— To visualize high-dimensional geospatial data has achieved much importance in last decades. But to analyze it, the technologies used of machine learning are not so convincing and thus it is high time to switch to a sub-domain of machine learning called deep learning, which has gained popularity because of its accuracy in processing and analyzing high- dimensional data. The convergence of deep learning with geospatial data analytics shall prove to be a boon to those who actually has a need to predict specific outputs over geospatial data. In this paper, we have presented some geospatial data generated using the GIS (Geographic Information Systems) technology and proposed ways to implement deep learning over these data. GIS technology is a mapping technology which signifies high-dimensional geospatial data and our aim is to propose a model where GIS converges with highly efficient deep neural networks such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Network) and LSTM (Long Short- Term Memory Network). We have provided geospatial as well as statistical results in this paper to visualize practically the GIS technology. This paper further provides future scope of the proposed model which shall present the challenges that needs to be tackled in future and its applicability in many relevant domains. Keywords— Geographic Information Systems (GIS); Deep Neural Networks; Convolutional Neural Networks (CNN); Recurrent Neural Network (RNN); Long Short-Term Memory Network (LSTM); Raster Data; Vector Data. I. INTRODUCTION Recent years have experienced the rage of deep learning which is a sub-field of machine learning well-known for the accuracy it provides in tasks of object detection, feature extraction and many more. Deep learning has proven its proficiency in multiple domains such as computer vision, natural language processing, speech recognition are a few areas to mention [1]. GIS technology needs deep learning in order to classify spatial, spectral as well as temporal data from high-dimensional hyperspectral datasets. The usage of hyperspectral images in the domains of mineral detection, urban mapping, environmental management, and many such is indispensable [2]. The advent of GIS technology has led towards the research direction in which deep learning acts as a backend technology for providing predictive power to GIS. Effective feature selection using deep learning in the GIS datasets leads to high-ended performance as well as accuracy in prediction [3]. Last decade led to the advent of a research area for image classification which can solve the problem of classifying satellite imagery [4]. The use of machine learning is outperformed in many domains of computer science by the deep neural networks. The reason being the problem faced in machine learning in order to achieve high accuracy in classifying many unsolved categories. Furthermore, deep neural networks came into existence to solve the challenges faced by machine learning in a proficient manner. The data files called geo-data in case of GIS technology consists many attributes along with east coordinates and north coordinates which make it geospatial. Many applications in the domain of GIS which require deep learning as the key enabler for prediction mechanisms are in action. Few of them are as follows: Estimation of building Energy Use Intensity (EUI) using a GIS based data mining approach proposed by Ma and Cheng [5]. Vector type map generation from aerial imagery datasets using deep neural networks proposed by Sahu and Ohri [6]. Crime prediction is also a field where deep learning and GIS merger can prove its efficiency since till date crime detection mechanisms have been shown still no real-life use cases are available for crime prediction [7, 8, 9, 10]. The applicability of deep learning in context to GIS is large and vivid and we have mentioned only a few. A. Motivations This is high-time where GIS technology must meet the competencies of deep learning. GIS technology deals with geographical data and the advent of spatial data mining led us to the idea of converging deep neural networks with geospatial hyperspectral datasets. Fig. 1. Google Trends data for the research over five years in deep learning and GIS merger. Fig. 1 presents the Google Trends data showing the increasing rage of deep learning in the field of geographic information systems over the past five years in a worldwide basis [11]. B. Objectives The objectives of this paper can be summarized as follows:  Exploring the applicability of deep learning in the field of GIS technology.  Providing a generalized framework over which any GIS application can use deep learning if required.  Providing future research directions for the merger technology of deep learning and GIS.  Citing specified applications as case studies for visualizing the practical competency of the merger technology.  Detailing of the key enablers to realize such a proposed model. II. ENABLING TECHNOLOGIES A. Geographic Information Systems (GIS) GIS may be defined as a special-purpose database where the data points are spatial coordinates. GIS databases work with the aid of geo-