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Mohan Shyama (Jawaharlal Nehru Technological University, Hyderabad, India), Giridhar M.V.S.S (Jawaharlal Nehru Technological University, Hyderabad, India)
A Brief Review of Recent Developments in the Integration of Deep Learning with GIS
Geomatics and Environmental Engineering, 2022, nr 16/2, s. 21-38, bibliogr. 55 poz.
Słowa kluczowe
System Informacji Przestrzennej, Uczenie maszynowe, Uczenie sieci neuronowych, Analiza przestrzenna, Planowanie przestrzenne miasta, Klęski żywiołowe, Zarządzanie ruchem drogowym
Geographic Information System (GIS), Machine learning, Learning of neural networks, Spatial analysis, Town spatial planning, Natural disaster, Road traffic management
The interaction of Deep Learning (DL) methods with Geographical Information System (GIS) provides the opportunity to obtain new insights into environmental processes through the spatial, temporal and spectral resolutions as well as data integration. The two technologies may be connected to form a dynamic system that is incredibly well adapted to the evaluation of environmental conditions through the interrelationships of texture, size, pattern, and process. This perspective has acquired popularity in multiple disciplines. GIS is significantly dependant on processors, particularly for 3D calculations, map rendering, and route calculation whereas DL can process huge amounts of data. DL has received a lot of attention recently as a technology with a plethora of promising results. Furthermore, the growing use of DL methods in a variety of disciplines, including GIS, is evident. This study tries to provide a brief overview of the use of DL methods in GIS. This paper introduces the essential DL concepts relevant to GIS, the majority of which have been published in recent years. This research explores remote sensing applications and technologies in areas such as mapping, hydrological modelling, disaster management, and transportation route planning. Finally, conclusions on contemporary framework methodologies and suggestions for further studies are provided.(original abstract)
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