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.

The global and regional systems for managing water resources are thought to be significantly impacted by climate change. Due to the fact that poor nations are most impacted, it has grown to be a significant worldwide problem. Natural disasters like aridity, droughts, floods, cyclones, and severe rainfall are all likely to have an impact on humans. Water scarcity can be brought on by climate change in some countries through persistent droughts. The rising population will result in a greater demand for water. Cities are becoming more vulnerable to water-related problems as a result of increased urbanization, changing rainfall patterns, and industrialization. The supply and demand of water can be balanced through the implementation of sustainable practices. In the event of future climate change, rainwater harvesting systems offer an alternative source of water and specific adaptation strategies for coping with water scarcity. The adoption of rainwater harvesting systems is discussed in this article as a strategy for climate change.