Abstract
This work aims to develop an efficient and intelligent method for forest resource management. The objective is to implement an automatic tree species identification system based on 3D data obtained from terrestrial laser scans. The approach adopted concerns first the acquisition of 2D and 3D data, then the processing of LIDAR data and finally a process of identification of tree species by machine learning. A platform is designed and developed to meet this objective. The platform is a means that can be used by local researchers for the identification of tree species, providing a forestry database of the Wadi Cherrat arboretum.
This work has been supported by MESRSFC and CNRST under the project PPR2-OGI-Env, reference PPR2/2016/79, and the project Al Khawarizmi: Tool for intelligent management of irrigation water and forest heritage.
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Acknowledgements
We thank Mr. Rachid Abouelouafa, Provincial Director of Water and Forests, Benslimane for his assistance.
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Abdennour, I., Mah, D., Bernoussi, A.S., Amharref, M. (2022). Automated Recognition of Tree Species by Laser Scanning from 3D Geometric Texture of Tree Barks: Case of the Wadi Cherrat Arboretum. In: Sheikh, Y.H., Rai, I.A., Bakar, A.D. (eds) e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 443. Springer, Cham. https://doi.org/10.1007/978-3-031-06374-9_27
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