{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:23:19Z","timestamp":1743070999704,"version":"3.40.3"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031729799"},{"type":"electronic","value":"9783031729805"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72980-5_6","type":"book-chapter","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T09:15:43Z","timestamp":1730106943000},"page":"94-111","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Get Your Embedding Space in\u00a0Order: Domain-Adaptive Regression for\u00a0Forest Monitoring"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9583-7230","authenticated-orcid":false,"given":"Sizhuo","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8135-1341","authenticated-orcid":false,"given":"Dimitri","family":"Gominski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9531-1239","authenticated-orcid":false,"given":"Martin","family":"Brandt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9709-0633","authenticated-orcid":false,"given":"Xiaoye","family":"Tong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8560-4943","authenticated-orcid":false,"given":"Philippe","family":"Ciais","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,29]]},"reference":[{"key":"6_CR1","unstructured":"Trees outside forests - towards a better awareness. https:\/\/www.fao.org\/3\/y2328e\/y2328e25.htm"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Akiva, P., Purri, M., Leotta, M.: Self-supervised material and texture representation learning for remote sensing tasks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8203\u20138215 (2022)","DOI":"10.1109\/CVPR52688.2022.00803"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Beery, S., Van\u00a0Horn, G., Perona, P.: Recognition in terra incognita. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 472\u2013489 (2018)","DOI":"10.1007\/978-3-030-01270-0_28"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Beery, S., et al.: The auto arborist dataset: a large-scale benchmark for multiview urban forest monitoring under domain shift. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21262\u201321275 (2022)","DOI":"10.1109\/CVPR52688.2022.02061"},{"issue":"7832","key":"6_CR5","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1038\/s41586-020-2824-5","volume":"587","author":"M Brandt","year":"2020","unstructured":"Brandt, M., et al.: An unexpectedly large count of trees in the west African Sahara and Sahel. Nature 587(7832), 78\u201382 (2020)","journal-title":"Nature"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Burgert, T., Ravanbakhsh, M., Demir, B.: On the effects of different types of label noise in multi-label remote sensing image classification. IEEE Trans. Geosci. Remote Sens. (2022)","DOI":"10.1109\/TGRS.2022.3226371"},{"issue":"3","key":"6_CR7","doi-asserted-by":"publisher","first-page":"476","DOI":"10.3390\/rs14030476","volume":"14","author":"G Chen","year":"2022","unstructured":"Chen, G., Shang, Y.: Transformer for tree counting in aerial images. Remote Sens. 14(3), 476 (2022)","journal-title":"Remote Sens."},{"key":"6_CR8","unstructured":"Chen, X., Wang, S., Wang, J., Long, M.: Representation subspace distance for domain adaptation regression. In: International Conference on Machine Learning, pp. 1749\u20131759 (2021)"},{"issue":"12","key":"6_CR9","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.3390\/rs8121034","volume":"8","author":"S Deng","year":"2016","unstructured":"Deng, S., Katoh, M., Yu, X., Hyypp\u00e4, J., Gao, T.: Comparison of tree species classifications at the individual tree level by combining ALS data and RGB images using different algorithms. Remote Sens. 8(12), 1034 (2016)","journal-title":"Remote Sens."},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Donoser, M., Bischof, H.: Diffusion processes for retrieval revisited. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1320\u20131327 (2013)","DOI":"10.1109\/CVPR.2013.174"},{"key":"6_CR11","unstructured":"Dosovitskiy, A., et al.: An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021)"},{"key":"6_CR12","unstructured":"DrivenData: The BioMassters. https:\/\/www.drivendata.org\/competitions\/99\/biomass-estimation\/page\/534\/"},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"113945","DOI":"10.1016\/j.rse.2023.113945","volume":"302","author":"I Fayad","year":"2024","unstructured":"Fayad, I., et al.: Hy-TeC: a hybrid vision transformer model for high-resolution and large-scale mapping of canopy height. Remote Sens. Environ. 302, 113945 (2024)","journal-title":"Remote Sens. Environ."},{"key":"6_CR14","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a070, pp. 1126\u20131135. PMLR (2017)"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Girard, N., Charpiat, G., Tarabalka, Y.: Noisy supervision for correcting misaligned cadaster maps without perfect ground truth data. In: 2019 IEEE International Geoscience and Remote Sensing Symposium, pp. 10103\u201310106 (2019)","DOI":"10.1109\/IGARSS.2019.8898071"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Iscen, A., Tolias, G., Avrithis, Y., Chum, O.: Label propagation for deep semi-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00521"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Iscen, A., Tolias, G., Avrithis, Y., Furon, T., Chum, O.: Efficient diffusion on region manifolds: Recovering small objects with compact CNN representations. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 926\u2013935 (2017)","DOI":"10.1109\/CVPR.2017.105"},{"issue":"1","key":"6_CR18","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1111\/gcb.13388","volume":"23","author":"T Jucker","year":"2017","unstructured":"Jucker, T., et al.: Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Glob. Change Biol. 23(1), 177\u2013190 (2017)","journal-title":"Glob. Change Biol."},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Kalinicheva, E., Landrieu, L., Mallet, C., Chehata, N.: Multi-layer modeling of dense vegetation from aerial LiDAR scans. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1342\u20131351 (2022)","DOI":"10.1109\/CVPRW56347.2022.00140"},{"key":"6_CR20","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.rse.2017.11.018","volume":"205","author":"N Knapp","year":"2018","unstructured":"Knapp, N., Fischer, R., Huth, A.: Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states. Remote Sens. Environ. 205, 199\u2013209 (2018)","journal-title":"Remote Sens. Environ."},{"key":"6_CR21","unstructured":"Kundu, J.N., Venkat, N., Babu, R.V.: Universal source-free domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)"},{"issue":"11","key":"6_CR22","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.1038\/s41559-023-02206-6","volume":"7","author":"N Lang","year":"2023","unstructured":"Lang, N., Jetz, W., Schindler, K., Wegner, J.D.: A high-resolution canopy height model of the earth. Nat. Ecol. Evol. 7(11), 1778\u20131789 (2023)","journal-title":"Nat. Ecol. Evol."},{"key":"6_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1007\/978-3-031-19778-9_12","volume-title":"Computer Vision \u2013 ECCV 2022","author":"SH Lee","year":"2022","unstructured":"Lee, S.H., Kim, C.S.: Order learning using partially ordered data via chainization. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13673, pp. 196\u2013211. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19778-9_12"},{"key":"6_CR24","unstructured":"Lee, S.H., Shin, N.H., Kim, C.S.: Geometric order learning for rank estimation. In: Advances in Neural Information Processing Systems, vol.\u00a035, pp. 27\u201339 (2022)"},{"key":"6_CR25","unstructured":"Lee, S., Seo, S., Kim, J., Lee, Y., Hwang, S.: Few-shot fine-tuning is all you need for source-free domain adaptation (2023). arXiv preprint arXiv:2304.00792"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Li, S., et al.: Deep learning enables image-based tree counting, crown segmentation and height prediction at national scale. PNAS Nexus 2(4) (2023)","DOI":"10.1093\/pnasnexus\/pgad076"},{"key":"6_CR27","doi-asserted-by":"publisher","unstructured":"Li, S., et al.: Deep learning tree and forest biomass from sub-meter resolution images (2023). https:\/\/doi.org\/10.21203\/rs.3.rs-3335298","DOI":"10.21203\/rs.3.rs-3335298"},{"key":"6_CR28","doi-asserted-by":"publisher","first-page":"111953","DOI":"10.1016\/j.rse.2020.111953","volume":"247","author":"W Li","year":"2020","unstructured":"Li, W., Buitenwerf, R., Munk, M., B\u00f8cher, P.K., Svenning, J.C.: Deep-learning based high-resolution mapping shows woody vegetation densification in greater Maasai Mara ecosystem. Remote Sens. Environ. 247, 111953 (2020)","journal-title":"Remote Sens. Environ."},{"issue":"1","key":"6_CR29","doi-asserted-by":"publisher","first-page":"9952","DOI":"10.1038\/s41598-020-67024-3","volume":"10","author":"Y Li","year":"2020","unstructured":"Li, Y., Li, M., Li, C., Liu, Z.: Forest aboveground biomass estimation using Landsat 8 and sentinel-1a data with machine learning algorithms. Sci. Rep. 10(1), 9952 (2020)","journal-title":"Sci. Rep."},{"key":"6_CR30","unstructured":"Liang, J., Hu, D., Feng, J.: Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation. In: International Conference on Machine Learning (ICML), pp. 6028\u20136039 (2020)"},{"key":"6_CR31","unstructured":"Lim, K., Shin, N.H., Lee, Y.Y., Kim, C.S.: Order learning and its application to age estimation. In: International Conference on Learning Representations (2020)"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe. Sci. Adv. 9(37) (2023)","DOI":"10.1126\/sciadv.adh4097"},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Marsocci, V., Gonthier, N., Garioud, A., Scardapane, S., Mallet, C.: GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 2075\u20132085 (2023)","DOI":"10.1109\/CVPRW59228.2023.00201"},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Mathelin, A.D., Richard, G., Deheeger, F., Mougeot, M., Vayatis, N.: Adversarial weighting for domain adaptation in regression. In: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pp. 49\u201356. IEEE Computer Society (2021)","DOI":"10.1109\/ICTAI52525.2021.00015"},{"issue":"1","key":"6_CR35","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1038\/s41558-022-01544-w","volume":"13","author":"M Mugabowindekwe","year":"2023","unstructured":"Mugabowindekwe, M., et al.: Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda. Nat. Clim. Chang. 13(1), 91\u201397 (2023)","journal-title":"Nat. Clim. Chang."},{"key":"6_CR36","doi-asserted-by":"crossref","unstructured":"Nejjar, I., Wang, Q., Fink, O.: DARE-GRAM: unsupervised domain adaptation regression by aligning inversed gram matrices. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.01130"},{"key":"6_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/978-3-031-20044-1_27","volume-title":"Computer Vision \u2013 ECCV 2022","author":"KD Nguyen","year":"2022","unstructured":"Nguyen, K.D., Tran, Q.H., Nguyen, K., Hua, B.S., Nguyen, R.: Inductive and transductive few-shot video classification via appearance and temporal alignments. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13680, pp. 471\u2013487. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20044-1_27"},{"key":"6_CR38","unstructured":"Pardoe, D., Stone, P.: Boosting for regression transfer. In: International Conference on Machine Learning (2010)"},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Robinson, C., et al.: Large scale high-resolution land cover mapping with multi-resolution data. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12718\u201312727 (2019)","DOI":"10.1109\/CVPR.2019.01301"},{"key":"6_CR40","doi-asserted-by":"publisher","first-page":"112061","DOI":"10.1016\/j.rse.2020.112061","volume":"251","author":"JR Roussel","year":"2020","unstructured":"Roussel, J.R., et al.: lidR: an R package for analysis of airborne laser scanning (ALS) data. Remote Sens. Environ. 251, 112061 (2020)","journal-title":"Remote Sens. Environ."},{"key":"6_CR41","doi-asserted-by":"crossref","unstructured":"Shin, N.H., Lee, S.H., Kim, C.S.: Moving window regression: a novel approach to ordinal regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01820"},{"key":"6_CR42","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations (ICLR 2015), pp. 1\u201314 (2015)"},{"key":"6_CR43","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems (2017)"},{"key":"6_CR44","doi-asserted-by":"crossref","unstructured":"Sumbul, G., Demir, B.: Label noise robust image representation learning based on supervised variational autoencoders in remote sensing. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2023)","DOI":"10.1109\/IGARSS52108.2023.10282820"},{"key":"6_CR45","unstructured":"Teshima, T., Sato, I., Sugiyama, M.: Few-shot domain adaptation by causal mechanism transfer. In: Proceedings of the 37th International Conference on Machine Learning (2020)"},{"key":"6_CR46","doi-asserted-by":"crossref","unstructured":"Voulgaris, G., Philippides, A., Dolley, J., Reffin, J., Marshall, F., Quadrianto, N.: Seasonal domain shift in the global south: dataset and deep features analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 2116\u20132124 (2023)","DOI":"10.1109\/CVPRW59228.2023.00205"},{"key":"6_CR47","unstructured":"Wang, B., Mendez, J., Cai, M., Eaton, E.: Transfer learning via minimizing the performance gap between domains. In: Advances in Neural Information Processing Systems, vol.\u00a032. Curran Associates, Inc. (2019)"},{"key":"6_CR48","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.isprsjprs.2024.02.003","volume":"209","author":"S Wang","year":"2024","unstructured":"Wang, S., Han, W., Huang, X., Zhang, X., Wang, L., Li, J.: Trustworthy remote sensing interpretation: concepts, technologies, and applications. ISPRS J. Photogramm. Remote. Sens. 209, 150\u2013172 (2024)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"7","key":"6_CR49","doi-asserted-by":"publisher","first-page":"e1009180","DOI":"10.1371\/journal.pcbi.1009180","volume":"17","author":"BG Weinstein","year":"2021","unstructured":"Weinstein, B.G., et al.: A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the national ecological observation network. PLoS Comput. Biol. 17(7), e1009180 (2021)","journal-title":"PLoS Comput. Biol."},{"key":"6_CR50","unstructured":"Zhou, D., Bousquet, O., Lal, T., Weston, J., Sch\u00f6lkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems, vol.\u00a016. MIT Press (2003)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72980-5_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T09:15:59Z","timestamp":1730106959000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72980-5_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031729799","9783031729805"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72980-5_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"29 October 2024","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"}}]}}