Capturing deprived areas using unsupervised machine learning and open data: a case study in São Paulo, Brazil
Author:
Affiliation:
1. Urban and Regional Planning and Geo-Information Management, ITC, University of Twente, Enschede, The Netherlands
2. Urban and Regional Planning and Geo-Information Management, Federal University of Bahia, Salvador, Brazil
Funder
received no external
Publisher
Informa UK Limited
Subject
Applied Mathematics,Atmospheric Science,Computers in Earth Sciences,General Environmental Science
Link
https://www.tandfonline.com/doi/pdf/10.1080/22797254.2023.2214690
Reference66 articles.
1. “Domains of Deprivation Framework” for Mapping Slums, Informal Settlements, and Other Deprived Areas in LMICs to Improve Urban Planning and Policy: A Scoping Review
2. Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks
3. Alelyani, S., Tang, J., & Liu, H. (2014). Feature selection for clustering: A review. In Aggarwal, Charu C., Reddy, Chandan K. (Ed.), Data Clustering - Algorithms and Applications 1st ed., Chapman and Hall/CRC. 9781315373515 .
4. O desastre no cotidiano da favela: reflexões a partir de três casos no Rio de Janeiro [An everyday disaster in favela: reflections based on three cases in Rio de Janeiro];Bastos da Cunha M.;O Social Em Questão,2015
5. Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence
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