ML models and neural networks for analyzing 3D data spatial planning tasks: Example of Khasansky urban district of the Russian Federation
Akylbekov O. Said N.A. Martínez-García R. Gura D.
November 2022Elsevier Ltd
Advances in Engineering Software
2022#173
The article addresses the issue of the Earths surface surveillance. It was determined that artificial intelligence tools and machine learning methods are required to enhance the effectiveness of image recognition. Cores, Era 5, and ArcGIS cloud computing services (APIs) were installed to evaluate spatial data quality. The article presents the results of analyzing climate data received through applications developed by the authors. Raster terrain images of Slavyanka city located in Khasansky District of the Russian Federation were obtained, and corresponding graphs of temperature regimes were built. The root-mean-square deviation (RMSD) value calculated using machine learning techniques served as the basis for developing a neural network. It was later used to define the land features of the Khasansky city district to evaluate the possibility of construction works in this area. The map received allowed setting the designation purpose of the land plots and their rating in terms of suitability for construction works and defining areas not serviceable for any civil engineering activities. Complex climatic factors and obtained raster images of the terrain were considered. Testing the neural network for terrain recognition showed the models training error of up to 1.3%. In terms of statistics, RSSD (2.5 ± 0.95), the Pearsons criterion, the Students criterion, and the statistical significance of the results were estimated. The obtained results can be used for terrain recognition and expert evaluation of land plots.
GIS , Landscape structure , Machine learning , Neural networks , SDGs , Spatial planning
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Department of Software Engineering, Satbayev University, Almaty, Kazakhstan
College of Mass Communication, Ajman University, Ajman, United Arab Emirates
Department of Mining Technology, Topography and Structures, University of León, León, Spain
Department of Cadastre and Geoengineering, Kuban State Technological University, Krasnodar, Russian Federation
Department of Geodesy, Kuban State Agrarian University, Krasnodar, Russian Federation
Department of Software Engineering
College of Mass Communication
Department of Mining Technology
Department of Cadastre and Geoengineering
Department of Geodesy
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