{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T03:58:43Z","timestamp":1776916723686,"version":"3.51.2"},"reference-count":40,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071383"],"award-info":[{"award-number":["42071383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Community roads are crucial to community navigation. There are automatic methods to obtain community roads using trajectories, but the sparsity and uneven density distribution of community trajectories present significant challenges in identifying community roads. To overcome these challenges, we propose a conditional generative adversarial network (MAC-GAN) supervised by pedestrian trajectories and neighborhood building footprints for road generation. MAC-GAN packs the \u201croad trajectory\u2013building footprint\u201d pairs into images to characterize implicit ternary relations and sets up a multi-scale skip-connected and asymmetric convolution-based generator to incorporate such a relationship, in which the generator and discriminator mutually learn to optimize the network parameters and then derive approximate optimal results. Experiments on 37 real-world community datasets in Wuhan, China, are conducted to verify the effectiveness of the proposed model. The experimental results show that the F1 score of our model increases by 1.7\u20136.8%, and the IOU of our model increases by 2.2\u20137.5% compared with three baselines (i.e., Pix2pix, GANmapper, and DLinkGAN (configured by DLinknet)). In areas with sparse and missing trajectory data, the generated fine roads have high accuracy with the supervision of building footprints.<\/jats:p>","DOI":"10.3390\/ijgi12050181","type":"journal-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T01:16:25Z","timestamp":1682471785000},"page":"181","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["MAC-GAN: A Community Road Generation Model Combining Building Footprints and Pedestrian Trajectories"],"prefix":"10.3390","volume":"12","author":[{"given":"Lin","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zejun","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunping","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China"},{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1438","DOI":"10.1126\/science.1235823","article-title":"The origins of scaling in cities","volume":"340","author":"Bettencourt","year":"2013","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Karagiorgou, S., and Pfoser, D. 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