Vision Foundation Models (VFMs), while powerful, often struggle in Remote Sensing (RS) segmentation tasks when combined with existing Parameter-Efficient Fine-Tuning (PEFT) methods. We observe that this limitation primarily arises from their inability to effectively handle the pervasive artifacts in RS imagery. To address this, we introduce Earth-Adapter, the first PEFT method specifically designed for RS artifact mitigation. Earth-Adapter introduces a novel Frequency-Guided Mixture of Adapters (MoA) approach, structured around a ''divide and conquer" strategy. It first utilizes Discrete Fourier Transformation (DFT) to "divide" features into distinct frequency components, thereby effectively isolating artifact-related information from semantic signals. Subsequently, to ''conquer" these artifact, MoA independently optimizes features within different subspaces and dynamically assigns weights via a router to aggregate the refined representations. This enables adaptive refinement of the VFM’s representation space to mitigate the impact of artifacts. This simple yet highly effective PEFT method demonstrably mitigates artifacts and significantly enhances VFMs performance on RS segmentation tasks. Extensive experiments demonstrate Earth-Adapter's effectiveness on in-domain semantic segmentation (SS), as well as Domain Adaptive (DA) and Domain Generalized (DG) semantic segmentation tasks. Compared with the baseline Rein, Earth-Adapter significantly improves mIoU by 1.2% in SS, 9.0% in DA, and 3.1% in DG benchmarks. Our code and weights will be released soon.

Unmanned Aerial Vehicles (UAVs) are increasingly important in dynamic environments such as logistics transportation and disaster response. However, current tasks often rely on human operators to monitor aerial videos and make operational decisions. This mode of human-machine collaboration suffers from significant limitations in efficiency and adaptability. In this paper, we present AirVista-II—an end-to-end agentic system for embodied UAVs, designed to enable general-purpose semantic understanding and reasoning in dynamic scenes. The system integrates agent-based task identification and scheduling, multimodal perception mechanisms, and differentiated keyframe extraction strategies tailored for various temporal scenarios, enabling the efficient capture of critical scene information. Experimental results demonstrate that the proposed system achieves high-quality semantic understanding across diverse UAV-based dynamic scenarios under a zero-shot setting.

Academic journals serve as pivotal bridges for knowledge dissemination and technology innovation, playing a crucial role in promoting scientific research, industrial progress, and societal development. However, traditional models of journal management and operation, hindered by prolonged peer review processes, overarching publication inefficiencies, and surging page expenditures, are increasingly powerless to address challenges posed by ever-faster knowledge updates, broad information dissemination, and interdisciplinary scholarly work. This has led to an urgent need for smart organizations and intelligent operations of journals. In view of this, the paper identifies the primary issues in current journal management and proposes the concept of smart journals based on TRUE Autonomous Organizations and Operations (TRUE DAO or TAO). This paper introduces the foundational architecture of smart journals and proposes incentive mechanisms for a more dynamic publishing ecosystem. Moreover, a simulation experiment is designed to evaluate an adaptive incentive mechanism, demonstrating significant improvements in review quality and accuracy through personalized incentive allocation strategies. Smart journals not only enable digital transformation but also foster profound innovation and long-term sustainability in publishing.