Visual geo-localization is a predominant approach for unmanned aerial vehicles (UAVs) operating in Global Navigation Satellite System (GNSS)-denied environments, typically achieved by matching UAV-captured visible optical images with satellite base maps. However, under low-light conditions, visible cameras struggle to capture distinct features. While infrared sensors can capture clear features in such scenarios, the significant modality gap between thermal infrared images and optical satellite base maps makes accurate matching highly challenging. In this paper, we propose a novel cross-modal super-resolution matching and geo-localization method constrained by geographic consistency. First, a geographic consistency normalization module is introduced to narrow the modality gap between satellite optical images and thermal infrared images, thereby enhancing cross-modal matchability. Subsequently, a thermal infrared super-resolution enhancement module is employed to improve the spatial resolution and detail representation of the images, effectively increasing feature discriminability in low-texture regions. Finally, an end-to-end dense matching module is utilized to strengthen the stability of cross-modal correspondence estimation, ultimately improving geo-localization accuracy in low-light environments. Extensive experiments conducted on both a self-constructed network dataset and a real-world flight dataset demonstrate that the proposed method outperforms current competitive approaches. The proposed framework is not a simple combination of existing enhancement and matching modules, but a task-driven design that jointly addresses cross-modal discrepancy, low-resolution thermal imagery, and robust correspondence estimation. Experiments on self-constructed and public datasets demonstrate its robustness and superiority, achieving average geo-localization errors of 1.31 m and 8.04 m, respectively.

Infrared-triggered cameras serve as essential tools for wildlife resource surveys, allowing camera traps to capture wildlife activity while minimizing the impact on the ecosystem. Traditional monitoring methods heavily rely on human resources for visual discrimination, which is inefficient and susceptible to environmental influences. Therefore, this paper focuses on Shennongjia National Park as a case study to address the limitations of traditional approaches. We constructed a Shennongjia wildlife object detection dataset using video data from the fixed-erected infrared cameras and proposed a supervised learning-based automatic monitoring method for golden monkeys, aiming to achieve intelligent target detection. With these processing results, we were able to capture golden monkeys' tracks and conduct statistical analysis of their life range and distribution characteristics. Through the integration of sensors and deep learning techniques, we developed a golden monkeys detection monitoring system to visualize the monitoring results and assess the spatial and temporal distribution characteristics of golden monkeys’ activities.

Large-area remote-sensing data provide essential pre-mission information for unmanned ground vehicles, but their spatial support and temporal latency may obscure local terrain changes. A remaining challenge is to translate heterogeneous regional evidence and recent local observations into a consistent, updateable, and planner-ready map. This study presents a satellite–unmanned aerial vehicle (UAV) workflow for constructing and incrementally maintaining an off-road traversability map for mission-level global planning. A common H3 index organizes satellite imagery, terrain, soil, road evidence, and local UAV semantic observations while retaining their native spatial support and provenance. The map separates environmental-prior, semantic, and traversability-cost layers to support interpretable fusion and independent updating. A confidence-hierarchical conflict resolution mechanism resolves inconsistencies in the regional prior, while an observer-agnostic interface projects UAV semantic observations onto local map cells. RGB imagery is used by the primary UAV observer, and digital surface model (DSM) is evaluated as an optional semantic-observation modality. Evaluation included a manually reviewed regional benchmark, a unified buffered spatial holdout, cell-level update assessment, and 40 fixed replanning tasks. Conflict resolution reduced high-risk omissions. RGB-only SegFormer-B2 achieved the highest semantic accuracy with moderate computational complexity. UAV override achieved a cell-level F1 score of 96.96% and limited the false-positive accumulation associated with conservative union. Replanning further revealed a trade-off between hazardous-cell avoidance and search-graph connectivity. The proposed workflow provides a maintainable interface between multi-source remote sensing and global UGV planning rather than a replacement for onboard perception, local obstacle avoidance, or vehicle control.