{"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/skyscapes-fine-grained-semantic-understanding","title":"SkyScapes Fine-Grained Semantic Understanding of Aerial Scenes","arxiv_id":null,"date":"2019-10-01","proceeding":"ICCV 2019 10","authors":["Seyed Majid Azimi"," Corentin Henry"," Lars Sommer"," Arne Schumann"," Eleonora Vig"],"abstract":"Understanding the complex urban infrastructure with centimeter-level accuracy is essential for many applications from autonomous driving to mapping, infrastructure monitoring, and urban management. Aerial images provide valuable information over a large area instantaneously; nevertheless, no current dataset captures the complexity of aerial scenes at the level of granularity required by real-world applications. To address this, we introduce SkyScapes, an aerial image dataset with highly-accurate, fine-grained annotations for pixel-level semantic labeling. SkyScapes provides annotations for 31 semantic categories ranging from large structures, such as buildings, roads and vegetation, to fine details, such as 12 (sub-)categories of lane markings. We have defined two main tasks on this dataset: dense semantic segmentation and multi-class lane-marking prediction. We carry out extensive experiments to evaluate state-of-the-art segmentation methods on SkyScapes. Existing methods struggle to deal with the wide range of classes, object sizes, scales, and fine details present. We therefore propose a novel multi-task model, which incorporates semantic edge detection and is better tuned for feature extraction from a wide range of scales. This model achieves notable improvements over the baselines in region outlines and level of detail on both tasks.\r","url_abs":"http://openaccess.thecvf.com/content_ICCV_2019/html/Azimi_SkyScapes__Fine-Grained_Semantic_Understanding_of_Aerial_Scenes_ICCV_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ICCV_2019/papers/Azimi_SkyScapes__Fine-Grained_Semantic_Understanding_of_Aerial_Scenes_ICCV_2019_paper.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":[],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"edge-detection","task_name":"Edge Detection"},{"task_slug":"management","task_name":"Management"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-skyscapes-dense-1","task":"Semantic Segmentation","dataset":"SkyScapes-Dense","model":"SkyScapesNet-Dense","rank_in_archive_order":1,"of":7,"metrics":{"Mean IoU":"40.13"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-skyscapes-lane-1","task":"Semantic Segmentation","dataset":"SkyScapes-Lane","model":"SkyScapesNet-Lane","rank_in_archive_order":1,"of":2,"metrics":{"Mean IoU":"50.93"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}