Optimization of Geographic Information System Data Processing by Integrating Deep Learning Algorithms

Traditional GIS (Geographic Information System) analysis relies on rule-driven spatial statistics, which makes it difficult to cope with complex pattern recognition and dynamic monitoring, especially when processing large-scale high-dimensional data. To this end, this paper adopts a fusion deep learning algorithm to improve the accuracy and efficiency of GIS in spatial data processing. This method first preprocesses the geospatial data and enhances the diversity through data enhancement; CNN is used to extract the spatial features of remote sensing images to identify targets such as buildings, roads, and vegetation; LSTM is applied to model time series such as meteorology and traffic to predict trends; multi-source data is fused to optimize the processing flow based on joint learning, and spatial anomalies and sudden changes are detected through GAN. Experiments show that the accuracy and recall rates are both higher than 0.8, and the inference time for each image is only 0.02 seconds.