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

• URL: http://arxiv.org/abs/2604.11415v1
• Date: Mon, 13 Apr 2026 13:00:29 GMT
• Title: Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding
• Abstract summary: High-resolution (HR) imagery provides critical local details at much higher acquisition cost and limited coverage.<n>This motivates a cross-scale sensing strategy that selectively acquires HR imagery from LR-based global perception.<n>We present GL-10M, a large-scale benchmark of 10 million spatially aligned multi-resolution images.
• Score: 49.97682794425118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 much higher acquisition cost and limited coverage. This motivates a cross-scale sensing strategy that selectively acquires HR imagery from LR-based global perception to improve task performance under constrained cost. Existing methods for HR sampling methods typically make selection decisions from isolated LR patches, which ignore 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 large-scale benchmark of 10 million spatially aligned multi-resolution images, enabling systematic evaluation of budget-constrained cross-scale reasoning in remote sensing. Extensive experiments on recognition and retrieval tasks show that our method consistently achieves a superior performance-cost trade-off.

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