Tree species classification on images from airborne mobile mapping using ML.NET

References

• Ahmed, Z., Amizadeh, S., Bilenko, M., Carr, R., Chin, W. S., Dekel, Y., Dupre, X., Eksarevsliy, V., Filipi, S., Finley, T., Goswami, A., Hoover, M., Inglis, S., Interlandi, M., Kazmi, N., Krivosheev, G., Luferenko, P., Matantsev, I., Matusevych, S.,… Zhu, Y. (2019, July). Machine learning at Microsoft with ML. NET. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2448–15).
   Google Scholar
• Al-Haija, Q. A., & Adebanjo, A. (2020). Breast cancer diagnosis in histopathological images using ResNet-50 convolutional neural network. 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1–7). IEEE.
   Google Scholar
• Avtar, R., Malik, R., Musthafa, M., Rathore, V. S., Kumar, P., & Singh, G. (2023). Forest plantation species classification using Full-Pol-Time-Averaged SAR scattering powers. Remote Sensing Applications: Society & Environment, 29, 100924. https://doi.org/10.1016/j.rsase.2023.100924
   Google Scholar
• Barshooi, A. H., & Amirkhani, A. (2022). A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-Ray images. Biomedical Signal Processing and Control, 72, 103326. https://doi.org/10.1016/j.bspc.2021.103326
   PubMed Web of Science ®Google Scholar
• Bont, L., Hill, A., Waser, L., Bürgi, A., Ginzler, C., & Blattert, C. (2020). Airborne-laser-scanning-derived auxiliary information discriminating between broadleaf and conifer trees improves the accuracy of models for predicting timber volume in mixed and heterogeneously structured forests. Forest Ecology and Management, 459, 117856. https://doi.org/10.1016/j.foreco.2019.117856
   Web of Science ®Google Scholar
• Brovkina, O., Cienciala, E., Surový, P., & Janata, P. (2018). Unmanned Aerial Vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands. Geo-Spatial Information Science, 21(1), 12–20. https://doi.org/10.1080/10095020.2017.1416994
   Web of Science ®Google Scholar
• Cao, K., & Zhang, X. (2020). An improved res-unet model for tree species classification using airborne high-resolution images. Remote Sensing, 12(7), 1128. https://doi.org/10.3390/rs12071128
   Web of Science ®Google Scholar
• Chamberlain, C. P., Meador, A. J. S., & Thode, A. E. (2020). Airborne lidar provides reliable estimates of canopy base height and canopy bulk density in southwestern ponderosa pine forests. Forest Ecology and Management, 481, 118695. https://doi.org/10.1016/j.foreco.2020.118695
   Web of Science ®Google Scholar
• Ciregan, D., Meier, U., & Schmidhuber, J. (2012). Multi-column deep neural networks for image classification. 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 3642–3649). IEEE.
   Google Scholar
• Crisci, C., Ghattas, B., & Perera, G. (2012). A review of supervised machine learning algorithms and their applications to ecological data. Ecological Modelling, 240, 113–122. https://doi.org/10.1016/j.ecolmodel.2012.03.001
   Web of Science ®Google Scholar
• Dalponte, M., Bruzzone, L., & Gianelle, D. (2012). Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sensing of Environment, 123, 258–270. https://doi.org/10.1016/j.rse.2012.03.013
   Web of Science ®Google Scholar
• Darwish, A., Leukert, K., & Reinhardt, W. (2003). Image segmentation for the purpose of object-based classification. IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477) (Vol. 3, pp. 2039–2041). Ieee.
   Google Scholar
• de Vries, T. N., Bronkhorst, J., Vermeer, M., Donker, J. C., Briels, S. A., Ziar, H., Zeman, M., & Isabella, O. (2020). A quick-scan method to assess photovoltaic rooftop potential based on aerial imagery and LiDAR. Solar Energy, 209, 96–107. https://doi.org/10.1016/j.solener.2020.07.035
   Web of Science ®Google Scholar
• Egli, S., & Höpke, M. (2020). CNN-Based tree species classification using high resolution RGB image data from automated UAV observations. Remote Sensing, 12(23), 3892. https://doi.org/10.3390/rs12233892.
   Web of Science ®Google Scholar
• El-Sheimy, N. (1996). The development of VISAT: A mobile survey system for GIS applications. University of Calgary.
   Google Scholar
• Etienne, A., Ahmad, A., Aggarwal, V., & Saraswat, D. (2021). Deep learning-based object detection system for identifying weeds using UAS imagery. Remote Sensing, 13(24), 5182. https://doi.org/10.3390/rs13245182
   Web of Science ®Google Scholar
• Firat, H., & Hanbay, D. (2021). Classification of hyperspectral images using 3D CNN based ResNet50. 2021 29th Signal Processing and Communications Applications Conference (SIU) (pp. 1–4). IEEE.
   Google Scholar
• Franklin, S. E. (2018). Pixel-and object-based multispectral classification of forest tree species from small unmanned aerial vehicles. Journal of Unmanned Vehicle Systems, 6(4), 195–211. https://doi.org/10.1139/juvs-2017-0022
   Web of Science ®Google Scholar
• Franklin, S. E., & Ahmed, O. S. (2018). Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data. International Journal of Remote Sensing, 39(15–16), 5236–5245. https://doi.org/10.1080/01431161.2017.1363442
   Web of Science ®Google Scholar
• Franklin, S. E., Ahmed, O. S., & Williams, G. (2017). Northern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle. PE&Rs, Photogrammetric Engineering & Remote Sensing, 83(7), 501–507. https://doi.org/10.14358/PERS.83.7.501
   Web of Science ®Google Scholar
• Fukuda, M., Ariji, Y., Kise, Y., Nozawa, M., Kuwada, C., Funakoshi, T., Muramatsu, C., Fujita, H., Katsumata, A., & Ariji, E. (2020). Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 130(3), 336–343. https://doi.org/10.1016/j.oooo.2020.04.005
   PubMed Web of Science ®Google Scholar
• Gamdha, D., Unnikrishnakurup, S., Rose, K. J., Surekha, M., Purushothaman, P., Ghose, B., & Balasubramaniam, K. (2021). Automated defect recognition on X-ray radiographs of solid propellant using deep learning based on convolutional neural networks. Journal of Nondestructive Evaluation, 40(1), 1–13. https://doi.org/10.1007/s10921-021-00750-4
   Web of Science ®Google Scholar
• Gini, R., Passoni, D., Pinto, L., & Sona, G. (2012). Aerial images from an UAV system: 3D modeling and tree species classification in a park area. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39(B1), 361–366. https://doi.org/10.5194/isprsarchives-XXXIX-B1-361-2012
   Google Scholar
• Goldblatt, R., Stuhlmacher, M. F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A., Cheng, W., & Balling, R. C., Jr. (2018). Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sensing of Environment, 205, 253–275. https://doi.org/10.1016/j.rse.2017.11.026
   Web of Science ®Google Scholar
• Guo, X., Li, H., Jing, L., & Wang, P. (2022). Individual tree species classification based on convolutional neural networks and multitemporal high-resolution remote sensing images. Sensors, 22(9), 3157. https://doi.org/10.3390/s22093157
   PubMed Web of Science ®Google Scholar
• Habibzadeh, M., Jannesari, M., Rezaei, Z., Baharvand, H., & Totonchi, M. (2018). Automatic white blood cell classification using pre-trained deep learning models: Resnet and inception. Tenth International Conference on Machine Vision (ICMV 2017) (Vol. 10696, pp. 274–281). SPIE.
   Google Scholar
• Haq, M. A., Rahaman, G., Baral, P., & Ghosh, A. (2021). Deep learning based supervised image classification using UAV images for forest areas classification. J Indian Soc Remote Sens, 49(3), 601–606. https://doi.org/10.1007/s12524-020-01231-3
   Web of Science ®Google Scholar
• Heinzel, J., & Koch, B. (2011). Exploring full-waveform LiDAR parameters for tree species classification. International Journal of Applied Earth Observation and Geoinformation: ITC Journal, 13(1), 152–160. https://doi.org/10.1016/j.jag.2010.09.010
   Web of Science ®Google Scholar
• Heinzel, J., & Koch, B. (2012). Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation. International Journal of Applied Earth Observation and Geoinformation, 18, 101–110. https://doi.org/10.1016/j.jag.2012.01.025
   Web of Science ®Google Scholar
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).
   Google Scholar
• He, T., Zhou, H., Xu, C., Hu, J., Xue, X., Xu, L., Lou, X., Zeng, K., & Wang, Q. (2023). Deep learning in forest tree species classification using sentinel-2 on google Earth engine: A case study of Qingyuan County. Sustainability, 15(3), 2741. https://doi.org/10.3390/su15032741
   Web of Science ®Google Scholar
• Huang, J., Zhang, X., Xin, Q., Sun, Y., & Zhang, P. (2019). Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 91–105. https://doi.org/10.1016/j.isprsjprs.2019.02.019
   Web of Science ®Google Scholar
• Hung, J., & Carpenter, A. (2017). Applying faster R-CNN for object detection on malaria images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 56–61).
   Google Scholar
• Immitzer, M., Atzberger, C., & Koukal, T. (2012). Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sensing, 4(9), 2661–2693. https://doi.org/10.3390/rs4092661
   Web of Science ®Google Scholar
• Kattenborn, T., Eichel, J., & Fassnacht, F. E. (2019). Convolutional neural networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. Scientific Reports, 9(1), 17656. https://doi.org/10.1038/s41598-019-53797-9
   PubMedGoogle Scholar
• Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. Isprs Journal of Photogrammetry & Remote Sensing, 173, 24–49. https://doi.org/10.1016/j.isprsjprs.2020.12.010
   Web of Science ®Google Scholar
• Ke, Y., Quackenbush, L. J., & Im, J. (2010). Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification. Remote Sensing of Environment, 114(6), 1141–1154. https://doi.org/10.1016/j.rse.2010.01.002
   Web of Science ®Google Scholar
• Khosravian, A., Amirkhani, A., Kashiani, H., & Masih-Tehrani, M. (2021). Generalizing state-of-the-art object detectors for autonomous vehicles in unseen environments. Expert Systems with Applications, 183, 115417. https://doi.org/10.1016/j.eswa.2021.115417
   Web of Science ®Google Scholar
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
   Web of Science ®Google Scholar
• Krüger Geb Amiri, N., Heurich, M., Krzystek, P., & Skidmore, A. (2018). Feature relevance assessment of multispectral airborne LiDAR data for tree species classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3, 31–34. https://doi.org/10.5194/isprs-archives-XLII-3-31-2018
   Google Scholar
• Kukkonen, M., Maltamo, M., Korhonen, L., & Packalen, P. (2019). Multispectral airborne LiDAR data in the prediction of boreal tree species composition. IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society, 57(6), 3462–3471. https://doi.org/10.1109/TGRS.2018.2885057
   Web of Science ®Google Scholar
• Kuwada, C., Ariji, Y., Fukuda, M., Kise, Y., Fujita, H., Katsumata, A., & Ariji, E. (2020). Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 130(4), 464–469. https://doi.org/10.1016/j.oooo.2020.04.813
   PubMed Web of Science ®Google Scholar
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
   PubMed Web of Science ®Google Scholar
• Li, H., Hu, B., Li, Q., & Jing, L. (2021). CNN-Based individual tree species classification using high-resolution satellite imagery and airborne LiDAR data. Forests, 12(12), 1697. https://doi.org/10.3390/f12121697
   Web of Science ®Google Scholar
• Li, B., & Lima, D. (2021). Facial expression recognition via ResNet-50. International Journal of Cognitive Computing in Engineering, 2, 57–64. https://doi.org/10.1016/j.ijcce.2021.02.002
   Google Scholar
• Li, Y., Tang, B., Li, J., Sun, W., Lin, Z., & Luo, Q. (2021). Research on common tree species recognition by faster R-CNN based on whole tree image. In 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP) (pp. 28–32). IEEE.
   Google Scholar
• Liu, D., & Xia, F. (2010). Assessing object-based classification: Advantages and limitations. Remote Sensing Letters, 1(4), 187–194. https://doi.org/10.1080/01431161003743173
   Web of Science ®Google Scholar
• Liu, Q., Xiao, L., Yang, J., & Wei, Z. (2020). CNN-enhanced graph convolutional network with pixel-and superpixel-level feature fusion for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 59(10), 8657–8671. https://doi.org/10.1109/TGRS.2020.3037361
   Web of Science ®Google Scholar
• Magnard, C., Morsdorf, F., Small, D., Stilla, U., Schaepman, M. E., & Meier, E. (2016). Single tree identification using airborne multibaseline SAR interferometry data. Remote Sensing of Environment, 186, 567–580. https://doi.org/10.1016/j.rse.2016.09.018
   Web of Science ®Google Scholar
• Mahdi, F. P., Motoki, K., & Kobashi, S. (2020). Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs. Scientific Reports, 10(1), 19261. https://doi.org/10.1038/s41598-020-75887-9
   PubMed Web of Science ®Google Scholar
• Mahdy, L. N., Ezzat, K. A., Elmousalami, H. H., Ella, H. A., & Hassanien, A. E. (2020). Automatic x-ray COVID-19 lung image classification system based on multi-level thresholding and support vector machine. MedRxiv, 2020.03. https://doi.org/10.1101/2020.03.30.20047787
   Google Scholar
• Mallick, P. K., Ryu, S. H., Satapathy, S. K., Mishra, S., Nguyen, G. N., & Tiwari, P. (2019). Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access, 7, 46278–46287. https://doi.org/10.1109/ACCESS.2019.2902252
   Web of Science ®Google Scholar
• Michałowska, M. (2020). Verification of building constructions surroundings based on airborne laser scanning data. XV International Conference on Durability of Building Materials and Components (DBMC 2020).
   Google Scholar
• Michałowska, M., & Rapiński, J. (2021). A review of tree species classification based on airborne LiDAR data and applied classifiers. Remote Sensing, 13(3), 353. https://doi.org/10.3390/rs13030353
   Web of Science ®Google Scholar
• Mikołajczyk, A., & Grochowski, M. (2018). Data augmentation for improving deep learning in image classification problem. 2018 international interdisciplinary PhD workshop (IIPhDW) (pp. 117–122). IEEE.
   Google Scholar
• MS, M., SS, S. R., & S S, S. R. (2022). Optimal squeeze net with deep neural network-based aerial image classification model in unmanned aerial vehicles. Traitement du Signal, 39(1), 275–281. https://doi.org/10.18280/ts.390128
   Web of Science ®Google Scholar
• Murata, M., Ariji, Y., Ohashi, Y., Kawai, T., Fukuda, M., Funakoshi, T., Kise, Y., Nozawa, M., Katsumata, A., Fujita, H., & Ariji, E. (2019). Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiology, 35(3), 301–307. https://doi.org/10.1007/s11282-018-0363-7
   PubMed Web of Science ®Google Scholar
• Natesan, S., Armenakis, C., & Vepakomma, U. (2019). Resnet-based tree species classification using uav images. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLII-2/W13, 475–481. https://doi.org/10.5194/isprs-archives-XLII-2-W13-475-2019
   Google Scholar
• Natesan, S., Armenakis, C., & Vepakomma, U. (2020). Individual tree species identification using dense convolutional network (DenseNet) on multitemporal RGB images from UAV. Journal of Unmanned Vehicle Systems, 8(4), 310–333. https://doi.org/10.1139/juvs-2020-0014
   Web of Science ®Google Scholar
• Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyyppä, J., Saari, H., Pölönen, I., Imai, N. N., & Tommaselli, A. M. (2017). Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sensing, 9(3), 185. https://doi.org/10.3390/rs9030185
   Web of Science ®Google Scholar
• Nogueira, K., dos Santos, J. A., Menini, N., Silva, T. S., Morellato, L. P. C., & Torres, R. D. S. (2019). Spatio-temporal vegetation pixel classification by using convolutional networks. IEEE Geoscience and Remote Sensing Letters, 16(10), 1665–1669. https://doi.org/10.1109/LGRS.2019.2903194
   Web of Science ®Google Scholar
• Onishi, M., & Ise, T. (2018). Automatic classification of trees using a UAV onboard camera and deep learning. arXiv preprint arXiv:1804.10390.
   Google Scholar
• Onishi, M., & Ise, T. (2021). Explainable identification and mapping of trees using UAV RGB image and deep learning. Scientific Reports, 11(1), 903. https://doi.org/10.1038/s41598-020-79653-9
   PubMed Web of Science ®Google Scholar
• Ørka, H. O., Næsset, E., & Bollandsås, O. M. (2010). Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning. Remote Sensing of Environment, 114(7), 1445–1461. https://doi.org/10.1016/j.rse.2010.01.024
   Web of Science ®Google Scholar
• Perez, L., & Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621.
   Google Scholar
• Pyysalo, U., & Hyyppa, H. (2002). Reconstructing tree crowns from laser scanner data for feature extraction. International Archives Of Photogrammetry Remote Sensing And Spatial Information Sciences, 34(3/B), 218–221. https://scholar.google.com/scholar_lookup?title=Reconstructing+tree+crowns+from+laser+scanner+data+for+feature+extraction&author=Pyysalo,+U.&author=Hyyppa,+H.&publication_year=2002&journal=Int.+Arch.+Photogramm.+Remote+Sens.+Spat.+Inf.+Sci.&volume=34&pages=218%E2%80%93221.
   Google Scholar
• Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779–788).
   Google Scholar
• Roberts, R., Giancontieri, G., Inzerillo, L., & DiMino, G. (2020). Towards low-cost pavement condition health monitoring and analysis using deep learning. Applied Sciences, 10(1), 319. https://doi.org/10.3390/app10010319
   Google Scholar
• 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 Sensing, 11(6), 643. https://doi.org/10.3390/rs11060643
   Web of Science ®Google Scholar
• Sarrazin, D., van Aardt, J., Asner, G., Mcglinchy, J., Messinger, D., & Wu, J. (2011). Fusing small-footprint waveform LiDAR and hyperspectral data for canopy-level species classification and herbaceous biomass modeling in savanna ecosystems. Canadian Journal of Remote Sensing, 37(6), 653–665. https://doi.org/10.5589/m12-007
   Web of Science ®Google Scholar
• Sarwinda, D., Paradisa, R. H., Bustamam, A., & Anggia, P. (2021). Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer. Procedia Computer Science, 179, 423–431. https://doi.org/10.1016/j.procs.2021.01.025
   Google Scholar
• Schiefer, F., Kattenborn, T., Frick, A., Frey, J., Schall, P., Koch, B., & Schmidtlein, S. (2020). Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 170, 205–215. https://doi.org/10.1016/j.isprsjprs.2020.10.015
   Web of Science ®Google Scholar
• Shen, X., & Cao, L. (2017). Tree-species classification in subtropical forests using airborne hyperspectral and LiDAR data. Remote Sensing, 9(11), 1180. https://doi.org/10.3390/rs9111180
   Web of Science ®Google Scholar
• Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., & Moore, R. (2013). Real-time human pose recognition in parts from single depth images. Communications of the ACM, 56(1), 116–124. https://doi.org/10.1145/2398356.2398381
   Web of Science ®Google Scholar
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
   Google Scholar
• Solberg, S., Næsset, E., & Bollandsas, O. M. (2006). Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest. Photogrammetric Engineering & Remote Sensing, 72(12), 1369–1378. https://doi.org/10.14358/PERS.72.12.1369
   Web of Science ®Google Scholar
• Song, Y., He, F., & Zhang, X. (2019). To identify tree species with highly similar leaves based on a novel attention mechanism for CNN. IEEE Access, 7, 163277–163286. https://doi.org/10.1109/ACCESS.2019.2951607
   Web of Science ®Google Scholar
• Sothe, C., Dalponte, M., Almeida, C. M. D., Schimalski, M. B., Lima, C. L., Liesenberg, V., Miyoshi, G. T., & Tommaselli, A. M. G. (2019). Tree species classification in a highly diverse subtropical forest integrating UAV-based photogrammetric point cloud and hyperspectral data. Remote Sensing, 11(11), 1338. https://doi.org/10.3390/rs11111338
   Web of Science ®Google Scholar
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9).
   Google Scholar
• Tsai, D. M., & Chen, W. L. (2017). Coffee plantation area recognition in satellite images using Fourier transform. Computers and Electronics in Agriculture, 135, 115–127. https://doi.org/10.1016/j.compag.2016.12.020
   Web of Science ®Google Scholar
• Tzutalin, L. (2015). Git code. https://github.com/tzutalin/labelImg
   Google Scholar
• Udali, A., Lingua, E., & Persson, H. J. (2021). Assessing forest type and tree species classification using sentinel-1 C-Band SAR data in Southern Sweden. Remote Sensing, 13(16), 3237. https://doi.org/10.3390/rs13163237
   Web of Science ®Google Scholar
• Wäldchen, J., Mäder, P., & Cooper, N. (2018). Machine learning for image based species identification. Methods in Ecology and Evolution, 9(11), 2216–2225. https://doi.org/10.1111/2041-210X.13075
   Web of Science ®Google Scholar
• Wang, Q., Bi, S., Sun, M., Wang, Y., Wang, D., Yang, S., & Zhang, J. (2019). Deep learning approach to peripheral leukocyte recognition. PloS One, 14(6), e0218808. https://doi.org/10.1371/journal.pone.0218808
   PubMed Web of Science ®Google Scholar
• Wen, L., Li, X., & Gao, L. (2020). A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Computing and Applications, 32(10), 6111–6124. https://doi.org/10.1007/s00521-019-04097-w
   Web of Science ®Google Scholar
• Wu, Q., Lane, C. R., Li, X., Zhao, K., Zhou, Y., Clinton, N., de Vries, B., Golden, H., & Lang, M. W. (2019). Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google earth engine. Remote Sensing of Environment, 228, 1–13. https://doi.org/10.1016/j.rse.2019.04.015
   PubMed Web of Science ®Google Scholar
• Xuan, J., Li, X., Du, H., Zhou, G., Mao, F., Wang, J., Zhang, B., Gong, Y., Zhu, D., Zhou, L., Huanf, Z., Xu, C., Chen, J., Zhou, Y., Chen, C., Tan, C., & Sun, J. (2022). Intelligent estimating the tree height in urban forests based on deep learning combined with a smartphone and a comparison with UAV-LiDAR. Remote Sensing, 15(1), 97. https://doi.org/10.3390/rs15010097
   Web of Science ®Google Scholar
• Yan, S., Jing, L., & Wang, H. (2021). A new individual tree species recognition method based on a convolutional neural network and high-spatial resolution remote sensing imagery. Remote Sensing, 13(3), 479. https://doi.org/10.3390/rs13030479
   Web of Science ®Google Scholar
• Yao, W., Krzystek, P., & Heurich, M. (2012). Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data. Remote Sensing of Environment, 123, 368–380. https://doi.org/10.1016/j.rse.2012.03.027
   Web of Science ®Google Scholar
• You, H. T., Lei, P., Li, M. S., & Ruan, F. Q. (2020). Forest species classification based on three-dimensional coordinate and intensity information of airborne LiDAR data with random forest method. The International Archives of Photogrammetry, Remote Sensing & Spatial Information Sciences, 42, 117–123. https://doi.org/10.5194/isprs-archives-XLII-3-W10-117-2020
   Google Scholar
• Yu, X., Hyyppä, J., Litkey, P., Kaartinen, H., Vastaranta, M., & Holopainen, M. (2017). Single-sensor solution to tree species classification using multispectral airborne laser scanning. Remote Sensing, 9(2), 108. https://doi.org/10.3390/rs9020108
   Web of Science ®Google Scholar
• Yu, H., & Zahidi, I. (2023). Tailings pond classification based on satellite images and machine learning: An exploration of Microsoft ML. Net Mathematics, 11(3), 517. https://doi.org/10.3390/math11030517
   Web of Science ®Google Scholar
• Zhang, C., Xia, K., Feng, H., Yang, Y., & Du, X. (2021). Tree species classification using deep learning and RGB optical images obtained by an unmanned aerial vehicle. Journal of Forestry Research, 32(5), 1879–1888. https://doi.org/10.1007/s11676-020-01245-0
   Web of Science ®Google Scholar
• Zhou, Y., Liu, W., Bi, H., Chen, R., Zong, S., & Luo, Y. (2022). A detection method for individual infected pine trees with pine wilt disease based on deep learning. Forests, 13(11), 1880. https://doi.org/10.3390/f13111880
   Web of Science ®Google Scholar