{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T09:10:01Z","timestamp":1751015401735,"version":"3.41.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T00:00:00Z","timestamp":1746403200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T00:00:00Z","timestamp":1746403200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s00521-025-11267-6","type":"journal-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T12:32:58Z","timestamp":1746448378000},"page":"13927-13942","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Convolutional neural network models with low spatial variability hamper the transfer learning process"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9614-3053","authenticated-orcid":false,"given":"Alejandra","family":"Bravo-Diaz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebasti\u00e1n","family":"Moreno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier","family":"Lopatin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,5]]},"reference":[{"issue":"9","key":"11267_CR1","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.1111\/2041-210x.13650","volume":"12","author":"H Meyer","year":"2021","unstructured":"Meyer H, Pebesma E (2021) Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods Ecol Evol 12(9):1620\u20131633. https:\/\/doi.org\/10.1111\/2041-210x.13650","journal-title":"Methods Ecol Evol"},{"key":"11267_CR2","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1002\/rse2.109","volume":"5","author":"J Lopatin","year":"2019","unstructured":"Lopatin J, Dolos K, Kattenborn T, Fassnacht F (2019) How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing. Remote Sens Ecol Conserv 5:302\u2013317. https:\/\/doi.org\/10.1002\/rse2.109","journal-title":"Remote Sens Ecol Conserv"},{"issue":"2","key":"11267_CR3","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MGRS.2016.2548504","volume":"4","author":"D Tuia","year":"2016","unstructured":"Tuia D, Persello C, Bruzzone L (2016) Domain adaptation for the classification of remote sensing data: an overview of recent advances. IEEE Geosci Remote Sens Mag 4(2):41\u201357. https:\/\/doi.org\/10.1109\/MGRS.2016.2548504","journal-title":"IEEE Geosci Remote Sens Mag"},{"key":"11267_CR4","doi-asserted-by":"publisher","unstructured":"Niu R, Moreno S, Neville J (2015) Analyzing the transferability of collective inference models across networks. In: IEEE International conference on data mining workshop (ICDMW), pp 908\u2013916. https:\/\/doi.org\/10.1109\/ICDMW.2015.192","DOI":"10.1109\/ICDMW.2015.192"},{"key":"11267_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2024.102684","volume":"82","author":"A Bravo-Diaz","year":"2024","unstructured":"Bravo-Diaz A, Moreno S, Lopatin J (2024) Evaluating the ability of convolutional neural networks for transfer learning in Pinus radiata cover predictions. Eco Inform 82:102684. https:\/\/doi.org\/10.1016\/j.ecoinf.2024.102684","journal-title":"Eco Inform"},{"issue":"1","key":"11267_CR6","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2021","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2021) A comprehensive survey on transfer learning. Proc IEEE 109(1):43\u201376. https:\/\/doi.org\/10.1109\/JPROC.2020.3004555","journal-title":"Proc IEEE"},{"issue":"1","key":"11267_CR7","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):9. https:\/\/doi.org\/10.1186\/s40537-016-0043-6","journal-title":"J Big Data"},{"key":"11267_CR8","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22:1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11267_CR9","doi-asserted-by":"publisher","unstructured":"Ehrig C, Sonnleitner B, Neumann U, Cleophas C, Forestier G (2024) The impact of data set similarity and diversity on transfer learning success in time series forecasting. arXiv https:\/\/doi.org\/10.48550\/arXiv.2404.06198","DOI":"10.48550\/arXiv.2404.06198"},{"key":"11267_CR10","doi-asserted-by":"publisher","unstructured":"Klabunde M, Schumacher T, Strohmaier M, Lemmerich F (2024) Similarity of neural network models: a survey of functional and representational measures. arXiv https:\/\/doi.org\/10.48550\/arXiv.2305.06329","DOI":"10.48550\/arXiv.2305.06329"},{"key":"11267_CR11","doi-asserted-by":"publisher","unstructured":"Stolte M, Bommert A, Rahnenf\u00fchrer J (2023) A review and taxonomy of methods for quantifying dataset similarity. arXiv https:\/\/doi.org\/10.48550\/arXiv.2312.04078","DOI":"10.48550\/arXiv.2312.04078"},{"key":"11267_CR12","doi-asserted-by":"publisher","unstructured":"M\u00f6hring J, Kattenborn T, Mahecha M, Cheng Y, Beloiu\u00a0Schwenke M, Cloutier M, Denter M, Frey J, Gassilloud M, G\u00f6ritz A, Hempel J, Horion S, Jucker T, Junttila S, Khatri-Chhetri P, Korznikov K, Kruse S, Lalibert\u00e9 E, Maroschek M, Mosig C (2025) Global, multi-scale standing deadwood segmentation in centimeter-scale aerial images. TechRxiv https:\/\/doi.org\/10.36227\/techrxiv.174137781.13803217\/v1","DOI":"10.36227\/techrxiv.174137781.13803217\/v1"},{"key":"11267_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/rs11111358","author":"X Qin","year":"2019","unstructured":"Qin X, Yang J, Li P, Sun W, Liu W (2019) A novel relational-based transductive transfer learning method for polsar images via time-series clustering. Remote Sens. https:\/\/doi.org\/10.3390\/rs11111358","journal-title":"Remote Sens"},{"key":"11267_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/rs14081809","author":"Y Luo","year":"2022","unstructured":"Luo Y, Zhang Z, Zhang L, Han J, Cao J, Zhang J (2022) Developing high-resolution crop maps for major crops in the European union based on Transductive transfer learning and limited ground data. Remote Sens. https:\/\/doi.org\/10.3390\/rs14081809","journal-title":"Remote Sens"},{"key":"11267_CR15","doi-asserted-by":"publisher","first-page":"4591","DOI":"10.1109\/JSTARS.2023.3263149","volume":"16","author":"B Wang","year":"2023","unstructured":"Wang B, Wang Z, Sun X, He Q, Wang H, Fu K (2023) Tdnet: a novel Transductive learning framework with conditional metric embedding for few-shot remote sensing image scene classification. IEEE J Sel Top Appl Earth Obs Remote Sens 16:4591\u20134606. https:\/\/doi.org\/10.1109\/JSTARS.2023.3263149","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"11267_CR16","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","volume":"173","author":"T Kattenborn","year":"2021","unstructured":"Kattenborn T, Leitloff J, Schiefer F, Hinz S (2021) Review on convolutional neural networks (CNN) in vegetation remote sensing. ISPRS J Photogramm Remote Sens 173:24\u201349. https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.12.010","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"11267_CR17","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1080\/20964471.2019.1657720","volume":"3","author":"YZ Jia Song","year":"2019","unstructured":"Jia Song YZ, Gao Shaohua, Ma C (2019) A survey of remote sensing image classification based on CNNs. Big Earth Data 3:232\u2013254. https:\/\/doi.org\/10.1080\/20964471.2019.1657720","journal-title":"Big Earth Data"},{"key":"11267_CR18","doi-asserted-by":"publisher","first-page":"7166","DOI":"10.3390\/s24227166","volume":"24","author":"B Liu","year":"2024","unstructured":"Liu B, Mo P, Wang S, Cui Y, Wu Z (2024) A refined and efficient CNN algorithm for remote sensing object detection. Sensors 24:7166. https:\/\/doi.org\/10.3390\/s24227166","journal-title":"Sensors"},{"key":"11267_CR19","unstructured":"Chollet F(2018) Deep learning with python. Manning 0ublications Co, Shelter Island"},{"key":"11267_CR20","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35:1299\u20131312. https:\/\/doi.org\/10.1109\/TMI.2016.2535302","journal-title":"IEEE Trans Med Imaging"},{"key":"11267_CR21","doi-asserted-by":"publisher","first-page":"5617","DOI":"10.1007\/s00500-022-07145-1","volume":"26","author":"S Thirumaladevi","year":"2022","unstructured":"Thirumaladevi S, Swamy KV, Sailaja M (2022) Improved transfer learning of CNN through fine-tuning and classifier ensemble for scene classification. Soft Comput 26:5617\u20135636. https:\/\/doi.org\/10.1007\/s00500-022-07145-1","journal-title":"Soft Comput"},{"key":"11267_CR22","doi-asserted-by":"publisher","first-page":"475","DOI":"10.5194\/isprs-archives-XLII-2-W13-475-2019","volume":"42","author":"S Natesan","year":"2019","unstructured":"Natesan S, Armenakis C, Vepakomma U (2019) ResNet based tree species classification using uav images. Int Arch Photogramm Remote Sens Spat Inform Sci 42:475\u2013481. https:\/\/doi.org\/10.5194\/isprs-archives-XLII-2-W13-475-2019","journal-title":"Int Arch Photogramm Remote Sens Spat Inform Sci"},{"key":"11267_CR23","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1038\/s41598-020-79653-9","volume":"11","author":"M Onishi","year":"2021","unstructured":"Onishi M, Ise T (2021) Explainable identification and mapping of trees using UAV RGB image and deep learning. Sci Rep 11:903. https:\/\/doi.org\/10.1038\/s41598-020-79653-9","journal-title":"Sci Rep"},{"key":"11267_CR24","doi-asserted-by":"publisher","first-page":"643","DOI":"10.3390\/rs11060643","volume":"11","author":"A Safonova","year":"2019","unstructured":"Safonova A, Tabik S, Alcaraz-Segura D, Rubtsov A, Maglinets Y, Herrera F (2019) Detection of fir trees (abies sibirica) damaged by the bark beetle in unmanned aerial vehicle images with deep learning. Remote Sens 11:643. https:\/\/doi.org\/10.3390\/rs11060643","journal-title":"Remote Sens"},{"key":"11267_CR25","doi-asserted-by":"publisher","first-page":"86","DOI":"10.3390\/rs12010086","volume":"12","author":"R Lima","year":"2019","unstructured":"Lima R, Marfurt K (2019) Convolutional neural network for remote-sensing scene classification: transfer learning analysis. Remote Sens 12:86. https:\/\/doi.org\/10.3390\/rs12010086","journal-title":"Remote Sens"},{"key":"11267_CR26","doi-asserted-by":"publisher","first-page":"0188714","DOI":"10.1371\/journal.pone.0188714","volume":"12","author":"S Baena","year":"2017","unstructured":"Baena S, Moat J, Whaley O, Boyd D (2017) Identifying species from the air: UAVs and the very high resolution challenge for plant conservation. PLoS ONE 12:0188714. https:\/\/doi.org\/10.1371\/journal.pone.0188714","journal-title":"PLoS ONE"},{"key":"11267_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophoto.2022.100018","volume":"5","author":"T Kattenborn","year":"2022","unstructured":"Kattenborn T, Schiefer F, Frey J, Feilhauer H, Mahecha MD, Dormann CF (2022) Spatially autocorrelated training and validation samples inflate performance assessment of convolutional neural networks. ISPRS Open J Photogramm Remote Sens 5:100018. https:\/\/doi.org\/10.1016\/j.ophoto.2022.100018","journal-title":"ISPRS Open J Photogramm Remote Sens"},{"key":"11267_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2023.103569","volume":"125","author":"A Safonova","year":"2023","unstructured":"Safonova A, Ghazaryan G, Stiller S, Main-Knorn M, Nendel C, Ryo M (2023) Ten deep learning techniques to address small data problems with remote sensing. Int J Appl Earth Obs Geoinf 125:103569. https:\/\/doi.org\/10.1016\/j.jag.2023.103569","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"11267_CR29","doi-asserted-by":"publisher","first-page":"14219","DOI":"10.7717\/peerj.14219","volume":"10","author":"NC Galuszynski","year":"2022","unstructured":"Galuszynski NC, Duker R, Potts AJ, Kattenborn T (2022) Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery. PeerJ 10:14219. https:\/\/doi.org\/10.7717\/peerj.14219","journal-title":"PeerJ"},{"issue":"4","key":"11267_CR30","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1139\/juvs-2020-0014","volume":"8","author":"S Natesan","year":"2020","unstructured":"Natesan S, Armenakis C, Vepakomma U (2020) Individual tree species identification using dense convolutional network Densenet on multitemporal RGB images from UAV. J Unmanned Veh Syst 8(4):310\u2013333. https:\/\/doi.org\/10.1139\/juvs-2020-0014","journal-title":"J Unmanned Veh Syst"},{"issue":"1","key":"11267_CR31","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):9. https:\/\/doi.org\/10.1186\/s40537-016-0043-6","journal-title":"J Big Data"},{"key":"11267_CR32","doi-asserted-by":"publisher","unstructured":"Iddianozie C, McArdle G (2019) A transfer learning paradigm for spatial networks. In: Proceedings of the 34th ACM\/SIGAPP symposium on applied computing. https:\/\/doi.org\/10.1145\/3297280.3297342","DOI":"10.1145\/3297280.3297342"},{"key":"11267_CR33","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1017\/S0030605322000102","volume":"57","author":"M Gonzalez","year":"2022","unstructured":"Gonzalez M, Galleguillos M, Lopatin J, Leal C, Becerra-Rodas C, Lara AJM (2022) Surviving in a hostile landscape: Nothofagus alessandrii remnant forests threatened by mega-fires and exotic pine invasion in the coastal range of central chile. Oryx 57:228\u2013238. https:\/\/doi.org\/10.1017\/S0030605322000102","journal-title":"Oryx"},{"key":"11267_CR34","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.rse.2019.03.025","volume":"227","author":"T Kattenborn","year":"2019","unstructured":"Kattenborn T, Lopatin J, Forster M, Braun A, Fassnacht F (2019) UAV data as alternative to field sampling to map woody invasive species based on combined sentinel-1 and sentinel-2 data. Remote Sens Environ 227:61\u201373","journal-title":"Remote Sens Environ"},{"key":"11267_CR35","unstructured":"Warmerdam F, Rouault E (1998) GDAL Documentation"},{"key":"11267_CR36","unstructured":"Gillies S (2018) Rasterio Documentation"},{"key":"11267_CR37","unstructured":"Chollet F, et al (2015) Keras. https:\/\/keras.io"},{"key":"11267_CR38","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.3390\/rs10071119","volume":"10","author":"M Mahdianpari","year":"2018","unstructured":"Mahdianpari M, Salehi B, Rezaee M, Mohammadimanesh F, Zhang Y (2018) Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sens 10:1119. https:\/\/doi.org\/10.3390\/rs10071119","journal-title":"Remote Sens"},{"key":"11267_CR39","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1002\/rse2.146","volume":"6","author":"T Kattenborn","year":"2020","unstructured":"Kattenborn T, Eichel J, Wiser S, Burrows L, Fassnacht F, Schmidtlein S (2020) Convolutional neural networks accurately predict cover fractions of plant species and communities in unmanned aerial vehicle imagery. Remote Sens Ecol Conserv 6:472\u2013486. https:\/\/doi.org\/10.1002\/rse2.146","journal-title":"Remote Sens Ecol Conserv"},{"key":"11267_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2020.101061","volume":"56","author":"B Weinstein","year":"2020","unstructured":"Weinstein B, Marconi S, Bohlman S, Zare A, White EP (2020) Cross-site learning in deep learning RGB tree crown detection. Eco Inform 56:101061. https:\/\/doi.org\/10.1016\/j.ecoinf.2020.101061","journal-title":"Eco Inform"},{"issue":"2","key":"11267_CR41","doi-asserted-by":"publisher","first-page":"565","DOI":"10.5194\/soil-6-565-2020","volume":"6","author":"W Ng","year":"2020","unstructured":"Ng W, Minasny B, Mendes WDS, Dematt\u00e9 JAM (2020) The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data. SOIL 6(2):565. https:\/\/doi.org\/10.5194\/soil-6-565-2020","journal-title":"SOIL"},{"key":"11267_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/igarss46834.2022.9884889","volume":"61","author":"Q Xu","year":"2023","unstructured":"Xu Q, Shi Y, Yuan X, Zhu XX (2023) Universal domain adaptation for remote sensing image scene classification. IEEE Trans Geosci Remote Sens 61:1\u201315. https:\/\/doi.org\/10.1109\/igarss46834.2022.9884889","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11267_CR43","doi-asserted-by":"publisher","first-page":"4942","DOI":"10.3390\/rs14194942","volume":"14","author":"W Li","year":"2022","unstructured":"Li W, Gao H, Su Y, Momanyi BM (2022) Unsupervised domain adaptation for remote sensing semantic segmentation with transformer. Remote Sens 14:4942. https:\/\/doi.org\/10.3390\/rs14194942","journal-title":"Remote Sens"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11267-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11267-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11267-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T08:28:49Z","timestamp":1751012929000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11267-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,5]]},"references-count":43,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["11267"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11267-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2025,5,5]]},"assertion":[{"value":"19 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}