GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation

The deployment of large-scale foundation models like Segment Anything Model (SAM) on resource-constrained Earth observation platforms is hindered by prohibitive computational costs and the domain shift between natural and remote sensing imagery. To address these challenges, we propose \textit{Geo}spatial \textit{S}egment \textit{A}nything \textit{M}odel-Lite (GeoSAM-Lite), a lightweight, prompt-free segmentation framework designed for efficient onboard remote sensing segmentation. GeoSAM-Lite incorporates two core innovations: (1) Geospatial-Domain Initialization (Geo-Init), a domain-aware pre-training strategy that distills geospatial priors from a specialized teacher to bridge the domain gap; and (2) Feature Fusion Layers (FFL), which recalibrate spatial features and restore high-frequency boundary cues to overcome the capacity bottlenecks of lightweight backbones. Experiments across representative datasets, with a primary focus on cloud scenarios to evaluate performance under extreme scale variations and complex boundaries, demonstrate that GeoSAM-Lite achieves competitive accuracy while reducing parameters by 92.8\% compared to the heavyweight RSAM-Seg. By establishing a superior Pareto frontier between efficiency and fidelity, GeoSAM-Lite offers a practical solution for real-time segmentation on edge devices.

LiBrA-Net: Lie-Algebraic Bilateral Affine Fields for Real-Time 4K Video Dehazing

Currently, there is a gap in the field of ultra-high-definition (UHD) video dehazing due to the lack of a benchmark for evaluation. Furthermore, existing video dehazing methods cannot run on consumer-grade GPUs when processing continuous UHD sequences of 3--5 frames at a time. In this paper, we address both issues with a new benchmark and an efficient method. Our key observation is that atmospheric dehazing reduces to a per-pixel affine transform governed by the low-frequency depth field, which can be compactly encoded in bilateral grids whose prediction cost is decoupled from the output resolution. Building on this, we propose LiBrA-Net, which factorizes the spatiotemporal affine field into a spatial--color and a temporal bilateral sub-grid predicted at a fixed low resolution, fuses their coefficients in the $\mathfrak{gl}(3)$ Lie algebra under group-theoretic regularization, maps the result to invertible GL(3) transforms via a Cayley parameterization, and restores high-frequency detail through a lightweight input-guided branch. We further release UHV-4K, the first paired 4K video dehazing benchmark with depth, transmission, and optical-flow annotations on every frame. Across UHV-4K, REVIDE, and HazeWorld, LiBrA-Net sets a new state of the art among compared video dehazing methods while running native 4K at 25 FPS on a single GPU with only 6.12 M parameters. Code and data are available at https://anonymous.4open.science/r/LiBrA-Net-42B8.