Abstract
The article proposes a method of automated solution for determining the species composition, stock coefficient and other characteristics of forest plantations with the use of deep learning. The analysis of existing approaches and ways of forest inventory, which include the use of LiDAR systems and machine learning methods, is carried out. An algorithm is proposed for solving this problem and features of its implementation are given. The problem of combining the data of a “dense cloud” and a lidar survey is considered, a possible solution is proposed. The problem of segmentation of tree crowns among many other objects in this data is also considered. For the segmentation of crowns, it is proposed to use the PointNet neural network of deep learning, which allows segmentation of objects by submitting a point cloud to the input. The description of the architecture and the main features of the neural network use are briefly given. The path of further research is determined.
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References
Weitkamp, C. (ed.).: Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere. vol. 102. Springer (2006)
Chernenkiy, V., Gapanyuk, Y., Revunkov, G., Kaganov, Y., Fedorenko, Y.: Metagraph approach as a data model for cognitive architecture. In: Biologically Inspired Cognitive Architectures Meeting, pp. 50–55. Springer, Cham, August 2018
Lychkov I.I., Alfimtsev A.N., Sakulin S.A.: Tracking of moving objects with regeneration of object feature points. In: 2018 Global Smart Industry Conference (GloSIC), pp. 1–6. IEEE (2018)
Neusypin, K.A., et al.: Algorithm for building models of INS/GNSS integrated navigation system using the degree of identifiability. In: 2018 25th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS), pp. 1–5. IEEE (2018)
Serov, V.A., Voronov, E.M.: Evolutionary algorithms of stable-effective compromises search in multi-object control problems. In: Smart Electromechanical Systems, pp. 19–29. Springer, Cham (2019)
Knyazev, B., Barth, E., Martinetz, T.: Recursive autoconvolution for unsupervised learning of convolutional neural networks. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2486–2493. IEEE (2017)
Tipping, M.E., et al.: Fast marginal likelihood maximisation for sparse Bayesian models. In: AISTATS (2003)
Alexeyev, V.A., et al.: Statistical data on forest fund of Russia and changing of forest productivity in the second half of XX century. St. Petersburg Forest Ecological Center, p. 272 (2004)
Hyyppä, J., et al.: Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. Int. J. Remote Sens. 29(5), 1339–1366 (2008)
Thrower, N.J.W., Jensen, J.R.: The orthophoto and orthophotomap: characteristics, development and application. Am. Cartogr. 3(1), 39–56 (1976)
Heidemann, H.K.: Lidar base specification. US Geol. Surv. (11-B4) (2012)
Nguyen, A., Le, B.: 3D point cloud segmentation: a survey. In: 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM), pp. 225–230. IEEE (2013)
Qi, C.R., et al.: Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)
Chumachenko, S.I., et al.: Simulation modelling of long-term stand dynamics at different scenarios of forest management for coniferous–broad-leaved forests. Ecol. Model. 170(2–3), 345–361 (2003)
Ishiguro, H., Miyashita, T., Tsuji, S.: T-net for navigating a vision-guided robot in a real world. In: Proceedings of 1995 IEEE International Conference on Robotics and Automation, vol. 1, pp. 1068–1073. IEEE, (1995)
Folk, M., et al.: An overview of the HDF5 technology suite and its applications. In: Proceedings of the EDBT/ICDT 2011 Workshop on Array Databases, pp. 36–47. ACM, (2011)
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Eroshenkova, D.A., Terekhov, V.I., Khusnetdinov, D.R., Chumachenko, S.I. (2020). Automated Determination of Forest-Vegetation Characteristics with the Use of a Neural Network of Deep Learning. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research III. NEUROINFORMATICS 2019. Studies in Computational Intelligence, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-30425-6_34
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DOI: https://doi.org/10.1007/978-3-030-30425-6_34
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