{"type":"ScholarlyArticle","title":"From Pixels to Progress: Generating Road Network from Satellite Imagery for Socioeconomic Insights in Impoverished Areas","authors":[{"name":"Yanxin Xi","slug":"yanxin-xi","given":"Yanxin","family":"Xi","affiliation":"University of Helsinki , Department of Computer Science"},{"name":"Yu Liu","slug":"yu-liu","given":"Yu","family":"Liu","affiliation":"Tsinghua University , Department of Electronic Engineering , Beijing National Research Center for Information Science and Technology (BNRist)"},{"name":"Zhicheng Liu","slug":"zhicheng-liu","given":"Zhicheng","family":"Liu","affiliation":"Tsinghua University , Department of Electronic Engineering , Beijing National Research Center for Information Science and Technology (BNRist)"},{"name":"Sasu Tarkoma","slug":"sasu-tarkoma","given":"Sasu","family":"Tarkoma","affiliation":"University of Helsinki , Department of Computer Science"},{"name":"Pan Hui","slug":"pan-hui","given":"Pan","family":"Hui","affiliation":"University of Helsinki , Department of Computer Science; Hong Kong University of Science and Technology (Guangzhou) , Computational Media and Arts Thrust"},{"name":"Yong Li","slug":"yong-li","given":"Yong","family":"Li","affiliation":"Tsinghua University , Department of Electronic Engineering , Beijing National Research Center for Information Science and Technology (BNRist)"}],"abstract":"The Sustainable Development Goals (SDGs) aim to resolve societal challenges, such as eradicating poverty and improving the lives of vulnerable populations in impoverished areas. Those areas rely on road infrastructure construction to promote accessibility and economic development. Although publicly available data like OpenStreetMap is available to monitor road status, data completeness in impoverished areas is limited. Meanwhile, the development of deep learning techniques and satellite imagery shows excellent potential for earth monitoring. To tackle the challenge of road network assessment in impoverished areas, we develop a systematic road extraction framework combining an encoder-decoder architecture and morphological operations on satellite imagery, offering an integrated workflow for interdisciplinary researchers. Extensive experiments of road network extraction on real-world data in impoverished regions achieve a 42.7% enhancement in the F1-score over the baseline methods and reconstruct about 80% of the actual roads. We also propose a comprehensive road network dataset covering approximately 794,178 km2 area and 17.048 million people in 382 impoverished counties in China. The generated dataset is further utilized to conduct socioeconomic analysis in impoverished counties, showing that road network construction positively impacts regional economic development. The technical appendix, code, and generated dataset can be found at https://github.com/tsinghua-fib-lab/Road_network_extraction_impoverished_counties.","doi":"10.24963/ijcai.2024/831","url":"https://ijcai.org/proceedings/2024/831","pdf":"https://ijcai.org/proceedings/2024/0831.pdf","markdown":"https://ijcai.org/proceedings/2024/831.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/831","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":831,"pages":{"first":7509,"last":7517},"keywords":["Humans and AI: General","Data Mining: General","Multidisciplinary Topics and Applications: General","Computer Vision: General","Machine Learning: General"],"videos":[],"references":[{"position":1,"raw":"[Abadie, 2005] Alberto Abadie. Semiparametric difference- in-differences estimators. The review of economic studies, 72(1):1-19, 2005.","title":"Semiparametric differencein-differences estimators","authors":"Alberto Abadie","year":"2005","venue":"The review of economic studies","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Aguilar and Buchin, 2021] Jordi Aguilar and Kevin Buchin. Graph sampling for map comparison. In 3rd ACM SIGSPATIAL International Workshop on Spatial Gems, 2021.","title":"Graph sampling for map comparison","authors":"Buchin Aguilar, Jordi Aguilar, Kevin Buchin","year":"2021","venue":"3rd ACM SIGSPATIAL International Workshop on Spatial Gems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Alagic, 2017] Arijan Alagic. An analysis of the causal re- lationship between transportation and gdp: A time-series approach for the united states. Major themes in economics, 19(1):17-37, 2017.","title":"Arijan Alagic. An analysis of the causal relationship between transportation and gdp: A time-series approach for the united states","authors":"Alagic","year":"2017","venue":"Major themes in economics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Anbao and Danhua, 2011] Tang Anbao and Zhao Danhua. A comparative analysis on the factors promoting china's economic growth based on demand. Energy Procedia, 5:1388-1393, 2011.","title":"A comparative analysis on the factors promoting china's economic growth based on demand","authors":"Tang Anbao, Zhao Danhua","year":"2011","venue":"Energy Procedia","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Banerjee et al., 2020] Abhijit Banerjee, Esther Duflo, and Nancy Qian. On the road: Access to transportation in- frastructure and economic growth in china. Journal of De- velopment Economics, 145:102442, 2020.","title":"On the road: Access to transportation infrastructure and economic growth in china","authors":"Abhijit Banerjee, Esther Duflo, Nancy Qian","year":"2020","venue":"Journal of Development Economics","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Bansal et al., 2016] Raghav Bansal, Gaurav Raj, and Tanupriya Choudhury. Blur image detection using lapla- cian operator and open-cv. In 2016 International Confer- ence System Modeling & Advancement in Research Trends (SMART), pages 63-67. IEEE, 2016.","title":"Blur image detection using laplacian operator and open-cv","authors":"Raghav Bansal, Gaurav Raj, Tanupriya Choudhury","year":"2016","venue":"2016 International Conference System Modeling & Advancement in Research Trends (SMART)","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Bettencourt, 2013] Luís MA Bettencourt. The origins of scaling in cities. science, 340(6139):1438-1441, 2013.","title":"The origins of scaling in cities","authors":"M A Luís, Bettencourt","year":"2013","venue":"science","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Biagioni and Eriksson, 2012] James Biagioni and Jakob Eriksson. Inferring road maps from global positioning system traces: Survey and comparative evaluation. Trans- portation research record, 2291(1):61-71, 2012.","title":"Inferring road maps from global positioning system traces: Survey and comparative evaluation","authors":"Eriksson Biagioni, James Biagioni, Jakob Eriksson","year":"2012","venue":"Transportation research record","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chen et al., 2023] Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, and Yu Qiao. Vi- sion Transformer Adapter for Dense Predictions. In The Eleventh International Conference on Learning Represen- tations, 2023.","title":"Vision Transformer Adapter for Dense Predictions","authors":"Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, Yu Qiao","year":"2023","venue":"The Eleventh International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[GaryBikini, 2023] GaryBikini. China administrative divi- sion shp, 2023.","title":"GaryBikini. China administrative division shp","authors":"Garybikini","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"Geofabrik GmbH, OSM Foundation & Contributors. Ge- ofabrik downloads. http://download.geofabrik.de/ north-america/us.html, 2022. Accessed: 2023-10-30.","title":"OSM Foundation & Contributors","authors":null,"year":"2022","venue":null,"doi":null,"url":"http://download.geofabrik.de/north-america/us.html","internal_url":null},{"position":12,"raw":"[Gonzales and Wintz, 1987] Rafael C Gonzales and Paul Wintz. Digital image processing. Addison-Wesley Long- man Publishing Co., Inc., 1987.","title":"Digital image processing","authors":"C Rafael, Paul Gonzales, Wintz","year":"1987","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Haklay and Weber, 2008] Mordechai Haklay and Patrick Weber. Openstreetmap: User-generated street maps. IEEE Pervasive computing, 7(4):12-18, 2008.","title":"Openstreetmap: User-generated street maps","authors":"Mordechai Haklay, Patrick Weber","year":"2008","venue":"IEEE Pervasive computing","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[He et al., 2010] Kaiming He, Jian Sun, and Xiaoou Tang. Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence, 33(12):2341-2353, 2010.","title":"Single image haze removal using dark channel prior","authors":"He","year":"2010","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Ighodaro, 2009] C Ighodaro. Transport infrastructure and economic growth in nigeria. Journal of research in na- tional development, 7(2), 2009.","title":"Transport infrastructure and economic growth in nigeria","authors":"Ighodaro","year":"2009","venue":"Journal of research in national development","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lewis-Beck and Lewis-Beck, 2015] Colin Lewis-Beck and Michael Lewis-Beck. Applied regression: An introduc- tion, volume 22. Sage publications, 2015.","title":"Applied regression: An introduction","authors":"Lewis-Beck, Lewis-Beck ; -Beck, Michael Lewis-Beck","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2022] Jiawei Li, Linkun Lyu, Jia Shi, Jie Zhao, Junjie Xu, Jiuchong Gao, Renqing He, and Zhizhao Sun. Generating community road network from gps trajectories via style transfer. In Proceedings of the 30th International Conference on Advances in Geographic Information Sys- tems, pages 1-4, 2022.","title":"Generating community road network from gps trajectories via style transfer","authors":"Jiawei Li, Linkun Lyu, Jia Shi, Jie Zhao, Junjie Xu, Jiuchong Gao, Renqing He, Zhizhao Sun","year":"2022","venue":"Proceedings of the 30th International Conference on Advances in Geographic Information Systems","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liu et al., 2018] Yahui Liu, Jian Yao, Xiaohu Lu, Menghan Xia, Xingbo Wang, and Yuan Liu. Roadnet: Learning to comprehensively analyze road networks in complex ur- ban scenes from high-resolution remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing, 57(4):2043-2056, 2018.","title":"Roadnet: Learning to comprehensively analyze road networks in complex urban scenes from high-resolution remotely sensed images","authors":"Yahui Liu, Jian Yao, Xiaohu Lu, Menghan Xia, Xingbo Wang, Yuan Liu","year":"2018","venue":"IEEE Transactions on Geoscience and Remote Sensing","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Mei et al., 2021] Jie Mei, Rou-Jing Li, Wang Gao, and Ming-Ming Cheng. CoANet: Connectivity attention net- work for road extraction from satellite imagery. IEEE Transactions on Image Processing, 30:8540-8552, 2021.","title":"CoANet: Connectivity attention network for road extraction from satellite imagery","authors":"Jie Mei, Rou-Jing Li, Wang Gao, Ming-Ming Cheng","year":"2021","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Mei, 2021] Jie Mei. CoANet: Connectivity attention net- work for road extraction from satellite imagery github page. https://github.com/mj129/CoANet, 2021.","title":"CoANet: Connectivity attention network for road extraction from satellite imagery github page","authors":"Jie Mei","year":null,"venue":null,"doi":null,"url":"https://github.com/mj129/CoANet,2021","internal_url":null},{"position":21,"raw":"[Mnih and Hinton, 2010] Volodymyr Mnih and Geoffrey E Hinton. Learning to detect roads in high-resolution aerial images. In Computer Vision-ECCV 2010: 11th Euro- pean Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part VI 11, pages 210-223. Springer, 2010.","title":"Learning to detect roads in high-resolution aerial images","authors":"Hinton Mnih, Volodymyr Mnih, Geoffrey E Hinton","year":"2010","venue":"Computer Vision-ECCV 2010: 11th European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Mnih, 2013] Volodymyr Mnih. Machine learning for aerial image labeling. University of Toronto (Canada), 2013.","title":"Volodymyr Mnih. Machine learning for aerial image labeling","authors":"Mnih","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Nachmany and Alemohammad, 2019] Yoni Nachmany and Hamed Alemohammad. Detecting roads from satellite imagery in the developing world. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019.","title":"Yoni Nachmany and Hamed Alemohammad. Detecting roads from satellite imagery in the developing world","authors":"Alemohammad Nachmany","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Nath, 2016] DK Nath. Making cities and human settlements inclusive, safe, resilient and sustainable, sl: Sustainable urbanization summit. 2016.","title":"DK Nath. Making cities and human settlements inclusive, safe, resilient and sustainable, sl: Sustainable urbanization summit","authors":"Nath","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Porta et al., 2009] Sergio Porta, Emanuele Strano, Valentino Iacoviello, Roberto Messora, Vito Latora, Alessio Cardillo, Fahui Wang, and Salvatore Scellato. Street centrality and densities of retail and services in bologna, italy. Environment and Planning B: Planning and design, 36(3):450-465, 2009.","title":"Street centrality and densities of retail and services in bologna, italy","authors":"Sergio Porta, Emanuele Strano, Valentino Iacoviello, Roberto Messora, Vito Latora, Alessio Cardillo, Fahui Wang, Salvatore Scellato","year":"2009","venue":"Environment and Planning B: Planning and design","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Pradhan and Bagchi, 2013] Rudra P Pradhan and Tapan P Bagchi. Effect of transportation infrastructure on eco- nomic growth in india: The vecm approach. Research in Transportation economics, 38(1):139-148, 2013.","title":"Effect of transportation infrastructure on economic growth in india: The vecm approach","authors":"P Rudra, Tapan P Pradhan, Bagchi","year":"2013","venue":"Research in Transportation economics","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Ratledge et al., 2022] Nathan Ratledge, Gabe Cadamuro, Brandon de la Cuesta, Matthieu Stigler, and Marshall Burke. Using machine learning to assess the livelihood impact of electricity access. Nature, 611(7936):491-495, 2022.","title":"Using machine learning to assess the livelihood impact of electricity access","authors":"Nathan Ratledge, Gabe Cadamuro, Brandon De La Cuesta, Matthieu Stigler, Marshall Burke","year":"2022","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Ruan et al., 2020] Sijie Ruan, Cheng Long, Jie Bao, Chun- yang Li, Zisheng Yu, Ruiyuan Li, Yuxuan Liang, Tianfu He, and Yu Zheng. Learning to generate maps from trajec- tories. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 890-897, 2020.","title":"Learning to generate maps from trajectories","authors":"Cheng Sijie Ruan, Jie Long, Chunyang Bao, Zisheng Li, Ruiyuan Yu, Yuxuan Li, Tianfu Liang, Yu He, Zheng","year":"2020","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Sebayang and Sebayang, 2020] Asnita Frida Sebayang and Lesta Karolina Sebayang. Infrastructure investment and its impact to regional development. Economics Development Analysis Journal, 9(3):269-280, 2020.","title":"Asnita Frida Sebayang and Lesta Karolina Sebayang. Infrastructure investment and its impact to regional development","authors":"Sebayang Sebayang","year":"2020","venue":"Economics Development Analysis Journal","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Shi et al., 2009] Wenhuan Shi, Shuhan Shen, and Yuncai Liu. Automatic generation of road network map from massive gps, vehicle trajectories. In 2009 12th interna- tional IEEE conference on intelligent transportation sys- tems, pages 1-6. IEEE, 2009.","title":"Automatic generation of road network map from massive gps, vehicle trajectories","authors":"Shi","year":"2009","venue":"12th international IEEE conference on intelligent transportation systems","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Spadon et al., 2018] Gabriel Spadon, Gabriel Gimenes, and Jose F Rodrigues. Topological street-network characteri- zation through feature-vector and cluster analysis. In Com- putational Science-ICCS 2018: 18th International Con- ference, Wuxi, China, June 11-13, 2018, Proceedings, Part I 18, pages 274-287. Springer, 2018.","title":"Topological street-network characterization through feature-vector and cluster analysis","authors":"Gabriel Gabriel Spadon, Jose F Gimenes, Rodrigues","year":"2018","venue":"Computational Science-ICCS 2018: 18th International Conference","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[The National Rural Revitalization Administration, 2014] The National Rural Revitalization Administration. List of 832 impoverished counties nationwide. https://nrra.gov.cn/art/2014/12/23/art 343 981.html, 2014.","title":"The National Rural Revitalization Administration. List of 832 impoverished counties nationwide","authors":null,"year":"2014","venue":null,"doi":null,"url":"https://nrra.gov.cn/art/2014/12/23/art343981.html","internal_url":null},{"position":33,"raw":"[UN General Assembly, 2015] UN General Assembly. Transforming our world: the 2030 agenda for sustainable development. United Nations: New York, NY, USA, 2015.","title":"UN General Assembly. Transforming our world: the 2030 agenda for sustainable development","authors":null,"year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[UN System Chief Executives Board for Coordination, 2017] UN System Chief Executives Board for Coordina- tion. Leaving no one behind: Equality and non- discrimination at the heart of sustainable development. https://unsceb.org/sites/default/files/imported files/CEB% 20equality%20framework-A4-web-rev3.pdf, 2017.","title":"UN System Chief Executives Board for Coordination. Leaving no one behind: Equality and nondiscrimination at the heart of sustainable development","authors":null,"year":"2017","venue":null,"doi":null,"url":"https://unsceb.org/sites/default/files/importedfiles/CEB%20equality%20framework-A4-web-rev3.pdf","internal_url":null},{"position":35,"raw":"[Van Etten et al., 2018] Adam Van Etten, Dave Linden- baum, and Todd M Bacastow. Spacenet: A remote sensing dataset and challenge series. arXiv preprint arXiv:1807.01232, 2018.","title":"Spacenet: A remote sensing dataset and challenge series","authors":"Van Etten","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Vargas-Munoz et al., 2020] John E Vargas-Munoz, Shiv- angi Srivastava, Devis Tuia, and Alexandre X Falcao. Openstreetmap: Challenges and opportunities in machine learning and remote sensing. IEEE Geoscience and Re- mote Sensing Magazine, 9(1):184-199, 2020.","title":"Openstreetmap: Challenges and opportunities in machine learning and remote sensing","authors":"John E Vargas-Munoz, Shivangi Srivastava, Devis Tuia, Alexandre X Falcao","year":"2020","venue":"IEEE Geoscience and Remote Sensing Magazine","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang et al., 2021] Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zhong. Loveda: A remote sensing land-cover dataset for domain adaptive semantic segmen- tation. In J. Vanschoren and S. Yeung, editors, Proceed- ings of the Neural Information Processing Systems Track on Datasets and Benchmarks, volume 1. Curran, 2021.","title":"Loveda: A remote sensing land-cover dataset for domain adaptive semantic segmentation","authors":"Wang","year":"2021","venue":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang et al., 2023] Zhaohua Wang, Bin Lu, Bo Wang, Yueming Qiu, Han Shi, Bin Zhang, Jingyun Li, Hao Li, and Wenhui Zhao. Incentive based emergency demand response effectively reduces peak load during heatwave without harm to vulnerable groups. Nature Communica- tions, 14(1):6202, 2023.","title":"Incentive based emergency demand response effectively reduces peak load during heatwave without harm to vulnerable groups","authors":"Wang","year":"2023","venue":"Nature Communications","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wegner et al., 2015] Jan Dirk Wegner, Javier Alexander Montoya-Zegarra, and Konrad Schindler. Road networks as collections of minimum cost paths. ISPRS Journal of Photogrammetry and Remote Sensing, 108:128-137, 2015.","title":"Road networks as collections of minimum cost paths","authors":"Wegner","year":"2015","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xi et al., 2023] Yanxin Xi, Yu Liu, Tong Li, Jingtao Ding, Yunke Zhang, Sasu Tarkoma, Yong Li, and Pan Hui. A satellite imagery dataset for long-term sustainable devel- opment in united states cities. Scientific data, 10(1):866, 2023.","title":"A satellite imagery dataset for long-term sustainable development in united states cities","authors":"Yanxin Xi, Yu Liu, Tong Li, Jingtao Ding, Yunke Zhang, Sasu Tarkoma, Yong Li, Pan Hui","year":"2023","venue":"Scientific data","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yang et al., 2022] Zhigang Yang, Daoxiang Zhou, Ying Yang, Jiapeng Zhang, and Zehua Chen. Road extraction from satellite imagery by road context and full-stage fea- ture. IEEE Geoscience and Remote Sensing Letters, 20:1- 5, 2022.","title":"Road extraction from satellite imagery by road context and full-stage feature","authors":"Zhigang Yang, Daoxiang Zhou, Ying Yang, Jiapeng Zhang, Zehua Chen","year":"2022","venue":"IEEE Geoscience and Remote Sensing Letters","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhang and Cheng, 2023] Yijia Zhang and Lu Cheng. The role of transport infrastructure in economic growth: Em- pirical evidence in the uk. Transport Policy, 133:223-233, 2023.","title":"The role of transport infrastructure in economic growth: Empirical evidence in the uk","authors":"Cheng ; ] Yijia Zhang, Lu Zhang, Cheng","year":"2023","venue":"Transport Policy","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang and Suen, 1984] Tongjie Y Zhang and Ching Y. Suen. A fast parallel algorithm for thinning digital pat- terns. Communications of the ACM, 27(3):236-239, 1984.","title":"A fast parallel algorithm for thinning digital patterns","authors":"Suen ; ] Zhang, Y Tongjie, Ching Y Zhang, Suen","year":"1984","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2018] Zhengxin Zhang, Qingjie Liu, and Yun- hong Wang. Road extraction by deep residual u-net. IEEE Geoscience and Remote Sensing Letters, 15(5):749-753, 2018.","title":"Road extraction by deep residual u-net","authors":"Zhang","year":"2018","venue":"IEEE Geoscience and Remote Sensing Letters","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhang et al., 2021] Jinrui Zhang, Ruilian Zhang, Junzhuo Xu, Jie Wang, and Guoqing Shi. Infrastructure investment and regional economic growth: Evidence from yangtze river economic zone. Land, 10(3):320, 2021.","title":"Infrastructure investment and regional economic growth: Evidence from yangtze river economic zone","authors":"Jinrui Zhang, Ruilian Zhang, Junzhuo Xu, Jie Wang, Guoqing Shi","year":"2021","venue":"Land","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhang et al., 2024] Xin Zhang, Yu Liu, Yuming Lin, Qing- min Liao, and Yong Li. Uv-sam: Adapting segment any- thing model for urban village identification. In Proceed- ings of the AAAI Conference on Artificial Intelligence, vol- ume 38, pages 22520-22528, 2024.","title":"Uv-sam: Adapting segment anything model for urban village identification","authors":"Xin Zhang, Yu Liu, Yuming Lin, Qingmin Liao, Yong Li","year":"2024","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhou and Lin, 2019] Qi Zhou and Hao Lin. Investigating the completeness and omission roads of openstreetmap data in hubei, china by comparing with street map and street view. arXiv preprint arXiv:1909.04323, 2019.","title":"Investigating the completeness and omission roads of openstreetmap data in hubei, china by comparing with street map and street view","authors":"Lin ; ] Qi Zhou, Hao Zhou, Lin","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Zhou et al., 2022] Yang Zhou, Chunyang Tong, and Yong- sheng Wang. Road construction, economic growth, and poverty alleviation in china. Growth and Change, 53(3):1306-1332, 2022.","title":"Road construction, economic growth, and poverty alleviation in china","authors":"Yang Zhou, Chunyang Tong, Yongsheng Wang","year":"2022","venue":"Growth and Change","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}