METHODS AND OPEN SOURCE MACHINE LEARNING GIS TOOLS FOR FOREST TRANSPORT MODELING

 E. S. Podolskaia

Center for Forest Ecology and Productivity of the Russian Academy of Sciences

Profsoyuznaya st. 84/32 bldg. 14, Moscow, 117997, Russian Federation

 

E-mail: podols_kate@mail.ru

Received: 05.06.2023

Revised: 22.06.2023

Accepted: 23.06.2023

 

Paper describes machine learning (ML) methods and tools for transport modeling to access forest fires and forest resources by ground means for the regions in Russia. Forestry transport accessibility is a subject to be studied and improved. ML methods play an important role in change detection and automated data collection for the transport infrastructure. We have analyzed recent scientific publications of two systems, namely Russian electronic library “CyberLeninka” and European network for researchers ResearchGate. It should be noted that as of autumn 2023 the number of papers on the ML forestry transport modeling in these systems is small. Plugins from Open Source QGIS’s repository were studied. Some possible increase in the number of ML plugins from researchers and students could be expected, individual developers and small groups show their interest in the topic. ML prospects for ground transport modeling in the forestry have not yet been sufficiently studied.

Key words: machine learning, Open Source, GIS, forestry, transport modeling

 

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