Research on geospatial technology optimization based on GeoAI multi-objective optimization

This research focuses on the key technologies of network-based collaboration for Geospatial Artificial Intelligence (GeoAI) services. This paper proposes a geospatial technology model based on GeoAI multi-objective optimization to address the challenges of multi-source heterogeneous models and services in collecting, processing, and analyzing geospatial coverage information. This technology constructs geospatial coverage processing services through programmatic encapsulation and model service methods. At the same time, a service class publishing method based on OGC standards was designed. Secondly, this article adopts a capacity modeling approach to cover and transfer geographic spatial coverage models, solving the problems of model utilization and massive data transmission. Mapping network processing services to REST through logical design, providing support for heterogeneous style geographic coverage processing service interactions for sharing and utilization. A geographic spatial prototype system was designed in the study, and the effectiveness of the proposed method was verified through experiments. The development of this study is of great significance for promoting the mutual collaboration of multi-source heterogeneous models and achieving effective utilization and sharing of geographic spatial resources.