{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:36:14Z","timestamp":1770741374275,"version":"3.49.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585822","type":"print"},{"value":"9783030585839","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58583-9_14","type":"book-chapter","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T10:08:18Z","timestamp":1605694098000},"page":"224-240","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation"],"prefix":"10.1007","author":[{"given":"Subeesh","family":"Vasu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mateusz","family":"Kozinski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leonardo","family":"Citraro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pascal","family":"Fua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"issue":"7","key":"14_CR1","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1109\/34.506793","volume":"18","author":"M Barzohar","year":"1996","unstructured":"Barzohar, M., Cooper, D.B.: Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation. IEEE Trans. Pattern Anal. Mach. Intell. 18(7), 707\u2013721 (1996)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Bastani, F., et al.: RoadTracer: automatic extraction of road networks from aerial images. In: Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00496"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Batra, A., Singh, S., Pang, G., Basu, S., Jawahar, C., Paluri, M.: Improved road connectivity by joint learning of orientation and segmentation. In: Conference on Computer Vision and Pattern Recognition, June 2019","DOI":"10.1109\/CVPR.2019.01063"},{"key":"14_CR4","unstructured":"Bengio, Y., L\u00e9onard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation. In: arXiv Preprint (2013)"},{"key":"14_CR5","doi-asserted-by":"publisher","first-page":"61","DOI":"10.3141\/2291-08","volume":"2291","author":"J Biagioni","year":"2012","unstructured":"Biagioni, J., Eriksson, J.: Inferring road maps from global positioning system traces: survey and comparative evaluation. Transp. Res. Rec. J. Transp. Res. Board 2291, 61\u201371 (2012)","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Chai, D., Forstner, W., Lafarge, F.: Recovering line-networks in images by junction-point processes. In: Conference on Computer Vision and Pattern Recognition (2013)","DOI":"10.1109\/CVPR.2013.247"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Van Gool, L.: ROAD: Reality oriented adaptation for semantic segmentation of urban scenes. In: Conference on Computer Vision and Pattern Recognition. pp. 7892\u20137901 (2018)","DOI":"10.1109\/CVPR.2018.00823"},{"issue":"6","key":"14_CR8","doi-asserted-by":"publisher","first-page":"3322","DOI":"10.1109\/TGRS.2017.2669341","volume":"55","author":"G Cheng","year":"2017","unstructured":"Cheng, G., Wang, Y., Xu, S., Wang, H., Xiang, S., Pan, C.: Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network. IEEE Trans. Geosci. Remote Sens. 55(6), 3322\u20133337 (2017)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Chu, H., et al.: Neural turtle graphics for modeling city road layouts. In: International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00462"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Demir, I., et al.: DeepGlobe 2018: a challenge to parse the earth through satellite images. In: Conference on Computer Vision and Pattern Recognition, June 2018","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"14_CR11","unstructured":"Etten, A.V., Lindenbaum, D., Bacastow, T.: SpaceNet: a remote sensing dataset and challenge series. CoRR abs\/1807.01232 (2018). http:\/\/arxiv.org\/abs\/1807.01232"},{"key":"14_CR12","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)"},{"issue":"4","key":"14_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073659","volume":"36","author":"S Iizuka","year":"2017","unstructured":"Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. 36(4), 1\u201314 (2017)","journal-title":"ACM Trans. Graph."},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"14_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"14_CR16","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s001380050121","volume":"12","author":"I Laptev","year":"2000","unstructured":"Laptev, I., Mayer, H., Lindeberg, T., Eckstein, W., Steger, C., Baumgartner, A.: Automatic extraction of roads from aerial images based on scale space and snakes. Mach. Vis. Appl. 12, 23\u201331 (2000). https:\/\/doi.org\/10.1007\/s001380050121","journal-title":"Mach. Vis. Appl."},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Li, Z., Wegner, J., Lucchi, A.: Topological map extraction from overhead images. In: International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00180"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"M\u00e1ttyus, G., Luo, W., Urtasun, R.: DeepRoadMapper: extracting road topology from aerial images. In: International Conference on Computer Vision, pp. 3458\u20133466 (2017)","DOI":"10.1109\/ICCV.2017.372"},{"key":"14_CR19","unstructured":"Mnih, V., Hinton, G.: Learning to label aerial images from noisy data. In: International Conference on Machine Learning (2012)"},{"key":"14_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1007\/978-3-642-15567-3_16","volume-title":"Computer Vision \u2013 ECCV 2010","author":"V Mnih","year":"2010","unstructured":"Mnih, V., Hinton, G.E.: Learning to detect roads in high-resolution aerial images. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 210\u2013223. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15567-3_16"},{"issue":"6","key":"14_CR21","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1109\/TPAMI.2019.2921327","volume":"42","author":"A Mosi\u0144ska","year":"2019","unstructured":"Mosi\u0144ska, A., Kozinski, M., Fua, P.: Joint segmentation and path classification of curvilinear structures. IEEE Trans. Pattern Anal. Mach. Intell. 42(6), 1515\u20131521 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Mosi\u0144ska, A., Marquez-Neila, P., Kozinski, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Conference on Computer Vision and Pattern Recognition, pp. 3136\u20133145 (2018)","DOI":"10.1109\/CVPR.2018.00331"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"M\u00e1ttyus, G., Urtasun, R.: Matching adversarial networks. In: Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00837"},{"key":"14_CR24","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS-W (2017)"},{"key":"14_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2014 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"2","key":"14_CR26","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1023\/B:VISI.0000013086.45688.5d","volume":"57","author":"R Stoica","year":"2004","unstructured":"Stoica, R., Descombes, X., Zerubia, J.: A Gibbs point process for road extraction from remotely sensed images. Int. J. Comput. Vision 57(2), 121\u2013136 (2004). https:\/\/doi.org\/10.1023\/B:VISI.0000013086.45688.5d","journal-title":"Int. J. Comput. Vision"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Turetken, E., Benmansour, F., Andres, B., Pfister, H., Fua, P.: Reconstructing loopy curvilinear structures using integer programming. In: Conference on Computer Vision and Pattern Recognition, June 2013","DOI":"10.1109\/CVPR.2013.238"},{"key":"14_CR28","unstructured":"Ventura, C., Pont-Tuset, J., Caelles, S., Maninis, K., Gool, L.V.: Iterative deep learning for network topology extraction. In: British Machine Vision Conference (2018)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00917"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Wang, W., Yu, K., Hugonot, J., Fua, P., Salzmann, M.: Recurrent U-Net for resource-constrained segmentation. In: International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00223"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Wegner, J., Montoya-Zegarra, J., Schindler, K.: A higher-order CRF model for road network extraction. In: Conference on Computer Vision and Pattern Recognition, pp. 1698\u20131705 (2013)","DOI":"10.1109\/CVPR.2013.222"},{"key":"14_CR32","unstructured":"Wiedemann, C., Heipke, C., Mayer, H., Jamet, O.: Empirical evaluation of automatically extracted road axes. In: Empirical Evaluation Techniques in Computer Vision, pp. 172\u2013187 (1998)"},{"issue":"9","key":"14_CR33","doi-asserted-by":"publisher","first-page":"7209","DOI":"10.1109\/TGRS.2019.2912301","volume":"57","author":"X Yang","year":"2019","unstructured":"Yang, X., Li, X., Ye, Y., Lau, R.Y.K., Zhang, X., Huang, X.: Road detection and centerline extraction via deep recurrent convolutional neural network U-Net. IEEE Trans. Geosci. Remote Sens. 57(9), 7209\u20137220 (2019)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"14_CR34","unstructured":"Yin, P., Lyu, J., Zhang, S., Osher, S.J., Qi, Y., Xin, J.: Understanding straight-through estimator in training activation quantized neural nets. In: International Conference on Learning Representations (2019)"},{"key":"14_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhang, C., Wu, M.: D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: CVPR Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00034"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58583-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:05:32Z","timestamp":1731888332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58583-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585822","9783030585839"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58583-9_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}