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Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning

Multimedia Understanding Group, Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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J. Imaging 2018, 4(11), 125; https://doi.org/10.3390/jimaging4110125
Received: 7 August 2018 / Revised: 2 October 2018 / Accepted: 18 October 2018 / Published: 23 October 2018
(This article belongs to the Special Issue Image Based Information Retrieval from the Web)
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Abstract

(1) Background: Evidence-based policymaking requires data about the local population’s socioeconomic status (SES) at detailed geographical level, however, such information is often not available, or is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View images, however, these methods are also resource-intensive, since they require large volumes of manually labeled training data. (2) Methods: We propose a methodology for automatically computing surrogate variables of SES indicators using street images of parked cars and deep multiple instance learning. Our approach does not require any manually created labels, apart from data already available by statistical authorities, while the entire pipeline for image acquisition, parked car detection, car classification, and surrogate variable computation is fully automated. The proposed surrogate variables are then used in linear regression models to estimate the target SES indicators. (3) Results: We implement and evaluate a model based on the proposed surrogate variable at 30 municipalities of varying SES in Greece. Our model has R 2 = 0.76 and a correlation coefficient of 0.874 with the true unemployment rate, while it achieves a mean absolute percentage error of 0.089 and mean absolute error of 1.87 on a held-out test set. Similar results are also obtained for other socioeconomic indicators, related to education level and occupational prestige. (4) Conclusions: The proposed methodology can be used to estimate SES indicators at the local level automatically, using images of parked cars detected via Google Street View, without the need for any manual labeling effort. View Full-Text
Keywords: deep learning; multiple instance learning; weakly supervised learning; demography; socioeconomic analysis; Google Street View deep learning; multiple instance learning; weakly supervised learning; demography; socioeconomic analysis; Google Street View
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Diou, C.; Lelekas, P.; Delopoulos, A. Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning. J. Imaging 2018, 4, 125.

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