{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T19:10:04Z","timestamp":1748200204386,"version":"3.41.0"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031923869","type":"print"},{"value":"9783031923876","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-92387-6_16","type":"book-chapter","created":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T18:43:07Z","timestamp":1748198587000},"page":"208-216","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Fusion Strategies for\u00a0Mapping Biophysical Landscape Features"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3219-6960","authenticated-orcid":false,"given":"Lucia","family":"Gordon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8434-027X","authenticated-orcid":false,"given":"Nico","family":"Lang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catherine","family":"Ressijac","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0003-1435","authenticated-orcid":false,"given":"Andrew","family":"Davies","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","unstructured":"Burke, M., Driscoll, A., Lobell, D.B., Ermon, S.: Using satellite imagery to understand and promote sustainable development. Science 371(6535) (2021). https:\/\/doi.org\/10.1126\/science.abe8628, https:\/\/www.science.org\/doi\/10.1126\/science.abe8628","DOI":"10.1126\/science.abe8628"},{"key":"16_CR2","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848, https:\/\/ieeexplore.ieee.org\/document\/5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"16_CR3","unstructured":"of\u00a0Economic, U.N.D., Affairs, S.: Goal 15: protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss (2023). https:\/\/sdgs.un.org\/goals\/goal15. Accessed 24 May 2023"},{"key":"16_CR4","unstructured":"Eigen, D., Ranzato, M.A., Sutskever, I.: Learning factored representations in a deep mixture of experts. In: ICLR (2014). https:\/\/arxiv.org\/pdf\/1312.4314"},{"key":"16_CR5","doi-asserted-by":"publisher","unstructured":"Gordon, L., et al.: Find rhinos without finding rhinos: active learning with multimodal imagery of South African rhino habitats. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pp. 5977\u20135985. International Joint Conferences on Artificial Intelligence Organization (2023). https:\/\/doi.org\/10.24963\/ijcai.2023\/663, https:\/\/www.ijcai.org\/proceedings\/2023\/0663.pdf. aI for Good","DOI":"10.24963\/ijcai.2023\/663"},{"key":"16_CR6","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90, https:\/\/arxiv.org\/pdf\/1512.03385","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR7","doi-asserted-by":"publisher","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. In: Proceedings of the the Eighth International Conference on Learning Representations, ICLR-20. International Conference on Learning Representations (2020). https:\/\/doi.org\/10.48550\/arXiv.1910.09217, https:\/\/arxiv.org\/pdf\/1910.09217","DOI":"10.48550\/arXiv.1910.09217"},{"key":"16_CR8","doi-asserted-by":"publisher","unstructured":"Loveridge, J.P., Moe, S.R.: Termitaria as browsing hotspots for African megaherbivores in miombo woodland. J. Trop. Ecol. 20(3), 337\u2013343 (2004). https:\/\/doi.org\/10.1017\/S0266467403001202, https:\/\/www.cambridge.org\/core\/journals\/journal-of-tropical-ecology\/article\/abs\/termitaria-as-browsing-hotspots-for-african-megaherbivores-in-miombo-woodland\/E2809392DD9ECB34C99A8FD0CA56EA4F","DOI":"10.1017\/S0266467403001202"},{"key":"16_CR9","unstructured":"Owen-Smith, R.N., Smith, R.N.O.: The behavioural ecology of the white rhinoceros. Ph.D. thesis, University of Wisconsin Madison (1973). https:\/\/www.rhinoresourcecenter.com\/pdf_files\/132\/1320739004.pdf"},{"key":"16_CR10","doi-asserted-by":"publisher","unstructured":"Parrish, J.D., Braun, D.P., Unnasch, R.S.: Are we conserving what we say we are? Measuring ecological integrity within protected areas. Bioscience 53(9), 851\u2013860 (2003). https:\/\/doi.org\/10.1641\/0006-3568(2003)053[0851:AWCWWS]2.0.CO;2, https:\/\/academic.oup.com\/bioscience\/article-abstract\/53\/9\/851\/311604?redirectedFrom=PDF","DOI":"10.1641\/0006-3568(2003)053[0851:AWCWWS]2.0.CO;2"},{"key":"16_CR11","unstructured":"Rolf, E., Klemmer, K., Robinson, C., Kerner, H.: Mission critical \u2013 satellite data is a distinct modality in machine learning (2024). https:\/\/arxiv.org\/pdf\/2402.01444"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Yeh, C., et al.: Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nat. Commun. 11(2583) (2020). https:\/\/www.nature.com\/articles\/s41467-020-16185-w","DOI":"10.1038\/s41467-020-16185-w"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-92387-6_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T18:43:09Z","timestamp":1748198589000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-92387-6_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031923869","9783031923876"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-92387-6_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}