A Comprehensive Review of the Recent Advancement in Integrating Deep Learning with Geographic Information Systems

Abstract

The integration of deep learning (DL) techniques with geographical information system (GIS) offers a promising avenue for gaining novel insights into environmental phenomena by using the capabilities of spatial, temporal, and spectral resolutions, as well as data integration. The integration of these two technologies can result in the development of a highly efficient system for assessing environmental conditions by analyzing the interplay between texture, size, pattern, and process. This viewpoint has gained appeal across various academic disciplines. GIS heavily relies on processors, especially for tasks such as 3D computations, map rendering, and route calculation. In contrast, DL has the capability to efficiently analyze vast quantities of data. DL has garnered significant attention in recent times due to its potential for delivering a wide range of promising outcomes. Moreover, there is clear evidence of the increasing utilization of deep learning techniques across various fields, including GIS. The objective of this study is to provide an overview of the application of DL techniques in the field of GIS. This paper presents a concise review of the fundamental DL ideas that are pertinent to GIS, with a focus on the most current research findings. The present study investigates the various uses and technology of remote sensing in diverse domains, including mapping, hydrological modeling, disaster management, and transportation route planning. This study offers insights into contemporary framework approaches and proposes avenues for further research.