{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T11:24:05Z","timestamp":1768994645490,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556274","type":"print"},{"value":"9789819556281","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\/978-981-95-5628-1_40","type":"book-chapter","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:30:00Z","timestamp":1768944600000},"page":"582-596","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Boundary-enhanced Semantic Change Detection Network via\u00a0Synergistic Multi-task Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2985-2786","authenticated-orcid":false,"given":"Mingliang","family":"Xue","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanlong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongqing","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"40_CR1","first-page":"1","volume":"62","author":"C Pang","year":"2024","unstructured":"Pang, C., Weng, X., Wu, J., Wang, Q., Xia, G.S.: Hicd: change detection in quality-varied images via hierarchical correlation distillation. IEEE Trans. Geosci. Remote Sens. 62, 1\u201316 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"Lunetta, R.S., Knight, J.F., Ediriwickrema, J., Lyon, J.G., Worthy, L.D.: Land-cover change detection using multi-temporal modis ndvi data. In: Geospatial Information Handbook for Water Resources and Watershed Management, Volume II, pp. 65\u201388. CRC Press (2022)","DOI":"10.1201\/9781003175025-5"},{"key":"40_CR3","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","volume":"14","author":"J Chen","year":"2020","unstructured":"Chen, J., et al.: Dasnet: dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images. IEEE J. Selected Top. Appl. Earth Observ. Remote Sens. 14, 1194\u20131206 (2020)","journal-title":"IEEE J. Selected Top. Appl. Earth Observ. Remote Sens."},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Lv, Z.Y., Shi, W., Zhang, X., Benediktsson, J.A.: Landslide inventory mapping from bitemporal high-resolution remote sensing images using change detection and multiscale segmentation. IEEE J. Selected Top. Appl. Earth Observ. Remote Sens. 11(5), 1520\u20131532 (2018)","DOI":"10.1109\/JSTARS.2018.2803784"},{"issue":"9","key":"40_CR5","doi-asserted-by":"publisher","first-page":"7651","DOI":"10.1109\/TGRS.2021.3055584","volume":"59","author":"M Papadomanolaki","year":"2021","unstructured":"Papadomanolaki, M., Vakalopoulou, M., Karantzalos, K.: A deep multitask learning framework coupling semantic segmentation and fully convolutional lstm networks for urban change detection. IEEE Trans. Geosci. Remote Sens. 59(9), 7651\u20137668 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Liu, C., Chen, K., Zhang, H., Qi, Z., Zou, Z., Shi, Z.: Change-agent: towards interactive comprehensive remote sensing change interpretation and analysis. IEEE Trans. Geosci. Remote Sens. (2024)","DOI":"10.1109\/TGRS.2024.3425815"},{"issue":"4","key":"40_CR7","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1109\/TIP.2017.2784560","volume":"27","author":"Z Liu","year":"2017","unstructured":"Liu, Z., Li, G., Mercier, G., He, Y., Pan, Q.: Change detection in heterogenous remote sensing images via homogeneous pixel transformation. IEEE Trans. Image Process. 27(4), 1822\u20131834 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"40_CR8","doi-asserted-by":"publisher","first-page":"78909","DOI":"10.1109\/ACCESS.2019.2922839","volume":"7","author":"L Xu","year":"2019","unstructured":"Xu, L., Jing, W., Song, H., Chen, G.: High-resolution remote sensing image change detection combined with pixel-level and object-level. IEEE Access 7, 78909\u201378918 (2019)","journal-title":"IEEE Access"},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: Remote sensing image semantic change detection boosted by semi-supervised contrastive learning of semantic segmentation. IEEE Trans. Geosci. Remote Sens. (2024)","DOI":"10.1109\/TGRS.2024.3395135"},{"key":"40_CR10","first-page":"1","volume":"60","author":"K Yang","year":"2021","unstructured":"Yang, K., et al.: Asymmetric siamese networks for semantic change detection in aerial images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201318 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"40_CR11","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1109\/TIP.2004.838698","volume":"14","author":"RJ Radke","year":"2005","unstructured":"Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: a systematic survey. IEEE Trans. Image Process. 14(3), 294\u2013307 (2005)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"40_CR12","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/S0034-4257(97)00112-0","volume":"63","author":"MK Ridd","year":"1998","unstructured":"Ridd, M.K., Liu, J.: A comparison of four algorithms for change detection in an urban environment. Remote Sens. Environ. 63(2), 95\u2013100 (1998)","journal-title":"Remote Sens. Environ."},{"issue":"1","key":"40_CR13","first-page":"9384","volume":"14","author":"FE Onuegbu","year":"2024","unstructured":"Onuegbu, F.E., Egbu, A.U.: Employing post classification comparison to detect land use cover change patterns and quantify conversions in abakaliki lga nigeria from 2000 to 2022. Sci. Reports 14(1), 9384 (2024)","journal-title":"Sci. Reports"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"Qi, Z., Yeh, A.G.O.: Integrating change vector analysis, post-classification comparison, and object-oriented image analysis for land use and land cover change detection using radarsat-2 polarimetric SAR images. In: Advances in Spatial Data Handling: Geospatial Dynamics, Geosimulation and Exploratory Visualization, pp. 107\u2013123. Springer (2012)","DOI":"10.1007\/978-3-642-32316-4_8"},{"key":"40_CR15","first-page":"1","volume":"60","author":"H Xia","year":"2022","unstructured":"Xia, H., Tian, Y., Zhang, L., Li, S.: A deep siamese postclassification fusion network for semantic change detection. IEEE Trans. Geosci. Remote Sens. 60, 1\u201316 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"40_CR16","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.isprsjprs.2020.01.026","volume":"161","author":"P Du","year":"2020","unstructured":"Du, P., Wang, X., Chen, D., Liu, S., Lin, C., Meng, Y.: An improved change detection approach using tri-temporal logic-verified change vector analysis. ISPRS J. Photogrammetry Remote Sens. 161, 278\u2013293 (2020)","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"40_CR17","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s10666-021-09758-6","volume":"26","author":"GR Sapucci","year":"2021","unstructured":"Sapucci, G.R., Negri, R.G., Casaca, W., Massi, K.G.: Analyzing spatio-temporal land cover dynamics in an atlantic forest portion using unsupervised change detection techniques. Environ. Modeling Assess. 26, 581\u2013590 (2021)","journal-title":"Environ. Modeling Assess."},{"key":"40_CR18","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.isprsjprs.2021.12.005","volume":"184","author":"Q Zhu","year":"2022","unstructured":"Zhu, Q., et al.: Land-use\/land-cover change detection based on a siamese global learning framework for high spatial resolution remote sensing imagery. ISPRS J. Photogrammetry Remote Sens. 184, 63\u201378 (2022)","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"issue":"9","key":"40_CR19","doi-asserted-by":"publisher","first-page":"2940","DOI":"10.1109\/TGRS.2007.902824","volume":"45","author":"F Pacifici","year":"2007","unstructured":"Pacifici, F., Del Frate, F., Solimini, C., Emery, W.J.: An innovative neural-net method to detect temporal changes in high-resolution optical satellite imagery. IEEE Trans. Geosci. Remote Sens. 45(9), 2940\u20132952 (2007)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"40_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2019.07.003","volume":"187","author":"RC Daudt","year":"2019","unstructured":"Daudt, R.C., Le Saux, B., Boulch, A., Gousseau, Y.: Multitask learning for large-scale semantic change detection. Comput. Vision Image Understand. 187, 102783 (2019)","journal-title":"Comput. Vision Image Understand."},{"key":"40_CR21","volume":"118","author":"F Cui","year":"2023","unstructured":"Cui, F., Jiang, J.: MTSCD-net: a network based on multi-task learning for semantic change detection of bitemporal remote sensing images. Int. J. Appl. Earth Observ. Geoinf. 118, 103294 (2023)","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Wang, Q., Jing, W., Chi, K., Yuan, Y.: Cross-difference semantic consistency network for semantic change detection. IEEE Trans. Geosci. Remote Sens. (2024)","DOI":"10.1109\/TGRS.2024.3386334"},{"key":"40_CR23","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.isprsjprs.2021.10.015","volume":"183","author":"Z Zheng","year":"2022","unstructured":"Zheng, Z., Zhong, Y., Tian, S., Ma, A., Zhang, L.: Changemask: deep multi-task encoder-transformer-decoder architecture for semantic change detection. ISPRS J. Photogrammetry Remote Sens. 183, 228\u2013239 (2022)","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"40_CR24","first-page":"1","volume":"60","author":"L Ding","year":"2022","unstructured":"Ding, L., Guo, H., Liu, S., Mou, L., Zhang, J., Bruzzone, L.: Bi-temporal semantic reasoning for the semantic change detection in hr remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"40_CR25","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.isprsjprs.2024.04.013","volume":"211","author":"K Tang","year":"2024","unstructured":"Tang, K., Xu, F., Chen, X., Dong, Q., Yuan, Y., Chen, J.: The clearscd model: Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery. ISPRS J. Photogrammetry Remote Sens. 211, 299\u2013317 (2024)","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"40_CR26","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"40_CR28","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.isprsjprs.2022.08.012","volume":"193","author":"S Tian","year":"2022","unstructured":"Tian, S., Zhong, Y., Zheng, Z., Ma, A., Tan, X., Zhang, L.: Large-scale deep learning based binary and semantic change detection in ultra high resolution remote sensing imagery: from benchmark datasets to urban application. ISPRS J. Photogrammetry Remote Sens. 193, 164\u2013186 (2022)","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"40_CR29","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.isprsjprs.2025.04.030","volume":"225","author":"J Long","year":"2025","unstructured":"Long, J., Liu, S., Li, M., Zhao, H., Jin, Y.: Bgsnet: A boundary-guided siamese multitask network for semantic change detection from high-resolution remote sensing images. ISPRS J. Photogrammetry Remote Sens. 225, 221\u2013237 (2025)","journal-title":"ISPRS J. Photogrammetry Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5628-1_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:30:04Z","timestamp":1768944604000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5628-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556274","9789819556281"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5628-1_40","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":"21 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}