Suel, E and Bhatt, S and Brauer, M and Flaxman, S and Ezzati, M},
doi = {10.1016/j.rse.2021.112339},
journal = {Remote Sensing of Environment: an interdisciplinary journal},
title = {Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas},
url = {http://dx.doi.org/10.1016/j.rse.2021.112339},
volume = {257},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Data collected at large scale and low cost (e.g. satellite and street level imagery) have the potential to substantially improve resolution, spatial coverage, and temporal frequency of measurement of urban inequalities. Multiple types of data from different sources are often available for a given geographic area. Yet, most studies utilize a single type of input data when making measurements due to methodological difficulties in their joint use. We propose two deep learning-based methods for jointly utilizing satellite and street level imagery for measuring urban inequalities. We use London as a case study for three selected outputs, each measured in decile classes: income, overcrowding, and environmental deprivation. We compare the performances of our proposed multimodal models to corresponding unimodal ones using mean absolute error (MAE). First, satellite tiles are appended to street level imagery to enhance predictions at locations where street images are available leading to improvements in accuracy by 20, 10, and 9% in units of decile classes for income, overcrowding, and living environment. The second approach, novel to the best of our knowledge, uses a U-Net architecture to make predictions for all grid cells in a city at high spatial resolution (e.g. for 3 m × 3 m pixels in London in our experiments). It can utilize city wide availability of satellite images as well as more sparse information from street-level images where they are available leading to improvements in accuracy by 6, 10, and 11%. We also show examples of prediction maps from both approaches to visually highlight performance differences.
AU  - Suel,E
AU  - Bhatt,S
AU  - Brauer,M
AU  - Flaxman,S
AU  - Ezzati,M
DO  - 10.1016/j.rse.2021.112339
PY  - 2021///
SN  - 0034-4257
TI  - Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas
T2  - Remote Sensing of Environment: an interdisciplinary journal
UR  - http://dx.doi.org/10.1016/j.rse.2021.112339
UR  - http://hdl.handle.net/10044/1/87597
VL  - 257
ER  -
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