{"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/deep-learning-based-large-scale-automatic","title":"Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification","arxiv_id":"1706.09302","date":"2017-06-28","proceeding":null,"authors":["Rodrigo F. Berriel","Andre Teixeira Lopes","Alberto F. de Souza","Thiago Oliveira-Santos"],"abstract":"High-resolution satellite imagery have been increasingly used on remote\nsensing classification problems. One of the main factors is the availability of\nthis kind of data. Even though, very little effort has been placed on the zebra\ncrossing classification problem. In this letter, crowdsourcing systems are\nexploited in order to enable the automatic acquisition and annotation of a\nlarge-scale satellite imagery database for crosswalks related tasks. Then, this\ndataset is used to train deep-learning-based models in order to accurately\nclassify satellite images that contains or not zebra crossings. A novel dataset\nwith more than 240,000 images from 3 continents, 9 countries and more than 20\ncities was used in the experiments. Experimental results showed that freely\navailable crowdsourcing data can be used to accurately (97.11%) train robust\nmodels to perform crosswalk classification on a global scale.","url_abs":"http://arxiv.org/abs/1706.09302v2","url_pdf":"http://arxiv.org/pdf/1706.09302v2.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":"deep-learning-based-large-scale-automatic","repo_url":"https://github.com/rodrigoberriel/satellite-crosswalk-classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}