Earth-OneVision is a unified 2B-parameter RS-MLLM supporting six modalities and nine tasks via FGVLA, SLIS, and PCMA mechanisms plus a 34M QA-pair dataset, reporting competitive or superior benchmark results versus larger models.
The qxs-saropt dataset for deep learning in sar-optical data fusion. arxiv 2021
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MetaEarth-MM unifies multi-modal remote sensing image generation and any-to-any translation across five modalities via scene-centered joint modeling on the new EarthMM dataset.
SMART-Ship introduces a new synchronized multi-modal remote sensing dataset with fine-grained annotations for berthed ships and benchmarks for five interpretation tasks.
CoDe-MAE achieves effective joint pretraining of heterogeneous high-resolution optical and SAR images via optical-anchored knowledge distillation, conditioned contrastive learning, and cross-modal degraded reconstruction, yielding better data efficiency and downstream performance than larger-scale基础
citing papers explorer
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Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks
Earth-OneVision is a unified 2B-parameter RS-MLLM supporting six modalities and nine tasks via FGVLA, SLIS, and PCMA mechanisms plus a 34M QA-pair dataset, reporting competitive or superior benchmark results versus larger models.
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MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling
MetaEarth-MM unifies multi-modal remote sensing image generation and any-to-any translation across five modalities via scene-centered joint modeling on the new EarthMM dataset.
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SMART-Ship: A Comprehensive Synchronized Multi-modal Aligned Remote Sensing Targets Dataset and Benchmark for Berthed Ships Analysis
SMART-Ship introduces a new synchronized multi-modal remote sensing dataset with fine-grained annotations for berthed ships and benchmarks for five interpretation tasks.
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Better with Less: Tackling Heterogeneous Multi-Modal Image Joint Pretraining via Conditioned and Degraded Masked Autoencoder
CoDe-MAE achieves effective joint pretraining of heterogeneous high-resolution optical and SAR images via optical-anchored knowledge distillation, conditioned contrastive learning, and cross-modal degraded reconstruction, yielding better data efficiency and downstream performance than larger-scale基础