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Graffiti and government in smart cities: a Deep Learning approach applied to Medellín City, Colombia

Published: 04 June 2021 Publication History

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Abstract

Graffiti is an element of graphic expression that manifests different states of the human being. However, for many governments worldwide, it has been an element of discord between them and the communities that express themselves through graffitis. This article proposes identifying graffiti and concentration zones through Computer Vision and object detection and localization to support public policy management in smart cities. ASUM-DM methodology is used to achieve the aim. Initially, the current problems faced by municipal governments in the management of public graffiti policy are identified. Then available datasets of images from Google Street View (GSV) and other acquired datasets are identified for the case study carried out in the city of Medellín (Colombia) and border municipalities. A training dataset of 1,395 images and a production dataset of 71,100 panoramas is placed on strictly using the experimental method of the division of training data, validation, and a production sample, to make a correct estimation of the generalization error. As a result of the training process, we obtained an Average Precision of 69,14%, which presented a high precision Tag of 89.23%, and low precision of 59.13% in Mural. Finally, it is possible to build heat maps of graffiti concentration areas that could guide rulers to create or improve public policies related to graffiti expression.

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Cited By

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  • (2025)Types and Effectiveness of Public Policy Measures Combatting Graffiti Vandalism at Heritage SitesHeritage10.3390/heritage80100188:1(18)Online publication date: 6-Jan-2025
  • (2023)Deep Learning-Based Graffiti Detection: A Study Using Images from the Streets of LisbonApplied Sciences10.3390/app1304224913:4(2249)Online publication date: 9-Feb-2023
  • (2022)Optimization of Deep Learning Neural Network by Analysis of Cross-Validated Metrics with and Without Data AugmentationAdvances in Computer Science for Engineering and Manufacturing10.1007/978-3-031-03877-8_22(248-259)Online publication date: 15-Apr-2022
  • Show More Cited By

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cover image ACM Other conferences
DATA'21: International Conference on Data Science, E-learning and Information Systems 2021
April 2021
277 pages
ISBN:9781450388382
DOI:10.1145/3460620
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 04 June 2021

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Author Tags

  1. Deep Learning
  2. Graffiti.
  3. Object Detection and Localization
  4. Public policies
  5. Smart cities

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DATA'21

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Overall Acceptance Rate 74 of 167 submissions, 44%

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Cited By

View all
  • (2025)Types and Effectiveness of Public Policy Measures Combatting Graffiti Vandalism at Heritage SitesHeritage10.3390/heritage80100188:1(18)Online publication date: 6-Jan-2025
  • (2023)Deep Learning-Based Graffiti Detection: A Study Using Images from the Streets of LisbonApplied Sciences10.3390/app1304224913:4(2249)Online publication date: 9-Feb-2023
  • (2022)Optimization of Deep Learning Neural Network by Analysis of Cross-Validated Metrics with and Without Data AugmentationAdvances in Computer Science for Engineering and Manufacturing10.1007/978-3-031-03877-8_22(248-259)Online publication date: 15-Apr-2022
  • (2021)Quantifying Urban Safety Perception on Street View ImagesIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3486622.3493975(611-616)Online publication date: 14-Dec-2021

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