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A Deep-Learning Method for the Prediction of Socio-Economic Indicators from Street-View Imagery Using a Case Study from Brazil

Jeaneth Machicao, Alison Specht, Danton Vellenich, Leandro Meneguzzi, Romain David, Shelley Stall, Katia Ferraz, Laurence Mabile, Margaret O'Brien, Pedro Corrêa
2022 Data Science Journal  
The objective of this paper is to evaluate the assessment of socioeconomic indicators using street-view imagery, through a case study conducted in a region of Brazil, the Vale do Ribeira, one of the poorest  ...  A pre-trained convolutional neural network (CNN) was used to predict socio-economic indicators from GSV.  ...  Deps Miguel from IBGE for their valuable suggestions and support. J.M. is grateful for the support from FAPESP (grant 2020/03514-9).  ... 
doi:10.5334/dsj-2022-006 fatcat:arpdoypjo5brhjh5huq5neubqi

Evaluation of deep-learning methods to understand the prediction of socio-economic indicators from remote sensing imagery

Jeaneth MACHICAO, Robin JARRY, Danton Ferreira VELLENICH, Jean Pierre OMETTO, Katia FERRAZ, Nadya DEPS, Miguel Suarez Xavier PENTEADO, Shelley STALL, Alison SPECHT, Laurence MABILE, Marc CHAUMONT, Pedro CORRÊA (+1 others)
2020 Zenodo  
annotated with a socioeconomic indicator; and (3) use a simpler regression model to predict poverty measures from the corresponding CNN feature vector output.  ...  " (pre-trained model) consisting to train a CNN (convolutional neural network) using a large dataset of images, aiming to intensively learn the relationship between the input and their images annotations  ...  Our next steps Aim: to conduct a DL approach to predict SEc for selected areas in Brazil using satellite imagery.  ... 
doi:10.5281/zenodo.4280070 fatcat:44zsptccvjfojpopkvuheo6ery

Progress on PARSEC : Building New Tools for Data Sharing and Reuse through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas

Jeaneth Machicao
2022 Zenodo  
This poster was presented during the 'Feira de pesquisa da Semana de Recepção da POLI-USP" hold on 15/04/2022 at University of São Paulo, Brazil.  ...  A Deep-Learning Method for the Prediction of Socio-Economic Indicators from Street-View Imagery Using a Case Study from Brazil. Data Science Journal.  ...  ., Specht, A., Vellenich, D., Meneguzzi, L., David, R., Stall, S., Ferraz, K., Mabile, L., O'Brien, M., Corrêa, P., 2022.  ... 
doi:10.5281/zenodo.6366446 fatcat:2ktwumrzgze77olcsktblvzs5i

Compartilhamento e reuso de dados na gestão de Unidades de Conservação

2022 Zenodo  
This virtual meeting aims to bring to the scientific community discussions on topics involving e-Science issues and scientific data management.  ...  A Deep-Learning Method for the Prediction of Socio-Economic Indicators from Street-View Imagery Using a Case Study from Brazil. Data Science Journal, 21(1), p.6.  ...  Only some information/data have gridded at the regional scalePrediction results using Street view imagesMachicao, et al.  ... 
doi:10.5281/zenodo.6539628 fatcat:2yr7vljydvhpba6ndtywcvlunu

Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images

Paloma Merodio Gómez, Olivia Jimena Juarez Carrillo, Monika Kuffer, Dana R. Thomson, Jose Luis Olarte Quiroz, Elio Villaseñor García, Sabine Vanhuysse, Ángela Abascal, Isaac Oluoch, Michael Nagenborg, Claudio Persello, Patricia Lustosa Brito
2021 Sustainability  
We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions.  ...  The intersection of very rapid socio-economic and demographic dynamics are often insufficiently understood, and relevant data for understanding them are commonly unavailable, dated, or too coarse (resolution  ...  A deep transfer learning approach based on a CNN is used to capture the socio-economic variability.  ... 
doi:10.3390/su132212640 fatcat:wgxb5lv5mrgsrbyhd7ybmsoqsq

The 'Paris-end' of town? Urban typology through machine learning [article]

Kerry A. Nice, Jason Thompson, Jasper S. Wijnands, Gideon D.P.A. Aschwanden, Mark Stevenson
2019 arXiv   pre-print
The use of street view imagery will emphasise the features of a human scaled visual geography of streetscapes.  ...  Departing from a traditional 'top-down' analysis of urban design features, this project analyses millions of images of urban form (consisting of street view, satellite imagery, and street maps) to find  ...  Acknowledgements This project was made possible thanks to computer hardware purchased by the Transportation, Health References  ... 
arXiv:1910.03220v1 fatcat:yuwvthskrjdvnp6b4nvycbfibu

Panoramic Street-Level Imagery in Data-Driven Urban Research: A Comprehensive Global Review of Applications, Techniques, and Practical Considerations

Jonathan Cinnamon, Lindi Jahiu
2021 ISPRS International Journal of Geo-Information  
This paper provides the first comprehensive, state-of-the-art review on the use of street-level imagery for urban analysis in five research areas: built environment and land use; health and wellbeing;  ...  The release of Google Street View in 2007 inspired several new panoramic street-level imagery platforms including Apple Look Around, Bing StreetSide, Baidu Total View, Tencent Street View, Naver Street  ...  Similarly, a study by Gebru and colleagues [83] used a deep learning model to identify car year and make information from 50 million Google Street View images across 200 US cities, demonstrating how  ... 
doi:10.3390/ijgi10070471 fatcat:bpq4ivtmrfd4rebzotmzbif7ra

The "Paris-end" of Town? Deriving Urban Typologies Using Three Imagery Types

Kerry A. Nice, Jason Thompson, Jasper S. Wijnands, Gideon D. P. A. Aschwanden, Mark Stevenson
2020 Urban Science  
The use of street view imagery emphasises the features of a human-scaled visual geography of streetscapes.  ...  The comparison city of Paris is used as an exemplar and we perform a case study using two Australian cities, Melbourne and Sydney, to determine if a "Paris-end" of town exists or can be found in these  ...  In addition, it provides a high consistency for map imagery (except in the case of South Korea), and street view imagery is captured using a common methodology and equipment set.  ... 
doi:10.3390/urbansci4020027 fatcat:qjtu4omrdzhbbay77mbvgosk3e

Computer vision-based analysis of buildings and built environments: A systematic review of current approaches [article]

Małgorzata B. Starzyńska, Robin Roussel, Sam Jacoby, Ali Asadipour
2022 arXiv   pre-print
The growing body of research offers new methods to architectural and design studies, with the paper identifying future challenges and directions of research.  ...  First, to use or optimise computer vision methods for architectural image data, which can then help automate time-consuming, labour-intensive, or complex tasks of visual analysis.  ...  ACKNOWLEDGMENTS This work is supported by the Prosit Philosophiae Foundation.  ... 
arXiv:2208.00881v1 fatcat:g2sg4ixgl5f2bd2t3dlc2blzfm

Large-Scale Classification of Urban Structural Units from Remote Sensing Imagery

Jacob Arndt, Dalton Lunga
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines.  ...  Inspired by the lack of scalability and generalization capacity in urban structural units mapping, we extend the reach of deep learning and conduct a set of classification experiments in all 13 cities.  ...  The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines.  ... 
doi:10.1109/jstars.2021.3052961 fatcat:jnw4umtc5zbhfigoewj5pbnare

A Review of the Machine Learning in GIS for Megacities Application [chapter]

Nasim Tohidi, Rustam B. Rustamov
2020 Geographic Information Systems in Geospatial Intelligence [Working Title]  
Machine learning (ML) is very useful for analyzing data in many domains, including the satellite images processing.  ...  GIS (geographic information system) is equivalent to methods related to the use of geospatial information.  ...  In particular, a deep learning model, that had been trained with millions of human ratings of street-level imagery, was used to predict human perceptions of a street view image.  ... 
doi:10.5772/intechopen.94033 fatcat:ojnhohqyjvgktcp2jqnwjc6vdi

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Shiho Kino, Yu-Tien Hsu, Koichiro Shiba, Yung-Shin Chien, Carol Mita, Ichiro Kawachi, Adel Daoud
2021 SSM: Population Health  
Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists.  ...  The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data.  ...  Acknowledgment This study was supported by Grant-in-Aid for JSPS Fellows (JP20J01910).  ... 
doi:10.1016/j.ssmph.2021.100836 pmid:34169138 pmcid:PMC8207228 fatcat:azq5223ylzcbzjuccsfdnfo23u

A Survey on Societal Event Forecasting with Deep Learning [article]

Songgaojun Deng, Yue Ning
2021 arXiv   pre-print
This paper is dedicated to providing a systematic and comprehensive overview of deep learning technologies for societal event predictions.  ...  Then, we summarize data resources, traditional methods, and recent development of deep learning models for these problems.  ...  UCI machine learning repository [8] published a crime dataset for machine learning study, Communities and Crime 15 , which combines socio-economic data from the 1990 US Census, law enforcement data from  ... 
arXiv:2112.06345v1 fatcat:jtdlo67bbbazhj6xea55h6bbqa

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Baihua Liu, Yingbin Deng, Miao Li, Ji Yang, Tao Liu
2021 Applied Sciences  
We predict future trends of urban functional zone identification based on the reviewed literature. Our findings are expected to be valuable for urban studies.  ...  The identification methods and classification schemes are summarized from the existing research.  ...  Acknowledgments: We would like to thank the anonymous reviewers for their constructive comments.  ... 
doi:10.3390/app11219968 fatcat:5z57fnuycvhvrlm2hxm57slsq4

Satellite-based mapping of urban poverty with transfer learned slum morphologies

Thomas Stark, Michael Wurm, Xiaoxiang Zhu, Hannes Josef Taubenbock
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This article has been accepted for publication in a future issue of this journal, but has not been fully edited.  ...  It was also partly funded by the BMBF project inform@risk" (FZK: 03G0883C) The authors would like to thank Johannes Mast, who kindly provided the reference data for the city of Shenzhen, China.  ...  In cases where no official census data was available, or the ground truth data was outdated, the reference was created based on Bing aerial imagery and Google Street View images.  ... 
doi:10.1109/jstars.2020.3018862 fatcat:4vhtzuxxtfhkzhhqk57ifmftuy
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