Geomatics and Environmental Engineering, vol. 16, no. 2

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Vol. 16 No. 2 (2022): Geomatics and Environmental Engineering

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Submitted
Sep 13, 2021
Accepted
Dec 22, 2021
Published
Feb 9, 2022
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This work is licensed under a Creative Commons Attribution 4.0 International License.

A Brief Review of Recent Developments in the Integration of Deep Learning with GIS

https://doi.org/10.7494/geom.2022.16.2.21
Shyama Mohan
Research Scholar, Centre for Water Resources, Institute of Science and Technology, Jawaharlal Nehru Technological University Hyderabad, Kukatpally, Hyderabad, India
https://orcid.org/0000-0002-7935-448X
Giridhar M.V.S.S
Centre for Water Resources, Institute of Science and Technology, Jawaharlal Nehru Technological University Hyderabad, Kukatpally, Hyderabad, India
https://orcid.org/0000-0001-9029-9621

Corresponding Author(s) : Shyama Mohan

shyama7789@gmail.com

Geomatics and Environmental Engineering, Vol. 16 No. 2 (2022): Geomatics and Environmental Engineering

Abstract

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.

Keywords

deep learning GIS integration classification remote sensing
Mohan, S., & M.V.S.S, G. (2022). A Brief Review of Recent Developments in the Integration of Deep Learning with GIS. Geomatics and Environmental Engineering, 16(2), 21–38. https://doi.org/10.7494/geom.2022.16.2.21
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