{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T17:03:56Z","timestamp":1789319036152,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032375919","type":"print"},{"value":"9783032375926","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\/978-3-032-37592-6_10","type":"book-chapter","created":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T16:42:43Z","timestamp":1789317763000},"page":"167-184","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CRD-Net: Frequency-Adaptive Feature Injection and\u00a0Change Decoupling for\u00a0Building Damage Assessment"],"prefix":"10.1007","author":[{"given":"Yao","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6843-6722","authenticated-orcid":false,"given":"Yuanxin","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tan","family":"Shu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liwei","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"issue":"15","key":"10_CR1","doi-asserted-by":"publisher","first-page":"2389","DOI":"10.1111\/mice.13197","volume":"39","author":"AM Braik","year":"2024","unstructured":"Braik, A.M., Koliou, M.: Automated building damage assessment and large-scale mapping by integrating satellite imagery, gis, and deep learning. Comput. Aided Civil Infrastr. Eng. 39(15), 2389\u20132404 (2024)","journal-title":"Comput. Aided Civil Infrastr. Eng."},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF iNternational Conference on Computer Vision, pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Chen, H., Nemni, E., Vallecorsa, S., Li, X., Wu, C., Bromley, L.: Dual-tasks siamese transformer framework for building damage assessment. In: IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. pp. 1600\u20131603. IEEE (2022)","DOI":"10.1109\/IGARSS46834.2022.9883139"},{"key":"10_CR4","first-page":"1","volume":"62","author":"H Chen","year":"2024","unstructured":"Chen, H., Song, J., Han, C., Xia, J., Yokoya, N.: Changemamba: remote sensing change detection with spatiotemporal state space model. IEEE Trans. Geosci. Remote Sens. 62, 1\u201320 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10_CR5","first-page":"1","volume":"21","author":"K Chen","year":"2024","unstructured":"Chen, K., Chen, B., Liu, C., Li, W., Zou, Z., Shi, Z.: Rsmamba: remote sensing image classification with state space model. IEEE Geosci. Remote Sens. Lett. 21, 1\u20135 (2024)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, C.H., Hsu, C.C.: Changedino: dinov3-driven building change detection in optical remote sensing imagery (2025). arXiv:2511.16322 arXiv preprint","DOI":"10.5194\/isprs-annals-XI-3-2026-523-2026"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Dong, S., Hu, Y., Wang, L., Chen, G., Meng, X.: PEFTCD: leveraging vision foundation models with parameter-efficient fine-tuning for remote sensing change detection (2025). arXiv:2509.09572 arXiv preprint","DOI":"10.1109\/JSTARS.2026.3679260"},{"key":"10_CR8","unstructured":"Gao, Y., Li, H., Yuan, F., Wang, X., Gao, X.: Dino u-net: exploiting high-fidelity dense features from foundation models for medical image segmentation. arXiv preprint arXiv:2508.20909 (2025)"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Guo, X., Kang, X., Wang, Z., Wu, Q., Duan, P., Yang, B., Li, S.: Flowmamba: Building damage assessment via optics flow-based state space model. IEEE Trans. Circuits Syst. Video Tech. (2025)","DOI":"10.2139\/ssrn.5339773"},{"key":"10_CR10","unstructured":"Gupta, R., et al.: Creating XBD: a dataset for assessing building damage from satellite imagery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 10\u201317 (2019)"},{"issue":"1","key":"10_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41651-024-00202-3","volume":"9","author":"Y Han","year":"2025","unstructured":"Han, Y., Li, J., Qu, Y., Wang, L., Pan, X., Huang, X.: HFNET: semantic and differential heterogenous fusion network for remote sensing image change detection. J. Geovisualization Spatial Anal. 9(1), 1 (2025)","journal-title":"J. Geovisualization Spatial Anal."},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Hao, H., et al.: An attention-based system for damage assessment using satellite imagery. In: 2021 IEEE international geoscience and remote sensing symposium IGARSS, pp. 4396\u20134399. IEEE (2021)","DOI":"10.1109\/IGARSS47720.2021.9554054"},{"issue":"2","key":"10_CR13","first-page":"3","volume":"1","author":"EJ Hu","year":"2022","unstructured":"Hu, E.J., et al.: Lora: low-rank adaptation of large language models. ICLR 1(2), 3 (2022)","journal-title":"ICLR"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Hu, L., Lu, W., Yu, H., Yin, D., Sun, X., Fu, K.: Tea: a training-efficient adapting framework for tuning foundation models in remote sensing. IEEE Trans. Geosci. Remote Sens. (2024)","DOI":"10.1109\/TGRS.2024.3488731"},{"key":"10_CR15","unstructured":"Huang, S., Hou, Y., Liu, L., Yu, X., Shen, X.: Real-time object detection meets dinov3 (2025). arXiv:2509.20787 arXiv preprint"},{"key":"10_CR16","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.isprsjprs.2025.09.010","volume":"230","author":"W Jiang","year":"2025","unstructured":"Jiang, W., Sun, Y., Lei, L., Kuang, G., Ji, K.: Adaptvfms-rscd: advancing remote sensing change detection from binary to semantic with sam and clip. ISPRS J. Photogramm. Remote. Sens. 230, 304\u2013317 (2025)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"15","key":"10_CR17","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1111\/mice.12981","volume":"38","author":"N Kaur","year":"2023","unstructured":"Kaur, N., Lee, C.C., Mostafavi, A., Mahdavi-Amiri, A.: Large-scale building damage assessment using a novel hierarchical transformer architecture on satellite images. Comput. Aided Civil Infrast. Eng. 38(15), 2072\u20132091 (2023)","journal-title":"Comput. Aided Civil Infrast. Eng."},{"key":"10_CR18","unstructured":"Kirillov, A., et al.: Segment anything. Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026. (2023)."},{"key":"10_CR19","unstructured":"Liao, Z., et al.: Rt-detrv4: painlessly furthering real-time object detection with vision foundation models (2025). arXiv:2510.25257 arXiv preprint"},{"key":"10_CR20","first-page":"1","volume":"60","author":"C Lin","year":"2021","unstructured":"Lin, C., Li, Y., Liu, Y., Wang, X., Geng, S.: Building damage assessment from post-hurricane imageries using unsupervised domain adaptation with enhanced feature discrimination. IEEE Trans. Geosci. Remote Sens. 60, 1\u201310 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"4917","DOI":"10.1109\/JSTARS.2024.3354310","volume":"17","author":"W Lu","year":"2024","unstructured":"Lu, W., Wei, L., Nguyen, M.: Bitemporal attention transformer for building change detection and building damage assessment. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 17, 4917\u20134935 (2024)","journal-title":"IEEE J. Select. Topics Appl. Earth Observ. Remote Sens."},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Ma, X., Wu, Q., Zhao, X., Zhang, X., Pun, M.O., Huang, B.: Sam-assisted remote sensing imagery semantic segmentation with object and boundary constraints. IEEE Trans. Geosci. Remote Sens. (2024)","DOI":"10.1109\/TGRS.2024.3443420"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Ma, X., Zhang, X., Pun, M.O., Huang, B.: A unified framework with multimodal fine-tuning for remote sensing semantic segmentation. IEEE Trans. Geosci. Remote Sens. (2025)","DOI":"10.1109\/TGRS.2025.3585238"},{"key":"10_CR24","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PmLR (2021)"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Sha, X., Guo, Z., Sang, X., Wang, S., Zhao, Y.: GES: a new building damage data augmentation and detection method based on extremely imbalanced data and unique spatial distribution of satellite images. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. (2024)","DOI":"10.1109\/JSTARS.2024.3435074"},{"key":"10_CR26","unstructured":"Sim\u00e9oni, O., et\u00a0al.: Dinov3. arXiv preprint arXiv:2508.10104 (2025)"},{"issue":"2","key":"10_CR27","doi-asserted-by":"publisher","first-page":"282","DOI":"10.3390\/rs18020282","volume":"18","author":"K Tan","year":"2026","unstructured":"Tan, K., Wu, Y., Yang, H., Ma, X.: UGFF-VLM: uncertainty-guided and frequency-fused vision-language model for remote sensing farmland segmentation. Remote Sens. 18(2), 282 (2026)","journal-title":"Remote Sens."},{"key":"10_CR28","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.isprsjprs.2017.03.001","volume":"140","author":"A Vetrivel","year":"2018","unstructured":"Vetrivel, A., Gerke, M., Kerle, N., Nex, F., Vosselman, G.: Disaster damage detection through synergistic use of deep learning and 3d point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning. ISPRS J. Photogramm. Remote. Sens. 140, 45\u201359 (2018)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Wang, D., et al.: MTP: advancing remote sensing foundation model via multitask pretraining. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 17, 11632\u201311654 (2024)","DOI":"10.1109\/JSTARS.2024.3408154"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Wang, L., Wu, J., Yang, Y., Tang, R., Ya, R.: Deep learning models for hazard-damaged building detection using remote sensing datasets: A comprehensive review. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. (2024)","DOI":"10.1109\/JSTARS.2024.3449097"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Wang, M., Zhou, L., Zhang, K., Li, X., Hao, M., Ye, Y.: ESAM-CD: fine-tuned efficientsam network with lora for weakly supervised remote sensing image change detection. IEEE Trans. Geosci. Remote Sens. (2024)","DOI":"10.1109\/TGRS.2024.3470808"},{"key":"10_CR32","doi-asserted-by":"publisher","first-page":"0733","DOI":"10.34133\/remotesensing.0733","volume":"5","author":"Z Wang","year":"2025","unstructured":"Wang, Z., Wu, C., Zhang, F., Xia, J.: Constructing an extensible building damage dataset via semi-supervised fine-tuning across 12 natural disasters. J. Remote Sens. 5, 0733 (2025)","journal-title":"J. Remote Sens."},{"issue":"5","key":"10_CR33","doi-asserted-by":"publisher","first-page":"905","DOI":"10.3390\/rs13050905","volume":"13","author":"C Wu","year":"2021","unstructured":"Wu, C., et al.: Building damage detection using u-net with attention mechanism from pre-and post-disaster remote sensing datasets. Remote Sens. 13(5), 905 (2021)","journal-title":"Remote Sens."},{"key":"10_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103547","volume":"102","author":"J Wu","year":"2025","unstructured":"Wu, J., et al.: Medical sam adapter: adapting segment anything model for medical image segmentation. Med. Image Anal. 102, 103547 (2025)","journal-title":"Med. Image Anal."},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Xu, J., et al.: Visual foundation models boost cross-modal unsupervised domain adaptation for 3d semantic segmentation. IEEE Trans. Intell. Transp. Syst. (2025)","DOI":"10.1109\/TITS.2025.3587430"},{"key":"10_CR36","unstructured":"Yang, S., Wang, H., Xing, Z., Chen, S., Zhu, L.: Segdino: an efficient design for medical and natural image segmentation with dino-v3 (2025). arXiv:2509.00833 arXiv preprint"},{"key":"10_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2021.112636","volume":"265","author":"Z Zheng","year":"2021","unstructured":"Zheng, Z., Zhong, Y., Wang, J., Ma, A., Zhang, L.: Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters. Remote Sens. Environ. 265, 112636 (2021)","journal-title":"Remote Sens. Environ."},{"key":"10_CR38","unstructured":"Zhenga, K., et al.: CSD: change semantic detection with only semantic change masks for damage assessment in conflict zones. arXiv e-prints pp. arXiv\u20132511 (2025)"},{"key":"10_CR39","unstructured":"Zhou, J., Wei, C., Wang, H., Shen, W., Xie, C., Yuille, A., Kong, T.: IBOT: image BERT pre-training with online tokenizer (2021). arXiv:2111.07832 arXiv preprint"},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Zhu, D., Huang, X., Huang, H., Cheng, Q., Huang, Z., Shao, Z.: Changevit: unleashing plain vision transformers for change detection in remote sensing images. Pattern Recogn. 112539 (2025)","DOI":"10.2139\/ssrn.5197294"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37592-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T16:42:50Z","timestamp":1789317770000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37592-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032375919","9783032375926"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37592-6_10","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":"14 September 2026","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":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}