{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T01:13:03Z","timestamp":1783559583708,"version":"3.55.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032222602","type":"print"},{"value":"9783032222619","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-22261-9_29","type":"book-chapter","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:13:07Z","timestamp":1783555987000},"page":"359-370","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Remote Sensing Cross-Domain Semantic Segmentation for\u00a0Unknown Class Detection in\u00a0Real-World Scenarios"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7673-3065","authenticated-orcid":false,"given":"Ke","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"De","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1151-6814","authenticated-orcid":false,"given":"Jin-Chun","family":"Piao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,2]]},"reference":[{"issue":"8","key":"29_CR1","doi-asserted-by":"publisher","first-page":"5367","DOI":"10.1109\/TGRS.2020.2964675","volume":"58","author":"L Ding","year":"2020","unstructured":"Ding, L., Zhang, J., Bruzzone, L.: Semantic segmentation of large-size VHR remote sensing images using a two-stage multiscale training architecture. IEEE Trans. Geosci. Remote Sens. 58(8), 5367\u20135376 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The synthia dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3234\u20133243. IEEE (2016)","DOI":"10.1109\/CVPR.2016.352"},{"key":"29_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3040221","volume":"60","author":"B Zhang","year":"2021","unstructured":"Zhang, B., Chen, T., Wang, B.: Curriculum-style local-to-global adaptation for cross-domain remote sensing image segmentation. IEEE Trans. Geosci. Remote Sens. 60, 1\u201312 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Pan, F., Shin, I., Rameau, F., Lee, S., Kweon, I.S.: Unsupervised intra-domain adaptation for semantic segmentation through self-supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3764\u20133773 (2020)","DOI":"10.1109\/CVPR42600.2020.00382"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Huang, H., Li, B., Zhang, Y., Chen, T., Wang, B.: Joint distribution adaptive-alignment for cross-domain segmentation of high-resolution remote sensing images. IEEE Trans. Geosci. Remote Sens. (2024)","DOI":"10.1109\/TGRS.2023.3348505"},{"key":"29_CR8","first-page":"1","volume":"60","author":"H Chen","year":"2022","unstructured":"Chen, H., Zhang, H., Yang, G., Li, S., Zhang, L.: A mutual information domain adaptation network for remotely sensed semantic segmentation. IEEE Trans. Geosci. Remote Sens. 60, 1\u201316 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Vu, T.-H., Jain, H., Bucher, M., Cord, M., P\u00e9rez, P.: Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2517\u20132526 (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Melas-Kyriazi, L., Manrai, A.K.: Pixmatch: unsupervised domain adaptation via pixelwise consistency training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12435\u201312445 (2021)","DOI":"10.1109\/CVPR46437.2021.01225"},{"key":"29_CR11","unstructured":"Caliva, F., Iriondo, C., Martinez, A.M., Majumdar, S., Pedoia, V.: Distance map loss penalty term for semantic segmentation (2019). https:\/\/arxiv.org\/abs\/1908.03679"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Improving semantic segmentation via decoupled body and edge supervision. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Proceedings, Part XVII, pp. 435\u2013452. Springer (2020)","DOI":"10.1007\/978-3-030-58520-4_26"},{"key":"29_CR13","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012)"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"29_CR15","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.isprsjprs.2021.02.009","volume":"175","author":"Y Li","year":"2021","unstructured":"Li, Y., Shi, T., Zhang, Y., Chen, W., Wang, Z., Li, H.: Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation. ISPRS J. Photogramm. Remote. Sens. 175, 20\u201333 (2021)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"29_CR16","doi-asserted-by":"publisher","first-page":"16755","DOI":"10.52202\/068431-1219","volume":"35","author":"J Jang","year":"2022","unstructured":"Jang, J., Na, B., Shin, D.H., Ji, M., Song, K., Moon, I.-C.: Unknown-aware domain adversarial learning for open-set domain adaptation. Adv. Neural. Inf. Process. Syst. 35, 16755\u201316767 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Wang, Q., Meng, F., Breckon, T.P.: Progressively select and reject pseudo-labelled samples for open-set domain adaptation. IEEE Trans. Artif. Intell (2024)","DOI":"10.1109\/TAI.2024.3379940"},{"key":"29_CR18","doi-asserted-by":"publisher","first-page":"103258","DOI":"10.1016\/j.cviu.2021.103258","volume":"212","author":"M Bucher","year":"2021","unstructured":"Bucher, M., Vu, T.-H., Cord, M., P\u00e9rez, P.: Handling new target classes in semantic segmentation with domain adaptation. Comput. Vis. Image Understand. 212, 103258 (2021)","journal-title":"Comput. Vis. Image Understand."},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Choe, S.-A., Shin, A.-H., Park, K.-H., Choi, J., Park, G.-M.: Open-set domain adaptation for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23943\u201323953 (2024)","DOI":"10.1109\/CVPR52733.2024.02260"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Chen, K., et al.: RSPrompter: learning to prompt for remote sensing instance segmentation based on visual foundation model. IEEE Trans. Geosci. Remote Sens. (2024)","DOI":"10.1109\/TGRS.2024.3356074"},{"issue":"4","key":"29_CR21","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1007\/s11263-020-01395-y","volume":"129","author":"Z Zheng","year":"2021","unstructured":"Zheng, Z., Yang, Y.: Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation. Int. J. Comput. Vis. 129(4), 1106\u20131120 (2021)","journal-title":"Int. J. Comput. Vis."},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Li, R., Li, S., He, C., Zhang, Y., Jia, X., Zhang, L.: Class-balanced pixel-level self-labeling for domain adaptive semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11593\u201311603 (2022)","DOI":"10.1109\/CVPR52688.2022.01130"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Zhao, X., Mithun, N.C., Rajvanshi, A., Chiu, H.-P., Samarasekera, S.: Unsupervised domain adaptation for semantic segmentation with pseudo label self-refinement. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2399\u20132409 (2024)","DOI":"10.1109\/WACV57701.2024.00239"},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, P., Zhang, B., Zhang, T., Chen, D., Wang, Y., Wen, F.: Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12414\u201312424 (2021)","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"29_CR25","unstructured":"Wang, J., Zheng, Z., Ma, A., Lu, X., Zhong, Y.: LoveDA: a remote sensing land-cover dataset for domain adaptive semantic segmentation (2021). https:\/\/arxiv.org\/abs\/2110.08733"},{"key":"29_CR26","unstructured":"Gerke, M.: Use of the stair vision library within the ISPRS 2D semantic labeling benchmark (Vaihingen). Use of the stair vision library within the ISPRS 2D Semantic Labeling Benchmark (Vaihingen) (2014)"},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Fu, B., Cao, Z., Long, M., Wang, J.: Learning to detect open classes for universal domain adaptation. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Proceedings, Part XV 16, pp. 567\u2013583. Springer (2020)","DOI":"10.1007\/978-3-030-58555-6_34"}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-22261-9_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:13:11Z","timestamp":1783555991000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-22261-9_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032222602","9783032222619"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-22261-9_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"42","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.cgs-network.org\/cgi25","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}