Zixuan Ni

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.

High-risk landslide extraction from remote sensing imagery is a fundamental task for geological disaster prevention, emergency response, and land-use planning in mountainous regions. Although deep-learning semantic segmentation has substantially advanced landslide detection from optical imagery, existing methods still suffer from three critical limitations: (i) Interferometric Synthetic Aperture Radar (InSAR) deformation data are treated as auxiliary channels and dominated by optical features during fusion; (ii) predicted masks exhibit fragmented boundaries and incomplete delineation due to the absence of deformation continuity constraints reflecting the physical coherence of slope movements; and (iii) heavy Transformer backbones hinder practical deployment over large areas. To address these issues, we propose a Deformation-Integrity-Guided Symmetric Fusion Network (DIGSFNet) for high-risk landslide extraction from InSAR and optical imagery. The framework consists of three components: a Symmetric Deformation-Aware Encoder (SDAE) that treats InSAR and optical modalities as equal information sources through modality-aware adapters and dynamic sparse cross-modal fusion; a Deformation Integrity Prior Decoder (DIPD) that imposes deformation continuity and boundary-gradient consistency as physical priors to enforce mask completeness and boundary accuracy; and a Lightweight Deployable Student Network (LDSN) obtained via cross-modal knowledge distillation and INT8 quantization for efficient inference. Experiments on the Nanning High-Hazard Landslide Segmentation (Nanning-HHLS) dataset and the public HAEFNet benchmark covering the Qinghai–Tibet–Sichuan landslide-prone regions show that the full DIGSFNet achieves state-of-the-art extraction accuracy, reaching 83.57% and 78.92% mIoU on the two datasets and surpassing the strongest competing method by 2.63 and 3.68 percentage points with a Recall of 91.48% on Nanning-HHLS, while its distilled lightweight student retains 79.24% mIoU at 218 frames per second after INT8 quantization, enabling efficient large-area operational deployment.

Cardiovascular diseases (CVDs) remain a major cause of mortality worldwide, and electrocardiograms (ECGs) are an important non-invasive tool for diagnosis. However, many ECG records are still stored as scanned paper images containing grid interference, noise, and variable image quality, which complicates automated analysis. This study presents a framework for four-class ECG image classification directly from paper-based records, covering Normal, Myocardial Infarction (MI), Abnormal Heartbeat (HB), and Post-Myocardial Infarction (PMI). The proposed method combines a targeted preprocessing pipeline with a CBAM-enhanced ResNet-18 architecture. The preprocessing stages are designed to suppress non-diagnostic background interference while preserving clinically relevant waveform morphology, and the attention mechanism refines feature learning in both channel and spatial dimensions. Under a unified training strategy, the proposed model achieved a test accuracy of 0.9674 and a macro-F1 score of 0.9702 on the independent test set. In direct comparison under the same evaluation protocol, the custom CNN reference model achieved 0.9022 test accuracy and 0.8950 macro-F1, indicating clear gains from the proposed architecture. These findings suggest that the combination of tailored preprocessing and attention-enhanced residual learning is effective for paper-based ECG image classification and may provide useful support for automated analysis of legacy ECG archives and resource-limited clinical settings.

Landslide hazard assessment increasingly demands the joint analysis of heterogeneous remote sensing data; however, automating this process remains difficult due to the pronounced resolution and texture discrepancies existing between satellite and aerial sensors. To address these limitations, this study proposes a robust segmentation framework capable of extracting sensor-robust representations. The framework leverages a DINOv3 transformer encoder and exploits representations from multiple transformer layers to capture complementary visual information, ranging from fine-grained surface textures to global semantic contexts, overcoming the receptive field constraints of conventional CNNs. Experiments on the Longxi satellite dataset achieve a Dice coefficient of 0.96 and an IoU of 0.938, and experiments on the Longxi UAV dataset achieve a Dice coefficient of 0.965 and an IoU of 0.941. These results show consistent segmentation performance on both the Longxi satellite and UAV datasets, despite differences in spatial resolution and surface appearance between acquisition platforms.