{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T19:23:09Z","timestamp":1783106589823,"version":"3.54.6"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031948947","type":"print"},{"value":"9783031948954","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"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\/978-3-031-94895-4_9","type":"book-chapter","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T15:07:22Z","timestamp":1756307242000},"page":"90-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DDDAS Probability Learning for Natural Disaster Change Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9246-8792","authenticated-orcid":false,"given":"Weicong","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6949-8859","authenticated-orcid":false,"given":"Adarsh","family":"Agrawal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4094-8413","authenticated-orcid":false,"given":"Haibin","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6894-6108","authenticated-orcid":false,"given":"Erik","family":"Blasch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3092-4696","authenticated-orcid":false,"given":"Erika","family":"Adiles-Cruz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6017-0509","authenticated-orcid":false,"given":"Paul T.","family":"Schrader","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1781-6412","authenticated-orcid":false,"given":"Jie","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Anindya, B., Das, S.: Chapter 22 - afforestation, revegetation, and regeneration: a case study on Purulia district, West Bengal (India). In: Gouri Sankar, B., et al., (eds.) Land Reclamation and Restoration Strategies for Sustainable Development, pp. 497\u2013524. Academic Press (2021)","DOI":"10.1016\/B978-0-12-823895-0.00014-2"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Shen, Y., et al.: BDANet: multiscale convolutional neural network with cross-directional attention for building damage assessment from satellite images. IEEE Trans. Geosci. Remote Sens. 1\u201314 (2021)","DOI":"10.1109\/TGRS.2021.3080580"},{"issue":"2","key":"9_CR3","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/S1053-8119(03)00406-3","volume":"20","author":"M Bosc","year":"2003","unstructured":"Bosc, M., Heitz, F., Armspach, J.P., Namer, I., Gounot, D., Rumbach, L.: Automatic change detection in multimodal serial MRI: application to multiple sclerosis lesion evolution. Neuroimage 20(2), 643\u2013656 (2003)","journal-title":"Neuroimage"},{"issue":"2","key":"9_CR4","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/S2095-3119(20)63458-X","volume":"20","author":"CAO Dan","year":"2021","unstructured":"Dan, C.A.O., et al.: Delineating the rice crop activities in Northeast China through regional parametric synthesis using satellite remote sensing time-series data from 2000 to 2015. J. Integr. Agric. 20(2), 424\u2013437 (2021)","journal-title":"J. Integr. Agric."},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Wei, J., et al.: Deep learning approach for data and computing efficient disaster mitigation in humanitarian assistance and disaster response applications, pp. 79\u201385 (2022)","DOI":"10.1109\/IHTC56573.2022.9998394"},{"key":"9_CR6","unstructured":"Wei, J., et al.: NIDA-CLIFGAN: natural infrastructure damage assessment through efficient classification combining contrastive learning, information fusion and generative adversarial networks. ArXiv, 2021. abs\/2110.14518"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Bernhard, M., Strau\u00df, N., Schubert, M.: MapFormer: boosting change detection by using pre-change information (2023)","DOI":"10.1109\/ICCV51070.2023.01544"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Wei, J., et al.: Deep learning approach for data and computing efficient situational assessment and awareness in human assistance and disaster response and battlefield damage assessment applications, pp. 187\u2013195 (2024)","DOI":"10.1007\/978-3-031-52670-1_18"},{"key":"9_CR9","unstructured":"Eugene Khvedchenya, T.G.: Fully convolutional Siamese neural networks for buildings damage assessment from satellite images. 2021: arXiv.org"},{"key":"9_CR10","unstructured":"Gupta, R., et al.: xBD: a dataset for assessing building damage from satellite imagery. ArXiv, 2019. abs\/1911.09296"},{"issue":"4","key":"9_CR11","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.3390\/rs14041012","volume":"14","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Cui, L., Zhang, C., Chen, W., Xu, Y., Zhang, Q.: A two-stage seismic damage assessment method for small, dense, and imbalanced buildings in remote sensing images. Remote Sens. 14(4), 1012 (2022)","journal-title":"Remote Sens."},{"key":"9_CR12","doi-asserted-by":"publisher","first-page":"4232","DOI":"10.1109\/JSTARS.2023.3267847","volume":"16","author":"ST Seydi","year":"2023","unstructured":"Seydi, S.T., Hasanlou, M., Chanussot, J., Ghamisi, P.: BDD-Net+: a building damage detection framework based on modified coat-net. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 16, 4232\u20134247 (2023)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"9_CR13","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.isprsjprs.2022.11.010","volume":"195","author":"J Ge","year":"2023","unstructured":"Ge, J., Tang, H., Yang, N., Hu, Y.: Rapid identification of damaged buildings using incremental learning with transferred data from historical natural disaster cases. ISPRS J. Photogrammetry Remote Sens. 195, 105\u2013128 (2023)","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"issue":"11","key":"9_CR14","doi-asserted-by":"publisher","first-page":"1794","DOI":"10.3390\/rs12111794","volume":"12","author":"N Yang","year":"2020","unstructured":"Yang, N., Tang, H.: GeoBoost: an incremental deep learning approach toward global mapping of buildings from VHR remote sensing images. Remote Sens. 12(11), 1794 (2020)","journal-title":"Remote Sens."},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Zhu, J-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp. 2242\u20132251 (20170","DOI":"10.1109\/ICCV.2017.244"},{"key":"9_CR16","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/JSTARS.2019.2954292","volume":"13","author":"Q Chen","year":"2019","unstructured":"Chen, Q., Yang, H., Li, L., Liu, X.: A novel statistical texture feature for SAR building damage assessment in different polarization modes. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 154\u2013165 (2019)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"7","key":"9_CR17","doi-asserted-by":"publisher","first-page":"886","DOI":"10.3390\/rs11070886","volume":"11","author":"B Adriano","year":"2019","unstructured":"Adriano, B., et al.: Multi-source data fusion based on ensemble learning for rapid building damage mapping during the 2018 Sulawesi earthquake and Tsunami in Palu, Indonesia. Remote Sens. 11(7), 886 (2019)","journal-title":"Remote Sens."},{"key":"9_CR18","doi-asserted-by":"publisher","first-page":"102783","DOI":"10.1016\/j.cviu.2019.07.003","volume":"187","author":"D Rodrigo","year":"2019","unstructured":"Rodrigo, D., et al.: Multitask learning for large-scale semantic change detection. Comput. Vis. Image Underst. 187, 102783 (2019)","journal-title":"Comput. Vis. Image Underst."},{"key":"9_CR19","unstructured":"Bromley, J., et al.: Signature verification using a \u201cSiamese\u201d time delay neural network. In: Proceedings of the 6th International Conference on Neural Information Processing Systems, pp. 737\u2013744. Morgan Kaufmann Publishers Inc.: Denver, Colorado (1993)"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Olaf Ronneberger, P.F., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., et al., (eds.) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015, pp. 234\u2013241. Springer, Cham (2015)","DOI":"10.1007\/978-3-319-24574-4_28"}],"container-title":["Lecture Notes in Computer Science","Dynamic Data Driven Applications Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-94895-4_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T19:14:55Z","timestamp":1783106095000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-94895-4_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,26]]},"ISBN":["9783031948947","9783031948954"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-94895-4_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,26]]},"assertion":[{"value":"26 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DDDAS\/Infosymbiotics for Reliable AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Dynamic Data Driven Applications Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Brunswick, NJ","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dddas2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dddas2024.rutgers.edu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}