Search Results (563)

Executing multi-UAV cooperative pursuit in complex public domains requires balancing interception efficiency with flight safety under strict micro-platform constraints. Existing planners often struggle with high computational overhead or lack kinodynamic adaptability in heterogeneous environments. To address this, we propose AC2F, a lightweight Adaptive Coarse-to-Fine hybrid framework featuring a bidirectional state-switching mechanism. The framework utilizes the Apollonius circle for efficient global guidance during the coarse phase, dynamically transitioning to a Dynamic Window Approach (DWA) upon detecting path oscillations or entering terminal capture zones. To ensure robustness, a dual-layer parameter paradigm integrates offline Bayesian optimization for globally optimal baselines with online real-time weight adaptation based on target distance. Extensive simulations show that AC2F effectively escapes local minima, such as urban-style U-shaped traps. Real-world suburban validation confirms an 86% capture rate with minimal computational overhead, demonstrating AC2F’s suitability for public domain protection and civil security. Full article

Urban low-altitude unmanned aerial vehicle (UAV) logistics networks face critical operational bottlenecks due to complex three-dimensional spatial blockages, continuous communication diffraction, and severe vulnerability to information-layer threats such as Automatic Dependent Surveillance—Broadcast (ADS-B) signal anomalies. To address these interconnected challenges, this paper proposes an event-driven, cloud–edge collaborative digital twin framework to guarantee continuous multi-link communication and flight safety. The architecture operates through a dual-tier “Teacher–Student” paradigm. Under secure conditions, a cloud digital twin acts as a high-capacity “Teacher,” employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to partition heterogeneous user topologies. It then utilizes an energy-guided stochastic diffusion sampling (EGSDS) method to refine initial macroscopic routing, generating precise, outage-free global trajectories by systematically minimizing non-line-of-sight (NLoS) observation penalties and kinematic regularization costs. To counteract signal anomalies, a distributed Time Difference of Arrival (TDOA) anchor network continuously validates UAV coordinate integrity. If a threshold is breached, control authority is instantly transferred to the UAV’s edge digital twin. This resource-constrained edge tier relies on a localized “Student” network trained via progressive distillation. By compressing the computationally heavy iterative diffusion process into a rapid one-step inference model, the UAV autonomously generates a secure, short-range emergency path that strictly adheres to minimum communication thresholds. Once interference clears, the cloud seamlessly regains control to complete the logistics mission. Experimental results demonstrate that the proposed scheme significantly outperforms conventional heuristic routing methods in cloud-based scenarios. Furthermore, the edge-based distillation mechanism substantially improves the overall trajectory survival rate under signal anomalies, ensuring resilient and continuous logistics operations. Full article

Reliable spatial georeferencing of mobile mapping platforms is a fundamental prerequisite for high-fidelity urban remote sensing products such as 3D point clouds and digital twins. However, in deep urban canyons, severe signal occlusion and multipath effects reduce visible GNSS satellites, causing ambiguity resolution (AR) failure and degraded observation geometry for UGV-borne systems. Conventional Vehicle-to-Vehicle (V2V) cooperation offers limited improvement due to symmetric ground-level occlusion. To overcome this, we propose an asymmetric GNSS/UWB fusion method that introduces Unmanned Aerial Vehicles (UAVs) as high-altitude dynamic spatial anchors to reconstruct the 3D observation geometry. Two contributions are presented: (i) an asymmetric heterogeneous stochastic model coupling carrier-to-noise ratio (C/N0) and elevation angle to handle the quality disparity between air and ground sensor links, preventing multipath contamination of high-fidelity UAV observations; and (ii) a dynamic baseline constrained least-squares algorithm integrating Ultra-Wideband (UWB) ranging to stabilize GNSS positioning under high-dynamic relative motion. Validated through high-fidelity simulations and field experiments, the method achieves a 98.2% AR success rate and sub-decimeter 3D accuracy under extreme occlusion (≤3 visible satellites), while urban-canyon tests demonstrate 100% positioning availability across all evaluated epochs and reduce the 95th-percentile 3D error from 7.25 m to 0.19 m under the tested single-UAV/single-UGV configuration. The framework supports smart city modeling, 3D reconstruction, and infrastructure monitoring. Full article

In the context of dynamic environmental changes, accurate geospatial information is fundamental for evidence-based decision-making in land-use planning. As urban areas undergo rapid structural transformations, characterizing their spatial morphology becomes essential for assessing ecosystem conditions and identifying pressure points within the urban–rural gradient. Drawing on the indicators for ecosystem condition and pressure recommended by the Mapping and Assessment of Ecosystem Services (MAES) framework, reflecting their trends, this study presents a methodology for comprehensive geospatial mapping of urban and peri-urban morphology, using the Functional Urban Area (FUA) of Burgas, Bulgaria, as a case study. The approach enables multi-scale spatial analysis (regional and local), integrates the structure and functions of urban ecosystems, and reveals the spatial heterogeneity of complex socio-economic systems. At the regional level, ecosystems within the FUA were identified using the national land-use/land-cover database. At the local level, within the city of Burgas, urban morphology was classified by combining building and land-cover types into 14 distinct urban morphological zones (local climate zones—LCZs) using high-resolution unmanned aerial vehicle (UAV)-based orthophotos. This precise spatial data allowed for a detailed assessment of the balance between pervious and impervious surfaces within each LCZ. By integrating Google Earth Engine (GEE) data, the appropriate conditions and pressure indicators in the case study are assessed. Regional ecosystem pressure is effectively captured through the spatial distribution of the Final Pressure Index (IPr). Concurrently, the Urban Ecosystem Performance Index (UEPI) highlights sharp spatial polarization, with critical stress concentrated in the industrial and port zones of the urban core. The results provide policy-makers and stakeholders with critical insights into current pressures and environmental changes in urban and peri-urban ecosystems, offering a robust foundation for evidence-based management and climate change adaptation strategies. Full article

UAV tracking is important for aerial surveillance, inspection, and autonomous perception, yet its progress is constrained by the tension between tracking robustness and limited onboard computation. Compared with existing UAV tracking surveys, this review examines UAV tracking from the perspective of architectural evolution under efficiency constraints, and incorporates Mamba- and SSM-based trackers into the analysis. Specifically, this review discusses UAV tracking as a deployment-constrained problem, analyzes CF, Siamese/CNN, Transformer, and Mamba/SSM trackers from a cross-paradigm perspective, and explains how the literature-reported benchmark results should be interpreted under heterogeneous evaluation settings. We then examine how these architectural paradigms, including recent state-space and Mamba-style models, balance representation ability, interaction strength, temporal modeling, and deployment cost under UAV tracking constraints. Finally, we summarize architecture-level trade-offs and outline open problems in preserving local details during sequence modeling, reproducible efficiency evaluation, hardware-aware design, and multimodal UAV tracking. Full article

This study developed a novel framework integrating UAV-derived orthophotography, deep learning-based substrate classification, two-dimensional hydraulic modeling, Froude number (Fr) analysis, and multispecies habitat suitability assessment to evaluate the effects of channel modification and precipitation on fish habitats in Gaoliao Creek, eastern Taiwan. Habitat changes under baseflow and rainfall-induced high-flow conditions were quantified using Fr-based hydraulic habitat availability and Habitat Suitability Index (HSI)- and Combined Habitat Suitability Index (CHSI)-based habitat suitability. Channel modification transformed the channel from a deep and slow-flowing system into a shallower and faster-flowing environment. Under baseflow conditions, the proportion of available habitat meeting the adopted hydraulic criteria decreased from 81.6% to 73.9%, whereas the CHSI-derived proportion of weighted usable area (PUA) increased from 0.300 to 0.323 due to favorable substrate composition. During rainfall events, habitat availability and suitability declined markedly during peak flows and recovered as discharge receded. Compared with the pre-engineering channel, the modified channel exhibited greater sensitivity to short-term hydrological fluctuations but effectively prevented overbank flooding during the selected extreme rainfall event. These findings highlight the trade-off between flood-control benefits and ecological resilience and emphasize the importance of maintaining habitat heterogeneity in river management. Because the analyses were based on a single typhoon-related rainfall event and lacked direct biological validation, the results should be interpreted as event-specific predictions requiring further verification. Full article

Multi-runway sequencing of unmanned aerial vehicles (UAVs) at temporary disaster relief aerodromes presents a priority-heterogeneous scheduling problem under class-asymmetric wake turbulence constraints. We formulate this as a priority-weighted Markov decision process with a deliberately minimalist reward—per-step class weights for completed landings, with no shaping or hand-crafted safety logic—and extend it with per-UAV operational deadlines (encoding en-route endurance consumption) and per-runway queue capacity constraints that produce a non-trivial action mask. We train a Proximal Policy Optimisation (PPO) agent and benchmark it against six baselines spanning deterministic optimisation (Joint-LA-1), stochastic lookahead (Stochastic-LA), and online tree search (MCTS). Across 100 paired evaluation episodes, PPO matches the operational standard Priority-FCFS within 2.7% (p = 0.124, not significant); Joint-LA-1, the strongest non-learned baseline, outperforms PPO by 3.2% (p = 0.043). Despite near-identical aggregate throughput, PPO autonomously develops a runway specialisation pattern—concentrating 60% of high-priority landings on a single strip while routing 93% of emergency arrivals to the remaining strips—that emerges entirely from the reward signal. Under looser deadlines, the PPO–PFCFS gap narrows to −0.5%, and wake symmetry ablation reveals that PPO outperforms Priority-FCFS by 46.5% when the asymmetric wake structure is removed. These results demonstrate that priority-aware capacity reservation can emerge without embedded domain knowledge, and that simple heuristics are near-optimal under tight operational constraints—a finding with direct implications for autonomous scheduling in disaster relief aviation. Full article

The detection technology of crop diseases and pests is transitioning from single sensor monitoring to intelligent perception and multimodal fusion. This paper follows the PRISMA 2020 standard and systematically reviews the relevant core literature. This paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases. The focus is on the architectural evolution of deep learning models, including lightweight convolutional neural networks (CNNs), vision transformers (ViTs) with long-range dependency modeling capabilities, and the efficient computing state space model Mamba. In addition, the research progress of spatial spectral joint learning, heterogeneous data fusion, and vision-language models (VLMs) in improving system robustness and interpretability are introduced. By synthesizing the integrated applications of UAV remote sensing, Internet of Things (IoT) edge computing and intelligent robots in staple and cash crops, this paper summarizes the implementation of the integrated system of perception, decision-making and execution. To address the issues of insufficient cross-domain generalization ability and uneven allocation of computing resources in existing models, this paper provides perspectives on the future development of agricultural artificial intelligence (AI) towards foundation model-driven, edge-intelligent collaboration, and green sustainable direction, which can provide theoretical reference for engineering applications in the field of intelligent plant protection. Full article

Landslide detection has largely relied on supervised learning or DEM-based representations, which can limit rapid deployment and generalization across heterogeneous terrain. In this work, we present a zero-shot, fully unsupervised framework that identifies landslide-like geomorphic instability candidates from raw UAV-mounted LiDAR, removing the need for labeled data, pre-event baselines, or rasterized terrain abstractions. Our approach is motivated by the observation that landslides manifest as localized geometric inconsistencies in the terrain surface. We capture this through a multi-scale formulation that combines point-level and cluster-level indicators of instability. At the point level, a PCA-based residual depth metric reduces slope-induced bias and highlights surface discontinuities, while local concavity captures terrain depletion patterns. At the cluster level, geomorphometric descriptors such as curvature concentration, surface roughness, elevation discontinuity, and slope variation are extracted using density-aware 3D clustering and integrated through adaptive feature fusion. The resulting probabilistic instability field enables spatially coherent delineation of landslide scars, including rupture boundaries, displaced material, and emerging failure regions. In addition, the detected patches provide useful priors for post-event susceptibility analysis without requiring temporal observations. Experiments across diverse geomorphic settings show that the proposed method improves detection of subtle terrain disturbances compared to DEM-based pipelines and supervised learning approaches, while remaining robust to noise and terrain variability. Overall, this work demonstrates that geometry-driven, unsupervised inference on raw 3D data can serve as a practical and scalable alternative for near real-time landslide detection using UAV-based systems. Full article

Spectral sensors have expanded the opportunities for the non-destructive monitoring of crops and weeds. However, the lack of standardized and accessible analytical pipelines remains a major limitation for data reproducibility and integration in this field. EasySpectra was developed to address these challenges by providing a unified environment that integrates data import, radiometric calibration, geometric alignment, spectral pre-processing, region-of-interest selection, feature extraction, vegetation index computation, and dataset construction. A graphical user interface guides users through the entire analytical workflow, reducing technical barriers for non-experts. EasySpectra supports heterogeneous data sources, including single-band images, spectral cubes and georeferenced orthomosaics. Across 100 sampled areas, the correction + normalization workflow in EasySpectra produced NDVI values very close to Pix4DFields (0.70 ± 0.052 vs. 0.69 ± 0.055), with a pixel-wise correlation of up to 0.98 and low bias (MBE = 0.05). In an independent UAV dataset, EasySpectra also showed close agreement with WebODM, with NDVI values ranging from 0.09 ± 0.10 to 0.42 ± 0.08 versus 0.08 ± 0.13 to 0.43 ± 0.10, across 13 sampled areas. In addition, hyperspectral species classification using EasySpectra-extracted profiles achieved a Macro F1-score of 0.880, with class-wise accuracies ranging from 0.83 for canola to 0.95 for redroot pigweed. Overall, EasySpectra enables reproducible, transparent, and standardized spectral analysis. Full article

This paper addresses the formation assignment problem for heterogeneous UAV swarms in dynamic mission environments. A constrained optimization model is constructed in which UAV capabilities are divided into shareable and exclusive types; a neighborhood collaboration decay factor captures the locality of capability complementarity; and a Cobb–Douglas production function evaluates position-specific effectiveness under bottleneck constraints. The objective dynamically trades off deployment costs and system risks through threat-adaptive weight adjustment. To solve the model, a Hybrid Adaptive Large Neighborhood Search (HALNS) algorithm is proposed, integrating an adaptive destroy-repair mechanism, a mathematical-programming-based local search, and an incremental re-optimization strategy for rapid dynamic response. Experiments verify that HALNS attains globally optimal solutions on small-scale instances and outperforms mainstream baselines on medium-to-large problems. The collaboration mechanism raises system effectiveness by an average of 34.75% across four mission scenarios. Compared with static re-optimization, the incremental strategy improves dynamic response performance by 58.25% while reducing runtime by up to 56.7%. Sensitivity analyses confirm the robustness of key parameters. This work provides a theoretical and algorithmic foundation for intelligent UAV swarm assignment and reconfiguration. Full article

The emergence of 6G heterogeneous networks integrating unmanned aerial vehicles (UAVs), intelligent reflecting surfaces (IRSs), Internet of Things (IoT) devices, and fog/edge nodes creates new opportunities for intelligent and latency-sensitive applications while introducing significant security challenges. Traditional authentication mechanisms are inadequate for such dynamic, distributed, and heterogeneous environments that require secure collaborative communications. This paper proposes an authentication scheme based on Fog-RAN (Fog Radio Access Network) and a dual-blockchain architecture with smart contracts and elliptic curve cryptography (ECC). The proposed scheme provides secure network access, mutual authentication, traceability, auditability, and zero-trust enforcement. Formal verification using the ROR model, AVISPA and performance evaluation through smart-contract simulations indicate resilience to common network and cryptographic attacks and improved efficiency. Compared with existing schemes, the proposed approach reduces computation cost, bandwidth, and energy consumption by 64.2%, 59.6%, and 31.4%, respectively. These results support the suitability of the scheme for secure, scalable, and energy-efficient authentication in next-generation 6G networks. Full article

To enhance crack detection accuracy during UAV-based inspections and address key challenges such as false positives from complex backgrounds, missed narrow cracks, and insufficient structural continuity modeling, this study proposes CFM-Net, a task-oriented segmentation network integrating Channel-Spatial Attention and Multi-Scale Structural Enhancement. Constructed on an optimized U-Net backbone, it employs three dedicated modules: the Channel and Spatial Attention Module (CBAM) to amplify crack-related features and suppress background interference; the Gated Fusion Module (GFF) to dynamically fuse multi-level features, improving detection of fine, narrow cracks; and the Morphology-Guided Multi-Scale Structural Perception Module (MGMSIB), designed to model the structural continuity and multi-scale characteristics of cracks. Comprehensive evaluations on the Mix Bridge Crack dataset demonstrate CFM-Net achieves competitive performance among the evaluated methods, with an mIoU of 80.05% and an F1-score of 87.06%. This represents a significant improvement over strong baselines, outperforming DeepCrack and CrackFormer by 2.3% and 2.42% in mIoU, and 1.21% and 1.03% in F1-score, respectively. Furthermore, the model demonstrates robust performance on heterogeneous crack datasets composed of multiple public sources, particularly in reducing false alarms, recovering narrow cracks, and maintaining crack topology. These results conclusively validate the effectiveness and practical utility of the proposed method for automated bridge crack inspection. Full article

The interception of saturation incursions by Unmanned Aerial Vehicle (UAV) swarms presents critical challenges in multi-agent coordination, including the curse of dimensionality, heterogeneous interaction effects, and multi-scale decision-making requirements. This paper proposes a Hierarchical Multi-scale Mean-Field DDPG (HM-MF-DDPG) framework augmented by graph sampling and aggregation networks to address these challenges. The framework introduces three key innovations: (1) a graph-enhanced weighted mean-field approximation that employs attention mechanisms to dynamically assess the contextual importance of neighboring agents, overcoming the homogeneity limitation of conventional mean-field methods; (2) a hierarchical decision architecture that separates strategic coordination (via graph attention networks) from low-level flight control (via improved gated recurrent units with situational awareness modulation); and (3) a distributed target assignment mechanism formulated as a potential game and solved via parallel auction algorithms, enabling collision-free allocation without central coordination. Extensive simulations in a constructed UAV swarm interception environment demonstrate that the proposed framework achieves a 93% interception success rate with 50 interceptors against 25 intruders, outperforming Deep Deterministic Policy Gradient (DDPG) and Mean-Field DDPG (MF-DDPG) baselines in both convergence speed and task efficiency. The framework exhibits robust generalization across varying No-Fly Zone (NFZ) configurations and swarm scales, providing a scalable solution for cooperative interception under saturation incursions. Full article

This paper investigates time-varying output formation (TVOF) tracking for heterogeneous small fixed-wing unmanned aerial vehicles (UAVs) and nonholonomic unmanned ground vehicles (UGVs). The small fixed-wing UAVs operate in three-dimensional space, and the UGVs move on a two-dimensional plane, leading to heterogeneous dynamics with nonholonomic constraints, asymmetric velocity constraints, and input saturation. To address these challenges, distributed adaptive control protocols are developed under switching directed communication topologies. Unlike existing TVOF tracking methods that require global information, the proposed protocols do not rely on the upper bound of the leader’s unknown input or the eigenvalues of the Laplacian matrix. A constructive parameter-selection algorithm is provided, and the closed-loop stability is established using Lyapunov theory. Numerical simulations involving heterogeneous UAV-UGV formations verify that the proposed method achieves TVOF tracking under random disturbance while satisfying the prescribed motion constraints. Full article