With the development and popularization of remote sensing earth observation technology and the remote sensing satellite system, the problems of insufficient proactiveness, relevance and timeliness of large-scale remote sensing supporting services are increasingly prominent, which seriously restricts the application of remote sensing resources in multi-domain and cross-disciplinary. It is urgent to help terminal users make appropriate decisions according to real-time network environment and domain requirements, and obtain the optimal resources efficiently from the massive remote sensing resources. In this paper, we propose a recommendation algorithm using fusion of attention and multi-perspective (MRS_AMRA). Based on MRS_AMRA, we further implement an active service recommendation model (MRS_ASRM) for massive multi-source remote sensing resources by combining streaming pushing technology. Firstly, we construct value evaluation functions from multi-perspective in terms of remote sensing users, data and services to enable the adaptive provision of remote sensing resources. Then, we define multi-perspective heuristic policies to support resource discovery, and fusion these policies through the attention network, to achieve the accurate pushing of remote sensing resources. Finally, we implement comparative experiments to simulate accurate recommendation scenarios, compared with state-of-the-art algorithms, such as DIN and Geoportal. Furthermore, MRS_AMRA achieves an average improvement of 10.5% in the recommendation accuracy NDCG@K, and in addition, we developed a prototype system to verify the effectiveness and timeliness of MRS_ASRM.

The integration of geospatial-analysis models is crucial for simulating complex geographic processes and phenomena. However, compared to non-geospatial models and traditional geospatial models, geospatial-analysis models face more challenges owing to extensive geographic data processing and complex computations involved. One core issue is how to eliminate model heterogeneity to facilitate model combination and capability integration. In this study, we propose a containerized service-based integration framework named GeoCSIF, specifically designed for heterogeneous-geospatial-analysis models. Firstly, by designing the model-servicized structure, we shield the heterogeneity of model structures so that different types of geospatial-analysis models can be effectively described and integrated based on standardized constraints. Then, to tackle the heterogeneity in model dependencies, we devise a prioritization-based orchestration method, facilitating optimized combinations of large-scale geospatial-analysis models. Lastly, considering the heterogeneity in execution modes, we design a heuristic scheduling method that establishes optimal mappings between models and underlying computational resources, enhancing both model stability and service performance. To validate the effectiveness and progressiveness of GeoCSIF, a prototype system was developed, and its integration process for flood disaster models was compared with mainstream methods. Experimental results indicate that GeoCSIF possesses superior performance in model management and service efficiency.

With the rapid development of Earth observation and information technology, people are increasingly able to access geospatial models. Geospatial models, based on principles of geography, utilize mathematical, statistical, as well as computer science methods to interpret and predict geographic phenomena. These models can be applied in the fields such as urban planning, environmental protection, traffic management to help decision-makers solve geography-related problems. However, integrating different geospatial models to collaboratively solve complex geographic problems still faces significant obstacles due to heterogeneity in model structure, dependencies, and running modes. In this study, we propose a containerized service-based integration framework for heterogeneous geospatial models (GeoCSIF). GeoCSIF consists of three main components: (1) Model encapsulation. It breaks down complicated geospatial models into independently manageable model units, and builds as unified service packages with a templated constraint method. (2) Model orchestration. It achieves an optimal combination of large-scale models with complex dependencies using a prioritization-based orchestration method. (3) Model publication. It incorporates heuristics into the model scheduling process, which can provide adaptive deployment for different model runs. Finally, a prototype system was developed to validate the effectiveness and progressiveness of GeoCSIF by the integrating process of heterogeneous flood disaster models.

Land use classification based on multi-modal data fusion has gained significant attention due to its potential to capture the complex characteristics of urban environments. However, effectively extracting and integrating discriminative features derived from heterogeneous geospatial data remain challenging. This study proposes an ensemble learning framework for land use classification by fusing cross-modal deep features from both physical and socioeconomic perspectives. Specifically, the framework utilizes the Masked Autoencoder (MAE) to extract global spatial dependencies from remote sensing imagery and applies long short-term memory (LSTM) networks to model spatial distribution patterns of points of interest (POIs) based on type co-occurrence. Furthermore, we employ inter-modal contrastive learning to enhance the representation of physical and socioeconomic features. To verify the superiority of the ensemble learning framework, we apply it to map the land use distribution of Bejing. By coupling various physical and socioeconomic features, the framework achieves an average accuracy of 84.33 %, surpassing several comparative baseline methods. Furthermore, the framework demonstrates comparable performance when applied to a Shenzhen dataset, confirming its robustness and generalizability. The findings highlight the importance of fully extracting and effectively integrating multi-source deep features in land use classification, providing a robust solution for urban planning and sustainable development.