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For a long time, due to the high heterogeneity in structure and semanticsamong various spatiotemporal modal data, the joint interpretation of multimodalspatiotemporal data has been an extremely challenging problem. The primarychallenge resides in striking a trade-off between the cohesion and autonomy ofdiverse modalities, and this trade-off exhibits a progressively nonlinearnature as the number of modalities expands. We introduce the Language asReference Framework (LaRF), a fundamental principle for constructing amultimodal unified model, aiming to strike a trade-off between the cohesion andautonomy among different modalities. We propose a multimodal spatiotemporalgeneral artificial intelligence model, called AllSpark. Our model integratesthirteen different modalities into a unified framework, including 1D (text,code), 2D (RGB, infrared, SAR, multispectral, hyperspectral, tables, graphs,trajectory, oblique photography), and 3D (point clouds, videos) modalities. Toachieve modal cohesion, AllSpark uniformly maps diverse modal features to thelanguage modality. In addition, we design modality-specific prompts to guidemulti-modal large language models in accurately perceiving multimodal data. Tomaintain modality autonomy, AllSpark introduces modality-specific encoders toextract the tokens of various spatiotemporal modalities. And modal bridge isemployed to achieve dimensional projection from each modality to the languagemodality. Finally, observing a gap between the model's interpretation anddownstream tasks, we designed task heads to enhance the model's generalizationcapability on specific downstream tasks. Experiments indicate that AllSparkachieves competitive accuracy in modalities such as RGB and trajectory comparedto state-of-the-art models.

AllSpark: a multimodal spatiotemporal general model