Traditionally, GIS has relied on conventional machine learning tools, employing techniques like image classification and clustering to decipher spatial relationships. Deep learning is a big step forward for GIS systems because it allows them to understand and analyze spatial features on their own, without human intervention. This is particularly evident in situations where understanding spatial patterns is important for identifying complex objects like buildings, roads, or land cover types. A key strength of deep learning lies in its ability to efficiently process extensive sets of labeled data. In the context of GIS, this translates into the training of deep learning models with comprehensive datasets containing spatial information. For example, when classifying land cover types, a deep learning model can be trained using satellite imagery, enabling it to discern the unique features associated with various land cover classes.