Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding

[2604.11415] Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2604.11415 (cs) [Submitted on 13 Apr 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding Authors: Zhenghao Xie , Jing Xiao , Zhenqi Wang , Kexin Ma , Liang Liao , Gui-song Xia , Mi Wang View a PDF of the paper titled Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding, by Zhenghao Xie and 5 other authors View PDF HTML (experimental) Abstract: Remote sensing understanding inherently requires multi-resolution observation, since different targets and application tasks demand different levels of spatial detail. While low-resolution (LR) imagery enables efficient global observation, high-resolution (HR) imagery provides critical local details at a much higher acquisition cost and with limited coverage. This motivates a cross-scale sensing strategy that selectively acquires HR imagery guided by LR-based global perception to improve task performance under constrained cost. Existing HR sampling methods typically make selection decisions from isolated LR patches, thereby ignoring fine-grained intra-patch importance and cross-patch contextual interactions, leading to fragmented feature representation and suboptimal scene reasoning under sparse HR observations. To address this issue, we formulate cross-scale remote sensing understanding as a unified cost-aware problem that couples fine-grained HR sampling with cross-patch representation prediction, enabling more effective task reasoning with fewer HR observations. Furthermore, we present GL-10M, a high- and low-resolution dataset with nearly 100,000 scene pairs and 10 million images for large-scale cross-resolution pretraining. Extensive experiments on recognition and retrieval tasks show that our method consistently achieves a superior performance-cost trade-off. The code is publicly available at this https URL . Comments: 14 pages, 7 figures, and 5 tables. Accepted to ACM MM 2026. Updated to the camera-ready version and added supplementary material. Code: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2604.11415 [cs.CV] (or arXiv:2604.11415v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2604.11415 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhenghao Xie [ view email ] [v1] Mon, 13 Apr 2026 13:00:29 UTC (12,610 KB) [v2] Sat, 15 Aug 2026 07:01:53 UTC (10,266 KB) Full-text links: Access Paper: View a PDF of the paper titled Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding, by Zhenghao Xie and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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