{"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/urban-perception-can-we-understand-why-a","title":"Urban Perception: Can we understand why a street is safe?","arxiv_id":null,"date":"2021-10-15","proceeding":"Advances in Computational Intelligence 2021 10","authors":["Felipe Moreno-Vera","Braham Lavi","Jorge Poco"],"abstract":"The importance of urban perception computing is relatively growing in machine learning, particularly in related areas to Urban Planning and Urban Computing. This field of study focuses on developing systems to analyze and map discriminant characteristics that might directly impact the city’s perception. In other words, it seeks to identify and extract discriminant components to define the behavior of a city’s perception. This work will perform a street-level analysis to understand safety perception based on the “visual components”. As our result, we present our experimental evaluation regarding the influence and impact of those visual components on the safety criteria and further discuss how to properly choose confidence on safe or unsafe measures concerning the perceptional scores on the city street levels analysis.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-030-89817-5_21","url_pdf":"https://fmorenovr.github.io/documents/papers/book_chapters/2021_MICAI.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":"urban-perception-can-we-understand-why-a","repo_url":"https://github.com/fmorenovr/Semi-Supervised-Learning_with_GAN_Keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"safety-perception-recognition","task_name":"Safety Perception Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/safety-perception-recognition-on-place-pulse","task":"Safety Perception Recognition","dataset":"Place Pulse 2.0","model":"ResNet151","rank_in_archive_order":1,"of":1,"metrics":{"AUC":"76.8"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}