{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:01:11Z","timestamp":1773414071110,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,23]],"date-time":"2018-04-23T00:00:00Z","timestamp":1524441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As light detection and ranging (LiDAR) technology becomes more available, it has become common to use these datasets to generate remotely sensed forest inventories across landscapes. Traditional methods for generating these inventories employ the use of height and proportion metrics to measure LiDAR returns and relate these back to field data using predictive models. Here, we employ a three-dimensional convolutional neural network (CNN), a deep learning technique that scans the LiDAR data and automatically generates useful features for predicting forest attributes. We test the accuracy in estimating forest attributes using the three-dimensional implementations of different CNN models commonly used in the field of image recognition. Using the best performing model architecture, we compared CNN performance to models developed using traditional height metrics. The results of this comparison show that CNNs produced 12% less prediction error when estimating biomass, 6% less in estimating tree count, and 2% less when estimating the percentage of needleleaf trees. We conclude that using CNNs can be a more accurate means of interpreting LiDAR data for forest inventories compared to standard approaches.<\/jats:p>","DOI":"10.3390\/rs10040649","type":"journal-article","created":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T04:44:48Z","timestamp":1524545088000},"page":"649","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory"],"prefix":"10.3390","volume":"10","author":[{"given":"Elias","family":"Ayrey","sequence":"first","affiliation":[{"name":"School of Forest Resources, University of Maine, 5755 Nutting Hall, Orono, ME 04469-5755, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3011-7934","authenticated-orcid":false,"given":"Daniel J.","family":"Hayes","sequence":"additional","affiliation":[{"name":"School of Forest Resources, University of Maine, 5755 Nutting Hall, Orono, ME 04469-5755, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1080\/02827580410019490","article-title":"Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators","volume":"19","author":"Lim","year":"2004","journal-title":"Scand. J. For. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S0034-4257(01)00290-5","article-title":"Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data","volume":"80","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote sensing technologies for enhancing forest inventories: A review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2012.02.023","article-title":"Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys","volume":"123","author":"Hudak","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.foreco.2008.08.021","article-title":"Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps","volume":"257","author":"Graf","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"512","DOI":"10.5558\/tfc2011-050","article-title":"Operational implementation of a LiDAR inventory in Boreal Ontario","volume":"87","author":"Woods","year":"2011","journal-title":"For. Chron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"722","DOI":"10.5558\/tfc2013-132","article-title":"A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach","volume":"89","author":"White","year":"2013","journal-title":"For. Chron."},{"key":"ref_8","first-page":"426","article-title":"Parametric vs. nonparametric LiDAR models for operational forest inventory in boreal Ontario","volume":"39","author":"Penner","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1093\/forestry\/cpq022","article-title":"Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: Application of multiple optical\/LiDAR-derived predictors","volume":"83","author":"Latifi","year":"2010","journal-title":"Forestry"},{"key":"ref_10","first-page":"1367","article-title":"Predicting forest stand characteristics with airborne scanning lidar","volume":"66","author":"Means","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.rse.2016.10.022","article-title":"A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory","volume":"194","author":"Nilsson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2938","DOI":"10.1080\/01431161.2016.1219425","article-title":"Updating residual stem volume estimates using ALS-and UAV-acquired stereo-photogrammetric point clouds","volume":"38","author":"Goodbody","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","unstructured":"McGaughey, R.J. (2009). FUSION\/LDV: Software for LIDAR Data Analysis and Visualization."},{"key":"ref_14","unstructured":"Silva, C.A., Crookston, N.L., Hudak, A.T., and Vierling, L.A. (2017, December 12). rLiDAR: An R Package for Reading, Processing and Visualizing LiDAR (Light Detection and Ranging) Data, Version 0.1. Available online: https:\/\/cran.r-project.org\/package=rLiDAR."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5600","DOI":"10.1109\/TGRS.2015.2425916","article-title":"Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data","volume":"53","author":"Junttila","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2006.03.003","article-title":"Assessment of forest structure with airborne LiDAR and the effects of platform altitude","volume":"103","author":"Goodwin","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.05.032","article-title":"Removing bias from LiDAR-based estimates of canopy height: Accounting for the effects of pulse density and footprint size","volume":"198","author":"Roussel","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"623","DOI":"10.5589\/m03-030","article-title":"Simulating the effects of lidar scanning angle for estimation of mean tree height and canopy closure","volume":"29","author":"Holmgren","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"830","DOI":"10.3390\/rs4040830","article-title":"LiDAR sampling density for forest resource inventories in Ontario, Canada","volume":"4","author":"Treitz","year":"2012","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"363","DOI":"10.3390\/f5020363","article-title":"Assessing the feasibility of low-density LiDAR for stand inventory attribute predictions in complex and managed forests of northern Maine, USA","volume":"5","author":"Hayashi","year":"2014","journal-title":"Forests"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1080\/07038992.2017.1263152","article-title":"Estimating stem diameter distributions in a management context for a tolerant hardwood forest using ALS height and intensity data","volume":"43","author":"Shang","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1016\/S0031-3203(01)00086-3","article-title":"A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video","volume":"35","author":"Antani","year":"2002","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1080\/014311697218863","article-title":"Biomass retrieval from high-dimensional active\/passive remote sensing data by using artificial neural networks","volume":"18","author":"Jin","year":"1997","journal-title":"Int. J Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"484","DOI":"10.5424\/fs\/2013223-03874","article-title":"Forest attributes estimation using aerial laser scanner and TM data","volume":"22","author":"Joibary","year":"2013","journal-title":"For. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TGRS.2009.2029864","article-title":"Neural networks for the prediction of species-specific plot volumes using airborne laser scanning and aerial photographs","volume":"48","author":"Niska","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","first-page":"255","article-title":"Convolutional networks for images, speech, and time series","volume":"3361","author":"LeCun","year":"1995","journal-title":"Handb. Brain Theory Neural Netw."},{"key":"ref_28","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, MIT Press Ltd."},{"key":"ref_29","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 8\u201310). Going deeper with convolutions. Proceedings of the Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014, January 23\u201328). Deepface: Closing the gap to human-level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.220"},{"key":"ref_31","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_33","unstructured":"Smith, L.N., and Topin, N. (arXiv, 2016). Deep convolutional neural network design patterns, arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1080\/2150704X.2015.1088668","article-title":"Deep learning-based tree classification using mobile LiDAR data","volume":"6","author":"Guan","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3011","DOI":"10.1109\/JSTARS.2016.2634863","article-title":"Hyperspectral and LiDAR data fusion using extinction profiles and deep convolutional neural network","volume":"10","author":"Ghamisi","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Maturana, D., and Scherer, S. (2015, January 26\u201330). 3D convolutional neural networks for landing zone detection from lidar. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139679"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, B. (arXiv, 2016). 3D fully convolutional network for vehicle detection in point cloud, arXiv.","DOI":"10.1109\/IROS.2017.8205955"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Matti, D., Ekenel, H.K., and Thiran, J. (arXiv, 2017). Combining LiDAR space clustering and convolutional neural networks for pedestrian detection, arXiv.","DOI":"10.1109\/AVSS.2017.8078512"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., and Nielsen, M. (2013). Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-642-40763-5_31"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_41","unstructured":"Yi, D., Zhou, M., Chen, Z., and Gevaert, O. (arXiv, 2016). 3-D Convolutional Neural Networks for Glioblastoma Segmentation, arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Maturana, D., and Scherer, S. (October, January 28). Voxnet: A 3d convolutional neural network for real-time object recognition. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353481"},{"key":"ref_43","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., and Guibas, L.J. (July, January 26). Volumetric and multi-view CNNS for object classification on 3d data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_44","unstructured":"Wu, J., Zhang, C., Xue, T., Freeman, B., and Tenenbaum, J. (2016). Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. Advances in Neural Information Processing Systems, MIT Press Ltd."},{"key":"ref_45","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_47","unstructured":"Weiskittel, A., Russell, M., Wagner, R., and Seymour, R. (2012). Refinement of the Forest Vegetation Simulator Northeast Variant Growth and Yield Model: Phase III, Cooperative Forestry Research Unit\u2014University of Maine."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Woodall, C.W., Heath, L.S., Domke, G.M., and Nichols, M.C. (2011). Methods and Equations for Estimating Aboveground Volume, Biomass, and Carbon for Trees in the US Forest Inventory, 2010.","DOI":"10.2737\/NRS-GTR-88"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1093\/njaf\/28.2.84","article-title":"Maximum and largest crown width equations for 15 tree species in Maine","volume":"28","author":"Russell","year":"2011","journal-title":"North J. Appl. For."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Amaral, T., Silva, L.M., Alexandre, L.A., Kandaswamy, C., de S\u00e1, J.M., and Santos, J.M. (2014). Transfer learning using rotated image data to improve deep neural network performance. International Conference Image Analysis and Recognition, Springer.","DOI":"10.1007\/978-3-319-11758-4_32"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Paulin, M., Revaud, J., Harchaoui, Z., Perronnin, F., and Schmid, C. (2014, January 24\u201327). Transformation pursuit for image classification. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.466"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4045","DOI":"10.3390\/rs5084045","article-title":"NASA Goddard\u2019s LiDAR, hyperspectral and thermal (G-LiHT) airborne imager","volume":"5","author":"Cook","year":"2013","journal-title":"Remote Sens."},{"key":"ref_53","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_54","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_55","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (arXiv, 2012). Improving neural networks by preventing co-adaptation of feature detectors, arXiv."},{"key":"ref_56","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (arXiv, 2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems, arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_58","unstructured":"Kuznetsova, A., Brockhoff, P.B., and Christensen, R.H.B. (2017, December 12). Package \u2018lmerTest\u2019. R Package Version 2.0. Available online: https:\/\/cran.r-project.org\/package=lmerTest."},{"key":"ref_59","unstructured":"Diaz-Uriarte, R., and de Andr\u00e9s, S.A. (arXiv, 2005). Variable selection from random forests: Application to gene expression data, arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1080\/07038992.2016.1229597","article-title":"Influence of prediction cell size on LiDAR-derived area-based estimates of total volume in mixed-species and multicohort forests in northeastern North America","volume":"42","author":"Hayashi","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/01621459.1979.10481038","article-title":"Robust locally weighted regression and smoothing scatterplots","volume":"74","author":"Cleveland","year":"1979","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2014.10.004","article-title":"Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data","volume":"156","author":"Bouvier","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Tuominen, S., and Haapanen, R. (2013). Estimation of forest biomass by means of genetic algorithm-based optimization of airborne laser scanning and digital aerial photograph features. Silva Fenn., 47.","DOI":"10.14214\/sf.902"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2015.12.039","article-title":"On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters","volume":"175","author":"Renaud","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_65","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Pinto, N., Cox, D.D., and DiCarlo, J.J. (2008). Why is real-world visual object recognition hard?. PLoS Comput. Biol., 4.","DOI":"10.1371\/journal.pcbi.0040027"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Taylor, L., and Nitschke, G. (arXiv, 2017). Improving Deep Learning using Generic Data Augmentation, arXiv.","DOI":"10.1109\/SSCI.2018.8628742"},{"key":"ref_68","unstructured":"Simonyan, K., and Zisserman, A. (2014). Two-stream convolutional networks for action recognition in videos. Advances in Neural Information Processing Systems, MIT Press Ltd."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Peng, X., Sun, B., Ali, K., and Saenko, K. (2015, January 7\u201313). Learning deep object detectors from 3D models. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.151"},{"key":"ref_70","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., and Lopez, A.M. (July, January 26). The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.ecolmodel.2015.11.018","article-title":"Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests","volume":"326","author":"Fischer","year":"2016","journal-title":"Ecol. Model."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014). Visualizing and understanding convolutional networks. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_74","unstructured":"Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., and Lipson, H. (arXiv, 2015). Understanding neural networks through deep visualization, arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1080\/01431160701736489","article-title":"Review of methods of small footprint airborne laser scanning for extracting forest inventory data in boreal forests","volume":"29","author":"Leckie","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.biombioe.2007.06.022","article-title":"Estimating biomass of individual pine trees using airborne lidar","volume":"31","author":"Popescu","year":"2007","journal-title":"Biomass Bioenergy"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1080\/07038992.2017.1252907","article-title":"Layer Stacking: A Novel Algorithm for Individual Forest Tree Segmentation from LiDAR Point Clouds","volume":"43","author":"Ayrey","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_78","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_79","unstructured":"Szegedy, C., Toshev, A., and Erhan, D. (2013). Deep neural networks for object detection. Advances in Neural Information Processing Systems, MIT Press Ltd."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_81","unstructured":"Kulkarni, T.D., Whitney, W.F., Kohli, P., and Tenenbaum, J. (2015). Deep convolutional inverse graphics network. Advances in Neural Information Processing Systems, MIT Press Ltd."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Yan, X., Yang, J., Sohn, K., and Lee, H. (2016). Attribute2image: Conditional image generation from visual attributes. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46493-0_47"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/649\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:01:42Z","timestamp":1760194902000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/649"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,23]]},"references-count":82,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["rs10040649"],"URL":"https:\/\/doi.org\/10.3390\/rs10040649","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,23]]}}}