Memory-Aware Remote Sensing Video Change Detection with Lightweight Vision Foundation Models
Keywords:
remote sensing, video change detection, vision foundation models, memory-aware systems, lightweight models, model deployment, AI governanceAbstract
Remote sensing video change detection is a critical task for environmental monitoring, urban planning, and disaster response, yet it imposes severe computational and memory demands due to high-resolution, temporally dense image sequences. The recent emergence of vision foundation models promises unprecedented segmentation and scene understanding capabilities, but their enormous parameter counts and reliance on attention mechanisms that scale quadratically with sequence length make direct deployment infeasible for resource-constrained satellite and airborne platforms. This paper investigates a systems-oriented framework that couples memory-aware sequence processing with lightweight adaptations of vision foundation models to enable sustainable, long-sequence video change detection. We discuss the architectural trade-offs between detection granularity, temporal coherence, memory footprint, and energy consumption, and we analyze how external memory banks, token compression, and hierarchical temporal aggregation can transform a heavy foundation model into a deployable edge-compatible system. The study further examines infrastructure-level considerations, including federated learning across distributed sensor swarms, on-satellite preprocessing, and carbon-aware scheduling. Governance dimensions such as fairness in land-cover change analysis, transparency of automated detection pipelines, and the geopolitical implications of ubiquitous overhead monitoring are foregrounded as integral to system design rather than afterthoughts. Throughout, we emphasize that the co-design of lightweight model architectures, memory management strategies, and policy-aware deployment workflows is essential for realizing the societal and scientific benefits of remote sensing video change detection at scale.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
