下行瓶颈促使 onboard 系统优先处理危险而不传输原始像素。我们研究一种严格设置，地面站仅上传紧凑嵌入和元数据，onboard 系统通过向量搜索筛选新捕获。我们问这种仅嵌入式流程在显式远程传感位移下是否有效：跨时间（事件前后）、跨事件/地点（不同灾害）、跨站点云（15个地理站点）和跨城市AOI验证（建筑物）。使用OlmoEarth嵌入在扩展的公共多任务基准（27个Sentinel-2 L2A场景，15个云站点，5个SpaceNet-2 AOI；10个种子）上，发现所有有效方法依赖相同上传嵌入，但最优决策头任务依赖：kNN检索在云分类中显著优于均值（0.92 vs. 均值0.91；p<0.01，Wilcoxon），而均值主导时间变化检测（0.85 vs. 检索0.48；p<0.01）。这些结果表明，仅嵌入式上行是关键，一旦嵌入在 onboard，系统可在不增加上行成本下选择最佳头，每查询所有 telemetry 低于1KB。
英文摘要：
Downlink bottlenecks motivate onboard systems that prioritize hazards without transmitting raw pixels. We study a strict setting where a ground station uplinks only compact embeddings plus metadata, and an onboard system performs vector search to triage new captures. We ask whether this embedding-only pipeline remains useful under explicit remote-sensing shift: cross-time (pre/post-event), cross-event/location (different disasters), cross-site cloud (15 geographic sites), and cross-city AOI holdout (buildings). Using OlmoEarth embeddings on a scaled public multi-task benchmark (27 Sentinel-2 L2A scenes, 15 cloud sites, 5 SpaceNet-2 AOIs; 10 seeds), we find that all effective methods rely on the same uplinked embeddings, but the optimal decision head is task-dependent: kNN retrieval is significantly superior for cloud classification (0.92 vs. centroid 0.91; p<0.01, Wilcoxon), while class centroids dominate temporal change detection (0.85 vs. retrieval 0.48; p<0.01). These results show that embedding-only uplink is the key enabler--once embeddings are onboard, the system can select the best head per task at no additional uplink cost, with all telemetry under 1 KB per query.
GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation
College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University（南京林业大学信息科学技术学院 & 人工智能学院）；College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications（南京邮电大学通信与信息工程学院）
Real-time remote sensing applications like search and rescue missions, military target detection, environmental monitoring, hazard prevention and other time-critical applications require onboard real time processing capabilities or autonomous decision making. Some unmanned remote systems like satellites are physically remote from their operators, and all control of the spacecraft and data returned by the spacecraft must be transmitted over a wireless radio link. This link may not be available for extended periods when the satellite is out of line of sight of its ground station. Therefore, ligh…
Compressed Sensing (CS) is suitable for remote acquisition of hyperspectral images for earth observation, since it could exploit the strong spatial and spectral correlations, llowing to simplify the architecture of the onboard sensors. Solutions proposed so far tend to decouple spatial and spectral dimensions to reduce the complexity of the reconstruction, not taking into account that onboard sensors progressively acquire spectral rows rather than acquiring spectral channels. For this reason, we propose a novel progressive CS architecture based on separate sensing of spectral rows and joint re…
Research Institute on Embedded Electronic Systems (IRSEEM)（嵌入式电子系统研究所（IRSEEM））；ESIGELEC School of Engineering（ESIGELEC高等工程学院）；University of the Basque Country（巴斯克大学）；IKERBASQUE Basque Foundation for Science（IKERBASQUE巴斯克科学基金会）
In this paper, we propose a design for novel and experimental cloud computing systems. The proposed system aims at enhancing computational, communicational and annalistic capabilities of road navigation services by merging several independent technologies, namely vision-based embedded navigation systems, prominent Cloud Computing Systems (CCSs) and Vehicular Ad-hoc NETwork (VANET). This work presents our initial investigations by describing the design of a global generic system. The designed system has been experimented with various scenarios of video-based road services. Moreover, the associa…
Text-RSIR: A Text-Guided Framework for Efficient Remote Sensing Image Transmission and Reconstruction
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）科学与工程学院）；Geosciences and Engineering, Southwest Jiaotong University（西南交通大学地球科学与工程学院）；Institute of Space and Earth Information Science and the Department of Geography and Resource Management, The Chinese University of Hong Kong（香港中文大学空间与地球信息科学研究所及地理与资源管理系）
Fluxamba: Topology-Aware Anisotropic State Space Models for Geological Lineament Segmentation in Multi-Source Remote Sensing
Technology and Engineering Center for Space Utilization, CAS, China（中国空间利用技术与工程中心，中国科学院）；University of Chinese Academy of Sciences, China（中国科学院大学）；Macau University of Science and Technology, China（澳门科学技术大学）；University of Peshawar, Pakistan（巴基斯坦普什塔大学）
