{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T21:09:11Z","timestamp":1771016951246,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005825","name":"USDA NIFA Awards","doi-asserted-by":"publisher","award":["2020-67030-30714"],"award-info":[{"award-number":["2020-67030-30714"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"USDA NIFA Awards","doi-asserted-by":"publisher","award":["2023-68016-39039"],"award-info":[{"award-number":["2023-68016-39039"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Natural disturbances like hurricanes can cause extensive disorder in forest structure, composition, and succession. Consequently, ecological, social, and economic alterations may occur. Terrestrial laser scanning (TLS) and deep learning have been used for estimating forest attributes with high accuracy, but to date, no study has combined both TLS and deep learning for assessing the impact of hurricane disturbance at the individual tree level. Here, we aim to assess the capability of TLS and convolutional neural networks (CNNs) combined for classifying post-Hurricane Michael damage severity at the individual tree level in a pine-dominated forest ecosystem in the Florida Panhandle, Southern U.S. We assessed the combined impact of using either binary-color or multicolored-by-height TLS-derived 2D images along with six CNN architectures (Densenet201, EfficientNet_b7, Inception_v3, Res-net152v2, VGG16, and a simple CNN). The confusion matrices used for assessing the overall accuracy were symmetric in all six CNNs and 2D image variants tested with overall accuracy ranging from 73% to 92%. We found higher F-1 scores when classifying trees with damage severity varying from extremely leaning, trunk snapped, stem breakage, and uprooted compared to trees that were undamaged or slightly leaning (&lt;45\u00b0). Moreover, we found higher accuracies when using VGG16 combined with multicolored-by-height TLS-derived 2D images compared with other methods. Our findings demonstrate the high capability of combining TLS with CNNs for classifying post-hurricane damage severity at the individual tree level in pine forest ecosystems. As part of this work, we developed a new open-source R package (rTLsDeep) and implemented all methods tested herein. We hope that the promising results and the rTLsDeep R package developed in this study for classifying post-hurricane damage severity at the individual tree level will stimulate further research and applications not just in pine forests but in other forest types in hurricane-prone regions.<\/jats:p>","DOI":"10.3390\/rs15041165","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T01:39:28Z","timestamp":1676943568000},"page":"1165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Post-Hurricane Damage Severity Classification at the Individual Tree Level Using Terrestrial Laser Scanning and Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6898-5593","authenticated-orcid":false,"given":"Carine","family":"Klauberg","sequence":"first","affiliation":[{"name":"School of Forest, Fisheries, and Geomatics Sciences, University of Florida Gainesville, FL 32611, USA"}]},{"given":"Jason","family":"Vogel","sequence":"additional","affiliation":[{"name":"School of Forest, Fisheries, and Geomatics Sciences, University of Florida Gainesville, FL 32611, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7151-8697","authenticated-orcid":false,"given":"Ricardo","family":"Dalagnol","sequence":"additional","affiliation":[{"name":"Center for Tropical Research, Institute of the Environment and Sustainability, University of California Los Angeles (UCLA), Los Angeles, CA 90095, USA"},{"name":"NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0687-4422","authenticated-orcid":false,"given":"Matheus Pinheiro","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Cartographic Engineering Department, Military Institute of Engineering (IME), Pra\u00e7a Gen, Tib\u00farcio 80, Rio de Janeiro 22290-270, RJ, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6149-5885","authenticated-orcid":false,"given":"Caio","family":"Hamamura","sequence":"additional","affiliation":[{"name":"Federal Institute of Education, Science and Technology of S\u00e3o Paulo, Avenida Doutor \u00canio Pires de Camargo, Capivari 13365-010, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4488-4237","authenticated-orcid":false,"given":"Eben","family":"Broadbent","sequence":"additional","affiliation":[{"name":"School of Forest, Fisheries, and Geomatics Sciences, University of Florida Gainesville, FL 32611, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7844-3560","authenticated-orcid":false,"given":"Carlos Alberto","family":"Silva","sequence":"additional","affiliation":[{"name":"School of Forest, Fisheries, and Geomatics Sciences, University of Florida Gainesville, FL 32611, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1139\/x83-029","article-title":"The impact of Hurricane David on forests of Dominica","volume":"13","author":"Lugo","year":"1983","journal-title":"Can. J. For. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"420","DOI":"10.2307\/2388261","article-title":"Hurricane Hugo wind damage to southeastern U.S. coastal forest tree species","volume":"23","author":"Gresham","year":"1991","journal-title":"Biotropica"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"512","DOI":"10.2307\/2388274","article-title":"Hurricane effects on forest ecosystems in the Caribbean","volume":"23","author":"Tanner","year":"1991","journal-title":"Biotropica"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s10310-002-0019-6","article-title":"Influence of typhoon disturbances on the understory light regime and stand dynamics of a subtropical rain forest in northeastern Taiwan","volume":"8","author":"Lin","year":"2003","journal-title":"J. For. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1093\/forestry\/cps058","article-title":"Wind as a natural disturbance agent in forests: A synthesis","volume":"86","author":"Mitchell","year":"2013","journal-title":"Forestry"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"434","DOI":"10.2307\/2388263","article-title":"The impact of Hurricane Gilbert on trees, litterfall, and woody debris in a dry tropical forest in the northeastern Yucatan Peninsula","volume":"32","author":"Whigham","year":"1991","journal-title":"Biotropica"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.yqres.2007.10.011","article-title":"A 1200-year proxy record of hurricanes and fires from the Gulf of Mexico coast: Testing the hypothesis of hurricane-fire interactions","volume":"69","author":"Liu","year":"2008","journal-title":"Quat. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.foreco.2007.03.024","article-title":"Biotic and abiotic influences on wind disturbance in forests of NW Pennsylvania, USA","volume":"245","author":"Evans","year":"2007","journal-title":"For. Ecol. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.foreco.2004.07.072","article-title":"Modelling the vulnerability of balsam fir forests to wind damage","volume":"204","author":"Achim","year":"2005","journal-title":"For. Ecol. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1080\/02827589309382775","article-title":"Attacks of bark and wood boring Coleoptera on snow broken conifers over a two-year period","volume":"8","author":"Schroeder","year":"1993","journal-title":"Scand. J. For. Res."},{"key":"ref_11","unstructured":"Oliver, C.D., and Larson, B.C. (1996). Forest Stand Dynamics, Wiley."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1038\/nclimate3303","article-title":"Forest disturbances under climate change","volume":"7","author":"Seidl","year":"2017","journal-title":"Nat. Clim. Chang."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1371\/journal.pone.0243362","article-title":"Long-term effects of catastrophic wind on southern US coastal forests: Lessons from a major hurricane","volume":"16","author":"Sharma","year":"2021","journal-title":"PLoS ONE"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1093\/jpe\/rtm003","article-title":"Changes in forest structure, species diversity and spatial pattern following hurricane disturbance in a Piedmont North Carolina forest, USA","volume":"1","author":"Xi","year":"2008","journal-title":"J. Plant Ecol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8483","DOI":"10.1038\/s41598-020-65436-9","article-title":"The impact of Hurricane Michael on longleaf pine habitats in Florida","volume":"10","author":"Zampieri","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"118724","DOI":"10.1016\/j.foreco.2020.118724","article-title":"Tree, Stand, and Landscape Factors Contributing to Hurricane Damage in a Coastal Plain Forest: Post-Hurricane Assessment in a Longleaf Pine Landscape","volume":"481","author":"Rutledge","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_17","first-page":"395","article-title":"Hurricane effects on climate-adaptive silviculture treatments to longleaf pine woodland in southwestern Georgia, USA","volume":"94","author":"Bigelow","year":"2020","journal-title":"For. Int. J. For. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s11676-019-00907-y","article-title":"Three-dimensional light structure of an upland Quercus stand post-tornado disturbance","volume":"31","author":"Willson","year":"2020","journal-title":"J. For. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1126\/science.1180568","article-title":"Modeled impact of anthropogenic warming on the frequency of intense Atlantic hurricanes","volume":"327","author":"Bender","year":"2010","journal-title":"Science"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/02796015.1991.12085530","article-title":"Conceptualizing behavior disorders in terms of resistance to intervention","volume":"20","author":"Gresham","year":"1991","journal-title":"Sch. Psychol. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"92","DOI":"10.3368\/er.19.2.92","article-title":"Restoration fire and hurricanes in longleaf pine sandhills","volume":"19","author":"Provencher","year":"2001","journal-title":"Ecol. Restor."},{"key":"ref_22","unstructured":"Joseph, W. (2008). Multiple Value Management: The Stoddard-Neel Approach to Ecological Forestry in Longleaf Pine Grasslands, Jones Ecological Research Center at Ichauway."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Blackman, R., and Yuan, F. (2020). Detecting Long-Term Urban Forest Cover Change and Impacts of Natural Disasters Using High-Resolution Aerial Images and LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12111820"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3512","DOI":"10.1109\/JSTARS.2018.2816962","article-title":"Comparison of Small- and Large-Footprint Lidar Characterization of Tropical Forest Aboveground Structure and Biomass: A Case Study From Central Gabon","volume":"11","author":"Silva","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1126\/science.1148913","article-title":"Hurricane Katrina\u2019s carbon footprint on US Gulf Coast forests","volume":"318","author":"Chambers","year":"2007","journal-title":"Science"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Russell, M., Eitel, J.U.H., Link, T.E., and Silva, C.A. (2021). Important Airborne Lidar Metrics of Canopy Structure for Estimating Snow Interception. Remote Sens., 1.","DOI":"10.3390\/rs13204188"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108820","DOI":"10.1016\/j.ecolmodel.2019.108820","article-title":"Characterizing fire effects on conifers at tree level from airborne laser scanning and high-resolution, multispectral satellite data","volume":"412","author":"Klauberg","year":"2019","journal-title":"Ecol. Model."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Silva, V.S., Silva, C.A., Mohan, M., Cardil, A., Rex, F.E., Loureiro, G.H., Almeida, D.R.A., Broadbent, E.N., Gorgens, E.B., and Dalla Corte, A.P. (2020). Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12091438"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cunha Neto, E.M., Veras, H.F.P., Moraes, A., Klauberg, C., Mohan, M., Cardil, A., and Broadbent, E.N. (2020). Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System. Remote Sens., 12.","DOI":"10.3390\/rs12050863"},{"key":"ref_30","unstructured":"Silva, C.A., Duncansona, L., Hancockb, S., Neuenshwanderc, A., Thomasd, N., Hofton, M., Simardd, M., Armston, J., Feng, T., and Montesano, P. (2022). Mapping Tropical Forest Aboveground Biomass Density from Synergism of GEDI, ICESat-2, and NISAR data. Remote Sens. Environ., in review."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1590\/0001-3765201820160071","article-title":"Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Imputation","volume":"90","author":"Silva","year":"2018","journal-title":"An. Acad. Bras. Ci\u00eancias"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jaafar, W.S.W.M., Woodhouse, I.H., Silva, C.A., Omar, H., Maulud, K.N.A., Hudak, A.T., Klauberg, C., Cardil, A., and Mohan, M. (2018). Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data. Forests, 9.","DOI":"10.3390\/f9120759"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1016\/j.agrformet.2011.05.004","article-title":"Estimating leaf area distribution in savanna trees from terrestrial LiDAR measurements","volume":"151","author":"Widlowski","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1111\/2041-210X.12301","article-title":"Nondestructive estimates of above-ground biomass using terrestrial laser scanning","volume":"6","author":"Calders","year":"2014","journal-title":"Methods Ecol. Evol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Dobre, A.C., Pascu, I.-S., Leca, \u0218., Garcia-Duro, J., Dobrota, C.-E., Tudoran, G.M., and Badea, O. (2021). Applications of TLS and ALS in Evaluating Forest Ecosystem Services: A Southern Carpathians Case Study. Forests, 12.","DOI":"10.3390\/f12091269"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Leite, R.V., Silva, C.A., Mohan, M., Cardil, A., Almeida, D.R.A., Carvalho, S.P.C., Jaafar, W.S.W.M., Guerra-Hern\u00e1ndez, J., Weiskittel, A., and Hudak, A.T. (2020). Individual Tree Attribute Estimation and Uniformity Assessment in Fast-Growing Eucalyptus spp. Forest Plantations Using Lidar and Linear Mixed-Effects Models. Remote Sens., 12.","DOI":"10.3390\/rs12213599"},{"key":"ref_37","first-page":"166","article-title":"Deep learning in remote sensing applications: A meta-analysis and review. ISPRS J. Photogramm","volume":"152","author":"Ma","year":"2019","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1109\/JSTARS.2020.3046053","article-title":"Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Abdi, O., Uusitalo, J., and Kivinen, V.-P. (2022). Logging Trail Segmentation via a Novel U-Net Convolutional Neural Network and High-Density Laser Scanning Data. Remote Sens., 14.","DOI":"10.3390\/rs14020349"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1002\/rse2.264","article-title":"Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning","volume":"8","author":"Dalagnol","year":"2022","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"118397","DOI":"10.1016\/j.foreco.2020.118397","article-title":"Individual tree detection and species classification of Amazonian palms using UAV images and deep learning","volume":"475","author":"Ferreira","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Nezami, N.S., Khoramshahi, E., Nevalainen, O., P\u00f6l\u00f6nen, I., and Honkavaara, E. (2020). Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks. Remote Sens., 12.","DOI":"10.20944\/preprints202002.0334.v1"},{"key":"ref_43","first-page":"3809","article-title":"Revisiting point cloud shape classification with a simple and effective baseline","volume":"139","author":"Goyal","year":"2021","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2020.08.001","article-title":"See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning","volume":"168","author":"Xi","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Allen, M.J., Grieve, S.W.D., Owen, H.J.F., and Lines, E.R. (2022). Tree species classification from complex laser scanning data in Mediterranean forests using deep learning. Methods Ecol. Evol., 1\u201311.","DOI":"10.1111\/2041-210X.13981"},{"key":"ref_46","unstructured":"Klauberg, C., Vogel, J., Dalagnol, R., Ferreira, M., Broadbent, E.N., Hamamura, C., Souza, D.R.F., Nogueira, L.G.A., and Silva, C.A. (2022, December 30). rTLsDeep: An R Package for Post-Hurricane Damage Severity Classification at the Individual Tree Level Using Terrestrial Laser Scanning and Deep Learning. Version 0.0.1. Available online: https:\/\/github.com\/carlos-alberto-silva\/rTLsDeep."},{"key":"ref_47","unstructured":"Georgia Forestry Commission (2022, October 03). TIMBER IMPACT ASSESSMENT Hurricane Michael. 10-11 October 2018. Available online: https:\/\/gatrees.org\/wp-content\/uploads\/2020\/01\/Hurricane-MichaelTimber-Impact-Assessment-Georgia-October-10-11-2018-2.pdf."},{"key":"ref_48","unstructured":"(2022, November 01). RiSCAN Pro\u00ae Version 2.9.0. RIEGL RIEGL VZ-400 VZ-400. RIEGL Laser Measurement Systems GmbH. Available online: http:\/\/www.riegl.com\/products\/software-packages\/riscan-pro\/."},{"key":"ref_49","unstructured":"(2022, July 01). CloudCompare\u00ae. CloudCompare (Version 2.12). Available online: http:\/\/www.cloudcompare.org\/."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Maaten, L.V.D., and Weinberger, K.Q. (2017). Densely Connected Convolutional Networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in vegetation remote sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","unstructured":"Tan, M., and Le, Q.V. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA. Available online: https:\/\/proceedings.mlr.press\/v97\/tan19a.html."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_56","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). European Conference on Computer Vision, Springer."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0378-3758(00)00115-4","article-title":"Improving predictive inference under covariate shift by weighting the log-likelihood function","volume":"90","author":"Shimodaira","year":"2000","journal-title":"J. Stat. Plan. Inference"},{"key":"ref_58","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_61","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"635440","DOI":"10.3389\/fpls.2021.635440","article-title":"Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning","volume":"12","author":"Seidel","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Cadieu, C.F., Hong, H., Yamins, D.L.K., Pinto, N., Ardila, D., Solomon, E.A., Majaj, N.J., and DiCarlo, J.J. (2014). Deep neural networks rival the representation of primate it cortex for core visual object recognition. PLoS Comput. Biol., 10.","DOI":"10.1371\/journal.pcbi.1003963"},{"key":"ref_65","first-page":"92","article-title":"Evaluation of pooling operations in convolutional architectures for object recognition","volume":"Volume 6354","author":"Diamantaras","year":"2010","journal-title":"International Conference on Artificial Neural Networks"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1165\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:37:34Z","timestamp":1760121454000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1165"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,20]]},"references-count":65,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15041165"],"URL":"https:\/\/doi.org\/10.3390\/rs15041165","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,20]]}}}