Pattern Recognition in the Tasks of Landform Mapping
https://doi.org/10.31857/S2587556623010089
Abstract
The article aims to show the modern state of pattern recognition techniques for automatic and semi-automatic geomorphological mapping. There is opinion among the geomorphometrists about the expert rules for traditional landform mapping can be quantitated. The general unsolved tasks of automatic landform mapping are: recognition of origin for morphologically similar Earth’s surface forms; criteria development for transfer from morphological to genetic and age landform’s characteristics; preventive choosing the optimal resolution of the remote sensing data; the choosing and rationale of predictor’s weights in statistical modeling procedures. Some cases of the pattern recognition techniques using in geomorphology and landform mapping are given: generalized linear models; classification trees; random forest; artificial neural networks; and computer vision methods. The overall accuracy of the different models according to planar continuous landform recognition (and recognition of lithology types too) is about 50–70% and more. At the same time, specific landform type’s (craters, volcanic cones and others) recognition can reach 90–100%.
Supporting agencies: The study was supported by the Russian Science Foundation (project no. 19-77-10036)
About the Author
S. V. Kharchenko
Moscow State University; Institute of Geography, Russian Academy of Sciences
Russian Federation
Russian Federation
Moscow
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For citations:
Kharchenko S.V. Pattern Recognition in the Tasks of Landform Mapping. Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya. 2023;87(1):192-206. (In Russ.) https://doi.org/10.31857/S2587556623010089
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