{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:03:58Z","timestamp":1743134638389,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030687793"},{"type":"electronic","value":"9783030687809"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68780-9_15","type":"book-chapter","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T17:04:13Z","timestamp":1614186253000},"page":"148-158","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["How Do Deep Convolutional SDM Trained on Satellite Images Unravel Vegetation Ecology?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0640-5706","authenticated-orcid":false,"given":"Benjamin","family":"Deneu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2161-9940","authenticated-orcid":false,"given":"Alexis","family":"Joly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2828-4389","authenticated-orcid":false,"given":"Pierre","family":"Bonnet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9426-2583","authenticated-orcid":false,"given":"Maximilien","family":"Servajean","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8776-4705","authenticated-orcid":false,"given":"Fran\u00e7ois","family":"Munoz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"issue":"9","key":"15_CR1","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1111\/j.1461-0248.2005.00792.x","volume":"8","author":"G Antoine","year":"2005","unstructured":"Antoine, G., Wilfried, T.: Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8(9), 993\u20131009 (2005). https:\/\/doi.org\/10.1111\/j.1461-0248.2005.00792.x","journal-title":"Ecol. Lett."},{"issue":"1","key":"15_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BF00028502","volume":"337","author":"P Baran","year":"1996","unstructured":"Baran, P., Lek, S., Delacoste, M., Belaud, A.: Stochastic models that predict trout population density or biomass on a mesohabitat scale. Hydrobiologia 337(1), 1\u20139 (1996). https:\/\/doi.org\/10.1007\/BF00028502","journal-title":"Hydrobiologia"},{"issue":"4","key":"15_CR3","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1111\/1365-2435.12531","volume":"30","author":"M Bartelheimer","year":"2016","unstructured":"Bartelheimer, M., Poschlod, P.: Functional characterizations of Ellenberg indicator values-a review on ecophysiological determinants. Funct. Ecol. 30(4), 506\u2013516 (2016)","journal-title":"Funct. Ecol."},{"key":"15_CR4","doi-asserted-by":"publisher","first-page":"101137","DOI":"10.1016\/j.ecoinf.2020.101137","volume":"60","author":"DJ Benkendorf","year":"2020","unstructured":"Benkendorf, D.J., Hawkins, C.P.: Effects of sample size and network depth on a deep learning approach to species distribution modeling. Ecol. Inform. 60, 101137 (2020)","journal-title":"Ecol. Inform."},{"key":"15_CR5","series-title":"Multimedia Systems and Applications","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-319-76445-0_10","volume-title":"Multimedia Tools and Applications for Environmental & Biodiversity Informatics","author":"C Botella","year":"2018","unstructured":"Botella, C., Joly, A., Bonnet, P., Monestiez, P., Munoz, F.: A deep learning approach to species distribution modelling. In: Joly, A., Vrochidis, S., Karatzas, K., Karppinen, A., Bonnet, P. (eds.) Multimedia Tools and Applications for Environmental & Biodiversity Informatics. MSA, pp. 169\u2013199. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-76445-0_10"},{"key":"15_CR6","unstructured":"Chen, D., Xue, Y., Chen, S., Fink, D., Gomes, C.P.: Deep multi-species embedding. CoRR abs\/1609.09353 (2016). http:\/\/arxiv.org\/abs\/1609.09353"},{"issue":"10","key":"15_CR7","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.1111\/2041-210X.13256","volume":"10","author":"S Christin","year":"2019","unstructured":"Christin, S., Hervet, \u00c9., Lecomte, N.: Applications for deep learning in ecology. Methods Ecol. Evol. 10(10), 1632\u20131644 (2019)","journal-title":"Methods Ecol. Evol."},{"key":"15_CR8","unstructured":"Cole, E., et al.: The GeoLifeCLEF 2020 dataset. arXiv preprint arXiv:2004.04192 (2020)"},{"issue":"11","key":"15_CR9","doi-asserted-by":"publisher","first-page":"2783","DOI":"10.1890\/07-0539.1","volume":"88","author":"DR Cutler","year":"2007","unstructured":"Cutler, D.R., et al.: Random forests for classification in ecology. Ecology 88(11), 2783\u20132792 (2007). https:\/\/doi.org\/10.1890\/07-0539.1","journal-title":"Ecology"},{"issue":"1","key":"15_CR10","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1890\/0012-9658(2007)88[243:BTFEMA]2.0.CO;2","volume":"88","author":"G De\u2019ath","year":"2007","unstructured":"De\u2019ath, G.: Boosted trees for ecological modeling and prediction. Ecology 88(1), 243\u2013251 (2007). https:\/\/doi.org\/10.1890\/0012-9658(2007)88[243:BTFEMA]2.0.CO;2","journal-title":"Ecology"},{"key":"15_CR11","unstructured":"Deneu, B., Servajean, M., Joly, A.: Participation of LIRMM\/Inria to the geo-lifeclef 2020 challenge. CLEF working notes (2020)"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Deneu, B., Joly, A., Bonnet, P., Servajean, M., Munoz, F.: Supplementary materials: How do deep convolutional SDM trained on satellite images unravel vegetation ecology? https:\/\/gitlab.inria.fr\/bdeneu\/supplementary-materials-maes2020-paper-19","DOI":"10.1007\/978-3-030-68780-9_15"},{"key":"15_CR13","unstructured":"Deneu, B., Servajean, M., Botella, C., Joly, A.: Location-based species recommendation using co-occurrences and environment- GeoLifeCLEF 2018 challenge. In: CLEF Working Notes 2018 (2018)"},{"key":"15_CR14","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1146\/annurev.ecolsys.110308.120159","volume":"40","author":"J Elith","year":"2009","unstructured":"Elith, J., Leathwick, J.R.: Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677\u2013697 (2009)","journal-title":"Annu. Rev. Ecol. Evol. Syst."},{"issue":"4","key":"15_CR15","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","volume":"77","author":"J Elith","year":"2008","unstructured":"Elith, J., Leathwick, J.R., Hastie, T.: A working guide to boosted regression trees. J. Anim. Ecol. 77(4), 802\u2013813 (2008). https:\/\/doi.org\/10.1111\/j.1365-2656.2008.01390.x","journal-title":"J. Anim. Ecol."},{"key":"15_CR16","volume-title":"Vegetation Ecology of Central Europe","author":"HH Ellenberg","year":"1988","unstructured":"Ellenberg, H.H.: Vegetation Ecology of Central Europe. Cambridge University Press, Cambridge (1988)"},{"issue":"2","key":"15_CR17","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/S0304-3800(00)00354-9","volume":"135","author":"A Guisan","year":"2000","unstructured":"Guisan, A., Zimmermann, N.E.: Predictive habitat distribution models in ecology. Ecol. Model. 135(2), 147\u2013186 (2000). https:\/\/doi.org\/10.1016\/S0304-3800(00)00354-9","journal-title":"Ecol. Model."},{"issue":"2","key":"15_CR18","doi-asserted-by":"publisher","first-page":"e0169748","DOI":"10.1371\/journal.pone.0169748","volume":"12","author":"T Hengl","year":"2017","unstructured":"Hengl, T., et al.: SoilGrids250m: global gridded soil information based on machine learning. PLoS One 12(2), e0169748 (2017)","journal-title":"PLoS One"},{"issue":"15","key":"15_CR19","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.1002\/joc.1276","volume":"25","author":"RJ Hijmans","year":"2005","unstructured":"Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A.: Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. J. R. Meteorol. Soc. 25(15), 1965\u20131978 (2005)","journal-title":"Int. J. Climatol. J. R. Meteorol. Soc."},{"issue":"5","key":"15_CR20","first-page":"345","volume":"81","author":"C Homer","year":"2015","unstructured":"Homer, C., et al.: Completion of the 2011 national land cover database for the conterminous united states-representing a decade of land cover change information. Photogram. Eng. Remote Sens. 81(5), 345\u2013354 (2015)","journal-title":"Photogram. Eng. Remote Sens."},{"issue":"4","key":"15_CR21","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989)","journal-title":"Neural Comput."},{"issue":"1","key":"15_CR22","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/0304-3800(95)00142-5","volume":"90","author":"S Lek","year":"1996","unstructured":"Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., Aulagnier, S.: Application of neural networks to modelling nonlinear relationships in ecology. Ecol. Model. 90(1), 39\u201352 (1996). https:\/\/doi.org\/10.1016\/0304-3800(95)00142-5","journal-title":"Ecol. Model."},{"key":"15_CR23","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"15_CR24","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1111\/2041-210X.13119","volume":"10","author":"JE Miller","year":"2019","unstructured":"Miller, J.E., Damschen, E.I., Ives, A.R.: Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models. Methods Ecol. Evol. 10(3), 415\u2013425 (2019)","journal-title":"Methods Ecol. Evol."},{"key":"15_CR25","doi-asserted-by":"publisher","DOI":"10.23943\/princeton\/9780691136868.001.0001","volume-title":"Ecological Niches and Geographic Distributions","author":"AT Peterson","year":"2011","unstructured":"Peterson, A.T.: Ecological Niches and Geographic Distributions. Princeton University Press, Princeton (2011)"},{"issue":"3\u20134","key":"15_CR26","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.ecolmodel.2005.03.026","volume":"190","author":"SJ Phillips","year":"2006","unstructured":"Phillips, S.J., Anderson, R.P., Schapire, R.E.: Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3\u20134), 231\u2013259 (2006)","journal-title":"Ecol. Model."},{"issue":"2","key":"15_CR27","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1111\/j.0906-7590.2008.5203.x","volume":"31","author":"SJ Phillips","year":"2008","unstructured":"Phillips, S.J., Dud\u00edk, M.: Modeling of species distributions with maxent: new extensions and a comprehensive evaluation. Ecography 31(2), 161\u2013175 (2008). https:\/\/doi.org\/10.1111\/j.0906-7590.2008.5203.x","journal-title":"Ecography"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"issue":"38","key":"15_CR29","doi-asserted-by":"publisher","first-page":"13690","DOI":"10.1073\/pnas.1415442111","volume":"111","author":"C Violle","year":"2014","unstructured":"Violle, C., Reich, P.B., Pacala, S.W., Enquist, B.J., Kattge, J.: The emergence and promise of functional biogeography. Proc. Natl. Acad. Sci. 111(38), 13690\u201313696 (2014)","journal-title":"Proc. Natl. Acad. Sci."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68780-9_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,18]],"date-time":"2022-12-18T21:44:21Z","timestamp":1671399861000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68780-9_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030687793","9783030687809"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68780-9_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ICPR2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icpr2020.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}