[2503.12843] Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data

View the PDF file for the paper entitled “The Development Foundation Form” for multimedia spatial geographic data and hyperactivity, by Hazhe Si and 4 other authors

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a summary:Spatial geographic bittime data, such as those collected by satellite -based imaging systems at different times and spectral ranges, contains huge potential to enable a wide range of high -influential applications. These capabilities stem from the rich information that is spatched and anxiously through multiple channels and sensors. The last work has adapted the learning methods subject to the current self -supervision of such spatial geographical data. However, they fail to develop developable models, which leads to flexibility and mathematical efficiency when facing an increasing number of channels and folding each. To address these restrictions, we offer a low -efficient spatial vision adapter with three main innovations: 1) Attention block that converges high -dimensional spatial attention through the Kronecker product for the components of spatial and low -dimensional spatial attention; 2) A layer of joining the continuous topical canal that maintains both continuity and physical properties for each gentle spatial correction; And 3) The mask of the visualization field that takes advantage of local spatial dependencies by restricting attention to the adjacent spots. To evaluate the proposed innovations, we build the GFM-Brenced, which acts as a comprehensive standard for such spatial geographical data. We cleared less using a masked automatic framework with integrated location and channels. Experimental results show that our proposed method achieves competitive performance against the latest models of multimedia spatial geographical foundation while outperforming the generalization tasks through the higher mathematical efficiency weapons. The flexibility and framework of our work makes it a promising trend for the tasks of analyzing future spatial geographical data that involve a wide range of methods and channels.