Researchers have developed a new cost-aware strategy for remote sensing understanding that selectively acquires high-resolution (HR) imagery guided by low-resolution (LR) perception. This approach aims to improve task performance under constrained costs by addressing limitations in existing HR sampling methods, which often ignore fine-grained importance and cross-patch interactions. The proposed method couples fine-grained HR sampling with cross-patch representation prediction for more effective reasoning with fewer HR observations. Additionally, a new dataset called GL-10M, comprising nearly 100,000 scene pairs and 10 million images, has been introduced for large-scale cross-resolution pretraining. AI
IMPACT This research could lead to more efficient and effective remote sensing analysis by optimizing the use of high-resolution imagery.
RANK_REASON This is a research paper detailing a new method and dataset for remote sensing. [lever_c_demoted from research: ic=1 ai=1.0]
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