Modeling algorithm on image segmentation and reconstruction

To solve the problem on storage and retrieval challenges associated with big data of map, this paper presents an image segmentation- reconstruction algorithm. This algorithm consists of four core steps, i.e., Firstly, it incorporates a semantic association mechanism for image segmentation and develops the corresponding segmentation method. Secondly, it determines the maximum remaining storage capacity of each server unit and performs a matching operation with a preset threshold. Thirdly, it establishes a topological mapping to realize the mapping transformation from the segmented original map to the map to be displayed. Finally, the map is reconstructed by using the location information recorded during the storage of features by image segmentation and mapping relationships. Experimental results show that the accuracy of the proposed reconstruction algorithm can reach 94.35%. To compared with existing image reconstruction algorithms, it not only achieves higher accuracy, faster speed, and stronger anti-interference ability, but also has lower information loss. These advantages of this proposed algorithm can offer an efficient framework for managing large-scale map datasets, thus addressing some critical challenges such as high computational overhead, information loss, and system unreliability in geospatial big data applications.