{"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/knowledge-and-topology-a-two-layer-spatially","title":"Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image","arxiv_id":null,"date":"2023-03-16","proceeding":"ISPRS Journal of Photogrammetry and Remote Sensing 2023 3","authors":["Yan Zhang","Pengyuan Liu","Filip Biljecki"],"abstract":"With the rise of GeoAI research, streetscape imagery has received extensive attention due to its comprehensive\r\ncoverage, abundant information, and accessibility. However, obtaining a holistic spatial–temporal scene\r\nrepresentation is difficult because places are often composed of multiple images from different angles, times\r\nand locations. This problem also exists in other types of geo-tagged imagery. To solve it, we propose a\r\npurely visual, robust, and reliable method for urban function identification at the street scale. We introduce\r\na method based on a two-layer spatially dependent graph neural network structure, which handles sequential\r\nstreet view imagery as input (typically available in services such as Google Street View, Baidu Maps, and\r\nMapillary), with full consideration of the spatial dependencies among road networks. In this paper, we\r\nconstruct an urban topological map network using OpenStreetMap data in Wuhan, China, and compute a\r\nsemantic representation of the scene as a whole at the street scale using a large-scale pre-trained model.\r\nWe construct the graph network with streets as nodes based on 28,693 mapping relationships constructed\r\nfrom 75,628 street view images and 5,458 streets. Only 5.3% of the node labels were required to obtain\r\n10 categories of functions for all nodes in the study area. The results demonstrate that by using appropriate\r\nspatial weights, street encoder, and graph structure, our novel method achieves high accuracy of P@1 46.2%,\r\nP@3 73.0%, P@5 82.4%, and P@10 89.9%, fully demonstrating the effectiveness of the introduced approach.\r\nWe also use the model to sense urban spatial–temporal renewal by computing time series street images.\r\nThe model is also applicable to the prediction of other attributes, where only a small number of labels\r\nare required to obtain valid and reliable scene perception results. The example data and code is shared at:\r\nhttps://github.com/yemanzhongting/Knowledge-and-Topology.","url_abs":"https://www.sciencedirect.com/science/article/abs/pii/S0924271623000680","url_pdf":"https://www.sciencedirect.com/science/article/abs/pii/S0924271623000680","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":"knowledge-and-topology-a-two-layer-spatially","repo_url":"https://github.com/yemanzhongting/Knowledge-and-Topology","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"time-series-1","task_name":"Time Series"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}