{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-city-safety-perception-based-on","title":"Predicting city safety perception based on visual image content","arxiv_id":"1902.06871","date":"2019-02-19","proceeding":"arXiv 2019 2","authors":["Sergio Acosta","Jorge E. Camargo"],"abstract":"Safety perception measurement has been a subject of interest in many cities\nof the world. This is due to its social relevance, and to its effect on some\nlocal economic activities. Even though people safety perception is a subjective\ntopic, sometimes it is possible to find out common patterns given a restricted\ngeographical and sociocultural context. This paper presents an approach that\nmakes use of image processing and machine learning techniques to detect with\nhigh accuracy urban environment patterns that could affect citizen's safety\nperception.","url_abs":"http://arxiv.org/abs/1902.06871v1","url_pdf":"http://arxiv.org/pdf/1902.06871v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-city-safety-perception-based-on","repo_url":"https://github.com/sergiojx/uspBogota2018","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/safety-perception-recognition-on-google","task":"Safety Perception Recognition","dataset":"Google Street Images","model":"CNN","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"81%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}