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"
Review and Analysis of XAI Methods for Addressing Geoecological Zoning and Public Health Prevention Challenges
№4 (2024) Trofimov Yu.V., Averkin A.N., Cheremisina E.N. УДК 0048 https://doi.org/10.47148/1609-364X-2024-4-93-118 Key words: sustainable development; explainable artificial intelligence (XAI); ecological zoning; geographic information systems (GIS); neuro-fuzzy models; disease prevention; environmental risks; decision support systems; healthcare Section: Artifical intelligence in applied … Continue reading
Basic artificial Intelligence tasks in сontext of geological prospecting
№4 (2024) Сheremisina E.N., Kirpicheva E.Y., Tokareva N.A., Milovidova A.A. УДК 004.8, 303.732, 550.8.01https://doi.org/10.47148/1609-364X-2024-4-83-92 Key words: artificial Intelligence; multidisciplinary research; explainable artificial intelligence; machine learning; neural networks; systematic approach Section: Artifical intelligence in applied fields of knowledge
Features of the use of neural network technology for recognizing graphic images of thermokarst objects
№4 (2024) Zhebsain V.V., Gololobov A.Yu., Basharin N.U., Poselsky A.F. УДК 528.854.2https://doi.org/10.47148/1609-364X-2024-4-39-47 Key words: neural network; thermokarst; modelling; satellite data; aerospace sensing. Section: Modeling geo objects and geo-processes
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