{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T20:40:30Z","timestamp":1781556030939,"version":"3.54.5"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation","award":["1755946, 2040950, 2006889, 2045567, CNS-2141095"],"award-info":[{"award-number":["1755946, 2040950, 2006889, 2045567, CNS-2141095"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2023,3,31]]},"abstract":"<jats:p>Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and personal experiences. To alleviate the heavy burden of them and produce consistent urban plans, we want to ask that can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models provides a possible answer, which inspires us to automate urban planning from an adversarial learning perspective. However, three major challenges arise: (1) how to define a quantitative land-use configuration? (2) how to automate configuration planning? (3) how to evaluate the quality of a generated configuration? In this article, we systematically address the three challenges. Specifically, (1) We define a land-use configuration as a longitude-latitude-channel tensor. (2) We formulate the automated urban planning problem into a task of deep generative learning. The objective is to generate a configuration tensor given the surrounding contexts of a target region. In particular, we first construct spatial graphs using geographic and human mobility data crawled from websites to learn graph representations. We then combine each target area and its surrounding context representations as a tuple, and categorize all tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Next, we develop an adversarial learning framework, in which a generator takes the surrounding context representations as input to generate a land-use configuration, and a discriminator learns to distinguish between positive and negative samples. (3) We provide quantitative evaluation metrics and conduct extensive experiments to demonstrate the effectiveness of our framework.<\/jats:p>","DOI":"10.1145\/3524302","type":"journal-article","created":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T11:53:12Z","timestamp":1649850792000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation"],"prefix":"10.1145","volume":"9","author":[{"given":"Dongjie","family":"Wang","sequence":"first","affiliation":[{"name":"University of Central Florida, Orlando, FL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, FL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunpeng","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, FL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanglan","family":"Chen","sequence":"additional","affiliation":[{"name":"Virginia Tech, Falls Church"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyang","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Macau, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang-Tien","family":"Lu","sequence":"additional","affiliation":[{"name":"Virginia Tech, Falls Church, VA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Urban Planning and the Development Process","author":"Adams David","year":"1994","unstructured":"David Adams. 1994. Urban Planning and the Development Process. Psychology Press."},{"key":"e_1_3_2_3_2","first-page":"622","volume-title":"Proceedings of the Asian Conference on Computer Vision","author":"Akcay Samet","year":"2018","unstructured":"Samet Akcay, Amir Atapour-Abarghouei, and Toby P. Breckon. 2018. Ganomaly: Semi-supervised anomaly detection via adversarial training. In Proceedings of the Asian Conference on Computer Vision. Springer, 622\u2013637."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2018.8518032"},{"key":"e_1_3_2_5_2","series-title":"Proceedings of the 34th International Conference on Machine Learning","first-page":"214","volume":"70","author":"Arjovsky Martin","year":"2017","unstructured":"Martin Arjovsky, Soumith Chintala, and L\u00e9on Bottou. 2017. Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 70), Doina Precup and Yee Whye Teh (Eds.), PMLR, International Convention Centre, 214\u2013223. Retrieved from http:\/\/proceedings.mlr.press\/v70\/arjovsky17a.html."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3161602"},{"key":"e_1_3_2_7_2","unstructured":"Maximilian Bachl and Daniel C. Ferreira. 2019. City-GAN: Learning architectural styles using a custom conditional GAN architecture. arXiv:1907.05280. Retrieved from https:\/\/arxiv.org\/abs\/1907.05280."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.4324\/9780203857755"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3347146.3359104"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00916"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271768"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3232229"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00026"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2837027"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301906"},{"key":"e_1_3_2_16_2","first-page":"1666","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Ganin Yaroslav","year":"2018","unstructured":"Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S. M. Ali Eslami, and Oriol Vinyals. 2018. Synthesizing programs for images using reinforced adversarial learning. In Proceedings of the International Conference on Machine Learning. 1666\u20131675."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295327"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380210"},{"key":"e_1_3_2_19_2","first-page":"1989","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Hoffman Judy","year":"2018","unstructured":"Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell. 2018. CyCADA: Cycle-consistent adversarial domain adaptation. In Proceedings of the International Conference on Machine Learning. 1989\u20131998."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3441303"},{"key":"e_1_3_2_21_2","unstructured":"Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv:1611.07308. Retrieved from https:\/\/arxiv.org\/abs\/1611.07308."},{"key":"e_1_3_2_22_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Li Jianan","year":"2018","unstructured":"Jianan Li, Jimei Yang, Aaron Hertzmann, Jianming Zhang, and Tingfa Xu. 2018. LayoutGAN: Generating graphic layouts with wireframe discriminators. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975321.20"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-016-0948-6"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6361"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-28112-4_1"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_10"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008817325994"},{"key":"e_1_3_2_29_2","unstructured":"Ariel Noyman and Kent Larson. 2020. A deep image of the city: Generative urban-design visualization. In Proceedings of the 11th Annual Symposium on Simulation for Architecture and Urban Design 1\u20138."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1177\/0885412210364589"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1137\/18M1177846"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-019-1904-1"},{"key":"e_1_3_2_33_2","unstructured":"Alec Radford Luke Metz and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434. Retrieved from https:\/\/arxiv.org\/abs\/1511.06434."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.4324\/9780203428245"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00605"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1177\/1473095212456771"},{"issue":"11","key":"e_1_3_2_38_2","article-title":"Visualizing data using t-SNE.","volume":"9","author":"Maaten Laurens Van der","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 11 (2008), 11.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397536.3422268"},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","unstructured":"Dongjie Wang Kunpeng Liu Pauline Johnson Leilei Sun Bowen Du and Yanjie Fu. 2021. Deep human-guided conditional variational generative modeling for automated urban planning. In Proceeding of the IEEE International Conference on Data Mining (ICDM\u201921) IEEE 679\u2013688.","DOI":"10.1109\/ICDM51629.2021.00079"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474717.3484212"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2021.762899"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","unstructured":"Dongjie Wang Pengyang Wang Kunpeng Liu Yuanchun Zhou Charles E. Hughes and Yanjie Fu. 2021. Reinforced imitative graph representation learning for mobile user profiling: An adversarial training perspective. Proceedings of the AAAI Conference on Artificial Intelligence 35 5 (2021) 4410\u20134417.","DOI":"10.1609\/aaai.v35i5.16567"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00012"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489530"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.06.049"},{"issue":"6","key":"e_1_3_2_47_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3209686","article-title":"Learning urban community structures: A collective embedding perspective with periodic spatial-temporal mobility graphs","volume":"9","author":"Wang Pengyang","year":"2018","unstructured":"Pengyang Wang, Yanjie Fu, Jiawei Zhang, Xiaolin Li, and Dan Lin. 2018. Learning urban community structures: A collective embedding perspective with periodic spatial-temporal mobility graphs. ACM Transactions on Intelligent Systems and Technology (TIST\u201918) 9, 6 (2018), 1\u201328.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST\u201918)"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219985"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/472"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2935203"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403128"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975321.40"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1068\/b160023"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2345405"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.629"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00193"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403127"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.158"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106286"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature25988"}],"container-title":["ACM Transactions on Spatial Algorithms and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3524302","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3524302","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:56Z","timestamp":1750188656000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3524302"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,17]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,3,31]]}},"alternative-id":["10.1145\/3524302"],"URL":"https:\/\/doi.org\/10.1145\/3524302","relation":{},"ISSN":["2374-0353","2374-0361"],"issn-type":[{"value":"2374-0353","type":"print"},{"value":"2374-0361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,17]]},"assertion":[{"value":"2021-03-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-03-07","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-01-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}