Using neural networks in exogenous processes monitoring by remote sensing.
№4 (2014) Varlamov A.D.,Sharapov R.V. Section: Modeling geo objects and geo-processes Keywords:monitoring, exogenous processes, remote sensing, neural network.
Search Results for: "neural networks"
Digital technologies of the future — modern solutions in Earth sciences
№3 (2024) Naumova V.V., Patuk M.I., Eremenko A.S., Zagumennov A.A., Eremenko V.S. УДК 004.85 https://doi.org/10.47148/1609-364X-2024-3-53-63 Key words: Earth sciences; artificial intelligence; quantum technologies; big data; wireless communication technologies; distributed ledger systems Section: Information systems in geology and geophysics
Obtaining retrospective data of the urbanized landscape condition using computer vision methods on the example of construction works
№2 (2024) Koryukin E.A., Bobakov V.S., Butorovа А.S., Sergeev A.P. УДК 504.064.2.001.18 https://doi.org/10.47148/1609-364X-2024-2-54-63 Keywords: neural network; urbanized environment; geoinformation system; dataset; object class; image segmentation; library; artificial intelligent; object recognition; mask r-cnn. Section: Methodological and technological support for data collection … Continue reading
Interpretation and origin determination of oil seepages using marine seismic data: a case study of the Western Continental Margin of India
№1 (2024) Loginov D.S. УДК 528.88 + 528.94 + 550.834.05 https://doi.org/10.47148/1609-364X-2024-1-55-67 Key words: Arabian Sea, visual interpretation, natural oil seepages, Western Continental Margin of India, marine seismic data, Sentinel-2 Section: Geoecology
Geoinformation technologies of computer-aided design of public places in urban areas: problems and experience (on the example of Saransk)
№ 4 (2023) Semina I.A., Malakhova O.E., Teslenok S.A., Yakovenko N.V. УДК 004:004.9:528:528.94:528.48:711.4:912.4:911(470.345) https://doi.org/10.47148/1609-364X-2023-4-15-28 Key words: geoinformation technologies, computer design, three-dimensional information modeling of cities, urban areas, public places, planning and design, AutoCAD program Section: Application of GIS technologies
Geoecological assessment of the prospective system for the development of cities in the Central Chernozemic region of Russia based on the index method
№3 (2023) УДК 911.375:528.88 https://doi.org/10.47148/1609-364X-2023-3-71-80 Dubrovskaya S.A. Key words: nature management, modern challenges, comfort of the urban environment, cartographic models, landscape unit, geomorphometric indicators, spatial differentiation, geoinformation index of comfort Section: Geoecology
Individual and pair representativeness of sampling points selection in interpolation tasks of the heavy metals distribution in the topsoil
№3 (2023) УДК 504.064.2.001.18 https://doi.org/10.47148/1609-364X-2023-3-63-70 Baglaeva E.M., Sergeev A.P., Shichkin A.V., Buevich A.G., Butorova A.S. Key words: representativeness of points, sampling, heavy metals, artificial neural networks, choice of training subset Section: Geoecology
Spectral classification of space agricultural landscape images using ranking of channels according to the highest informativity
№3 (2022) УДК 504.064.37, 004.93.11, 633 https://doi.org/10.47148/1609-364X-2022-3-15-29 Vakulenko D.V., Kravets A.G. Key words: agricultural landscape, multispectral space image, informativity indicator of channel, spectral channel ranking, classification, geospatial recognition system, digital stratification plan. Section: Application of GIS technologies
Development of a database of three-dimensional models of deciduous forest species of Birch and Aspen for a geoinformation model of the forest ecosystem
№3 (2022) УДК 004.94 https://doi.org/10.47148/1609-364X-2022-3-39-46 Vagizov M.R., Shishkin M.L., Istomin E.P., Novikova M.A., Boytsov A.K. Key words: databases, geoinformation modeling, 3-d modeling, forest ecosystems, the birch is hanging, the poplar is trembling, the aspen. Section: Geoecology
Application of the permutation method to the assessment of predictive ability of the models of spatial distribution of copper and iron concentrations in the topsoil
№2 (2022) Sergeev A.P., Butorova A.S., Shichkin A.V., Buevich A.G., Baglaeva E.M., Subbotina I.E. УДК 504.064.2.001.18 https://doi.org/10.47148/1609-364X-2022-2-42-53 Key words: permutation method, randomization, predicted values, observed values, spatial distribution, predictive ability assessment, artificial neural networks Section: Modeling geo objects and geo-processes