Search Results (492)

Wildfires are becoming more frequent, severe, and long-lasting, driving rapid growth in sensor-based and artificial intelligence (AI)-enabled systems for early detection and risk assessment. This article presents a systematic literature review, based on 169 studies screened from 7511 records, of wildfire monitoring approaches using satellite and aerial remote sensing, fixed cameras, wireless sensor networks, and Internet of Things (IoT) platforms combined with machine learning (ML) and deep learning (DL) models for ignition detection, fire-weather indices, spread prediction, and burned-area mapping. The review organizes existing work by sensing modality, spatial and temporal scale, learning task, model type, and deployment architecture, and identifies the environmental drivers most commonly used across systems, including temperature, humidity, vegetation state, drought indices, and smoke or air quality. Based on this analysis, the review summarizes key technical challenges, including data sparsity in remote regions, high false-alarm rates, limited edge resources, and difficulty fusing heterogeneous data streams in real time. As an exploratory future direction, the review discusses bioindicator signals from wildlife and managed species, using honeybee colonies as a case example. Current bee bioacoustic studies support the detection of colony states and environmental stress proxies, but they do not yet validate wildfire or smoke detection. Therefore, this review proposes bee bioacoustics only as a potential complementary contextual signal for future hybrid wildfire monitoring systems. Full article

The latest generation of 4D imaging radar demonstrates significant potential in autonomous driving environmental perception, leveraging its capability to provide target elevation data and dense point clouds. This paper introduces a complete method for constructing a multimodal 4D imaging radar dataset for three-dimensional traffic scenes. It illustrates the hardware and software configurations of the data-acquisition vehicle. Methods including multi-sensor coordination, parameter calibration, timestamp synchronization and spatial datum synchronization are proposed. And eight typical three-dimensional traffic scenarios are designed, such as rainy weather environments, dense heterogeneous targets, enclosed tunnels, high-speed cut-in of multiple vehicles, multi-layered stereoscopic structures and edge working condition reproduction. In addition, this paper puts forward a frame-by-frame processing method for high-resolution images and point cloud data collected by the high-definition camera-LiDAR-4D imaging radar collaborative system. A large model-based 3D annotation method for multiple types of targets is proposed, generating a spatio-temporal sequence-optimized four-dimensional annotation sequence, and finally constructs a complete and high-quality multimodal 4D imaging radar dataset for three-dimensional traffic scenes. The results show that the constructed dataset enables the synchronization of timestamps and spatial coordinate systems. The large model can achieve high-precision 3D annotation for the four predefined target types. The dataset contains 11,400 frames of data from high-definition cameras, LiDAR, and 4D imaging radar, with 131,642 labels. This study will provide reliable fundamental support for the training and verification of 4D imaging radar perception algorithms, vehicle decision-making and planning in complex scenarios, and multi-sensor fusion technologies. Full article

Event cameras offer microsecond-level temporal resolution and high dynamic range, but their spatial resolution remains much lower than that of modern RGB cameras. This paper studies high-resolution novel-view synthesis from low-resolution (i.e., low-spatial-resolution) event streams alone. Given multi-view low-resolution events of a static scene, without RGB images or any high-resolution signal, our goal is to reconstruct a 3D Gaussian radiance field that can be rendered beyond the native event-sensor resolution. To this end, we propose an event-only framework that integrates event super-resolution into event-driven 3D Gaussian optimization. The framework exploits two types of cues. Temporal cues convert the high temporal resolution of event streams into dense local multi-view constraints by constructing event observations between nearby viewpoints. Spatial cues provide target-resolution event priors by lifting low-resolution event increments with a 2D event super-resolution module. To make these priors compatible with the physical measurements, we apply pool correction so that each high-resolution prior reproduces the original low-resolution event increment after downsampling. The corrected high-resolution priors and the native low-resolution measurements are jointly used to optimize a shared 3D Gaussian radiance field, enforcing multi-view consistency during reconstruction. Experiments on synthetic multi-view scenes with paired low- and high-resolution event ground truth show that our method outperforms Pre-SR and Post-SR baselines in both quantitative metrics and visual quality, demonstrating the effectiveness of reconstructing high-resolution radiance fields from low-resolution events alone. Full article

Three-dimensional (3D) imaging systems, including depth cameras, LiDAR sensors, and multi-view scanning pipelines, often produce point clouds with noisy normals, outliers, sparse sampling, and non-uniform density, which can degrade downstream mesh reconstruction. Poisson surface reconstruction is lightweight and training-free, but its global implicit formulation is sensitive to unreliably oriented samples and fixed density-trimming thresholds. This paper presents RG-PSR, a reliability-guided enhancement framework for Poisson-family surface reconstruction from degraded 3D-imaging point clouds. RG-PSR estimates a deterministic per-point reliability score from local density regularity, spacing variation, and normal consistency, and propagates this score through conservative point filtering, reliability-guided normal refinement, adaptive density-reliability trimming, and structure-aware postprocessing. The main pipeline requires no manual labels, neural network training, or ground-truth meshes at inference time. Experiments on three groups of object meshes under five deterministic degradation types show that RG-PSR improves Poisson-family reconstruction under degraded inputs. Compared with fixed density-trimmed Poisson reconstruction, RG-PSR reduces the overall Chamfer-$L_1$ from 0.0218 to 0.0172, improves $F_{0.01}$ from 0.6618 to 0.6836, and reduces ${\mathrm{Artifact}}_{0.02}$ from 0.3090 to 0.2632. In the broader classical comparison, local triangulation methods achieve stronger point-wise accuracy, while RG-PSR yields the fewest connected components and the highest largest-component ratio. These results position RG-PSR as a practical reliability layer for coherent Poisson-family reconstruction rather than a universal replacement for all surface-reconstruction methods. Full article

Camera and LiDAR sensors in safety-critical autonomous systems suffer undetected distributional shifts that silently corrupt downstream perception. Out-of-distribution (OOD) detection is the established sensor-data-integrity primitive, but no single post hoc detector covers every shift type, and existing detectors lack guarantees on their false-positive rate (FPR). This paper asks when calibrated score fusion helps and provides a distribution-free finite-sample FPR certificate. Four post hoc scores—Maximum Softmax Probability (MSP), Energy, Mahalanobis distance and k-nearest-neighbour (KNN) distance—are calibrated to p-values by the empirical cumulative distribution function (ECDF) and combined by Fisher’s method or cross-backbone z-score averaging, then wrapped in a conformal predictor with Hoeffding-based Probably Approximately Correct (PAC) bounds. On the full-split PUG camera benchmark (215,040 images), uniform same-backbone p-value fusion does not beat the best single detector (Mahalanobis); the gain comes from cross-backbone diversity: a z-score average of Mahalanobis distances over ResNet-50 and frozen DINOv2 reaches a mean area-under-the-ROC-curve (AUROC) of $0.9258$ ($+0.0199$), rising to $0.9292$ ($+0.0233$) with added spectral and dropout signals (DeLong $p<{10}^{-9}$). On the nuScenes LiDAR sensor (256,873 frames), uniform fusion yields only a small, calibration-sensitive gain over the best single detector (MSP), so the substantial fusion gain is confined to cross-backbone averaging on the camera. The distribution-free PAC certificate, by contrast, transfers across both sensors with margins below $1.5\%$ ($0.96\%$ camera, $0.25\%$ LiDAR), giving evidence usable in ISO 26262 and EASA CoDANN safety cases. Full article

Rapid detection and localization of liquid fuel spills is critical for first responders assessing fire and health hazards, yet current methods require ground-based sampling or specialized instrumentation, limiting their practicality for wide-area emergency response. We present a drone-based passive colorimetric sensor system using test strips impregnated with Nile red, similar to colored confetti. Nile red is a solvatochromic dye that undergoes distinct visible color transitions upon exposure to different liquids. The dye is embedded within a polymer matrix that minimizes leaching while providing high optical contrast between dry, water-exposed, and fuel-exposed states. The sensor strips exhibit solvent-specific colorimetric responses within one minute of exposure, readily detectable by standard RGB cameras mounted on unmanned aerial vehicles (UAVs) at altitudes up to 50 m. Automated classification was validated at 20 m altitude, enabling remote surveillance of contaminated surfaces without specialized equipment. Color-corrected image analysis using Calibrite ColorChecker calibration ensures reliable interpretation under variable field illumination (625–77,000 lux). Systematic laboratory evaluation of twelve fossil and bio-derived fuels revealed characteristic hue shifts that clearly discriminate ethanol-containing gasoline blends from diesel-range fuels. Rather than identifying specific molecules, the method functionally categorizes contamination into gasoline/ethanol blends versus diesel-type fuels, reflecting bulk polarity rather than molecular composition. Field validation confirmed localization and classification of fuel-exposed sensors, achieving F1 scores of 0.94 for gasoline and 0.98 for diesel detection with no false positives in the tested scenarios. This cost-effective and scalable approach provides actionable information on both contamination location and fuel type, crucial for rapid hazard assessment in emergency response scenarios. Full article

The majority of currently available hand kinematic databases have been gathered using expensive marker-based systems or are restricted to a particular gesture-recognition task, failing to capture the dynamic nature of joints when the hand is engaged with an object. To address this gap, we introduce the RGB-based Hand Joint Kinematics (RGB-HJK) dataset, a publicly available collection of continuous, frame-level 3D joint angle trajectories, recorded while ten healthy adults (six male, four female; age $25.8\pm 3.2$ years; BMI $22.8\pm 2.0$ kg/m²) performed five standardized object interaction grasps: Power Grasp (cylindrical bottle), Tripod Grasp (pen), Static Power Hold (smartphone), Precision Pinch (thin paper), and Lateral Pinch (book). Data were collected using a standard RGB camera and the MediaPipe Hands markerless pipeline at $26.95\pm 0.29$ Hz, a rate that was stable across all subjects. Each participant completed five trials for each grasp type. After filtering using active hold, 28,111 validated frames remained, with a 100% detection rate for all 250 trials. Intra-subject repeatability was good (mean SD $\le 7.9°$ across all joint grasp combinations) and inter-subject variability was within the range expected based on normal anatomical diversity. Importantly, kinematic validation of the Index Proximal Interphalangeal (PIP) joint (61.8° ± 18.4°) showed values consistent with ranges reported in previous studies using instrumented gloves and depth sensors. Principal Component Analysis (PCA) confirmed clear linear separability among the five grasp configurations. Unlike existing datasets, the RGB-HJK method does not compromise the natural sense of touch and is free of hardware occlusions, thereby providing an easily accessible ecological baseline. Full article

Accurate and unobtrusive measurement of upper-limb kinematics is critical for advancing wearable sensing technologies used in industrial ergonomics, human–machine interaction, and real-time biomechanics monitoring. This study evaluates the performance of two soft, flexible wearable sensors—BendLabs biaxial angular displacement sensors and StretchSense capacitive stretch sensors—for quantifying wrist and elbow motions during simulated dynamic industrial tasks. Wrist flexion–extension and radial–ulnar deviation were measured using BendLabs sensors mounted on the dorsal hand, while elbow flexion–extension was captured using StretchSense sensors positioned along the elbow joint. A multi-camera optical motion capture system served as the reference standard. Sensor data were preprocessed using baseline correction, smoothing, denoising, and normalized cross-correlation techniques to support temporal alignment with motion-capture recordings. Across all activities, the BendLabs sensors demonstrated moderate agreement with motion capture for wrist kinematics, with generally better performance for radial–ulnar deviation than for flexion–extension. StretchSense sensors demonstrated stronger agreement with motion capture for elbow flexion–extension, with performance that was generally consistent across task types. These findings support the feasibility of soft wearable sensors for capturing upper-limb kinematics during simulated occupational tasks and highlight their potential for integration into ergonomic assessment, occupational monitoring systems, and future industrial wearable platforms. Full article

Accurate road segmentation is a core perceptual technology for autonomous driving, but faces two challenges: (1) ambiguous road boundaries caused by insufficient modeling of contextual information relationships in CNN-based networks and (2) inadequate LiDAR-camera fusion due to modality gaps between heterogeneous sensors. To mitigate these limitations, this paper proposes a novel approach, named LiDAR-Camera High-Resolution Feature Fusion Network (LC-HR2FNet), a multi-cross-stage fusion model designed for road segmentation. Firstly, a new type of pseudo-LiDAR-Image representation is generated via an early-level fusion strategy and data complementation. Sparse point clouds are transformed into dense LiDAR-Image data and then concatenated with RGB channel maps to form complementary multi-modal data inputs. Subsequently, a modified HRNet backbone integrated with cross-stage feature fusion is constructed to strengthen information interaction across different branches and enhance the modeling of contextual relationships. Additionally, a dilated feature collection model is designed to collect multi-scale confidence scores for pixel-wise class determination. Experiments on the KITTI road benchmark demonstrate that the proposed method achieves a MaxF of 97.39% on UMM_ROAD and an average of 96.28% across all urban scenarios, demonstrating superior performance and robustness. Full article

This study presents a headset-type biofluorometric gas sensor incorporating a CMOS camera for continuous, non-invasive monitoring of transcutaneous ethanol from the ear canal. The sensor employs alcohol dehydrogenase (ADH) to catalyze the NAD⁺-to-NADH conversion during ethanol oxidation, enabling quantitative measurement through NADH fluorescence detection (λ_ex = 340 nm, λ_em = 490 nm). The integrated system comprises a wireless CMOS camera, an ADH-immobilized cotton mesh enzyme membrane, UV-LED excitation source, optical bandpass filters, and a dual convex lens assembly housed in a 3D-printed headset powered by a lithium battery. Key improvements include a 3.5-fold enhancement in fluorescence collection efficiency achieved through optimized dual convex lens configuration. Systematic screening of seven cotton mesh materials identified Iwatsuki cotton mesh as the optimal enzyme immobilization substrate, exhibiting minimal autofluorescence and 14.2-fold higher water retention capacity compared to H-PTFE membranes. The glutaraldehyde-crosslinked ADH-immobilized cotton mesh maintained enzymatic activity for over 45 min with a 10-fold improvement in signal-to-noise ratio. The system demonstrated a dynamic detection range spanning 10 ppb to 10 ppm for gaseous ethanol and exhibited high selectivity against interfering volatile organic compounds in skin gas, including methanol, acetaldehyde, formaldehyde, and acetone. Human experiments validated the system’s practical performance. Following alcohol consumption, subjects wore the device for 50 min while real-time fluorescence monitoring captured dynamic ethanol concentration changes in the ear canal. The dose-dependent fluorescence response—approximately 2-fold higher at 0.4 g/kg versus 0.04 g/kg alcohol intake—correlated well with calibration data. This headset-type biofluorometric sensor enables unrestrained continuous monitoring of ear canal ethanol, providing a novel wearable platform for alcohol metabolism assessment with potential applications in health monitoring and clinical research. Full article

As smart eyewear becomes increasingly widespread, the need for hands-free input interfaces is growing. Although eye-based input is a promising approach, many everyday interactions do not necessarily require the high-precision gaze-point estimation used in mainstream camera-based systems; instead, what is often needed is the recognition of coarse eyeball direction. In this study, we propose a method for recognizing coarse eyeball direction using infrared distance sensors mounted on eyewear. The proposed method leverages deformation patterns in the eyelid and surrounding skin associated with changes in eyeball direction. The evaluation results show that the proposed method achieved macro-F1 scores of 0.9 or higher in the best-performing conditions for the five- and nine-direction settings. These results demonstrate the feasibility of recognizing coarse eyeball direction from eyelid-skin deformation using infrared distance sensors on eyewear. Rather than replacing high-precision gaze-point estimation, the proposed method can be positioned as a low-cost, non-contact, and low-dimensional sensing approach for command-type eye-based input on eyewear devices. Full article

Rising temperatures and droughts are triggering forest die-off in climate warming hotspots such as the Mediterranean Basin. UAVs equipped with LiDAR and multispectral sensors offer a powerful tool for surveys of tree vigor at landscape level. We used UAV-acquired LiDAR data and multispectral camera imagery to segment individual tree crowns, classify species, and assess the health status in two drought-affected forests in northeastern Spain: a mixed Pinus pinaster–Quercus ilex forest and a Pinus halepensis forest. Individual trees were segmented and classified using object-based image analysis with the Random Forest algorithm incorporating spectral, structural, and topographic variables. Greenness indices (NDVI and EVI) were analyzed in relation to crown height, topography (slope and elevation) and solar radiation, and their interactions. Analyses showed satisfactory crown segmentation (F-Score = 0.85–0.86) and species classification (Overall accuracy = 0.86–0.99), though distinguishing spectrally similar classes remained challenging. Taller P. pinaster trees exhibited higher NDVI, while taller P. halepensis displayed higher NDVI values in dense neighborhoods and on gentle slopes. These findings highlight the potential of high-resolution UAV-based remote sensing for effective near-real-time detection and attribution of forest die-off. Future research should aim to improve algorithm accuracy and better integrate field-based validation across different forest types. Full article

Human activity recognition (HAR) lies at the core of digital healthcare applications that monitor different types of physical activity. Traditional HAR methods often struggle to adapt to variable-length, real-world activity data and to generalise across cohorts (e.g., from young to old cohorts). Thus, the aim of this study was to investigate HAR using wearable sensor data, with a particular focus on cross-cohort evaluation. Each dataset included two accelerometers (right thigh and lower back) sampling at 50 Hz, capturing a range of daily-life activities that were annotated using video recordings from chest-mounted cameras synchronised with the accelerometers. Neural networks were trained on young cohorts’ data and tested on old cohorts’ data. The effects of network architecture, sampling frequency and sensor location on classification performance were investigated. Network performance was evaluated using accuracy, recall, precision, F1-score and confusion matrices. The gated recurrent unit architecture achieved the best performance when trained solely on young cohorts’ data, with weighted F1-score of 0.95 ± 0.05 and 0.93 ± 0.05 for young and old cohorts, respectively, resulting in a highly generalizable method. Classification performance across multiple sampling frequencies was comparable. The thigh-mounted sensor consistently achieved higher performance than the lower back sensor across activities except lying. Furthermore, combining datasets significantly improved performance on the old cohort (weighted F1-score: 0.97 ± 0.02) due to increased variability in the training data. This study highlights the importance of network architecture and dataset composition in HAR and demonstrates the potential of neural networks for robust, real-world activity recognition across age-defined cohorts, specifically between young and old cohorts. Full article

The monitoring and conservation of built heritage is a major challenge for the scientific community, given the continuous degradation caused by natural, anthropogenic and climatic factors. The generation of high-resolution 3D documentation is important in the diagnosis of deterioration in historic buildings and the planning of conservation and restoration efforts. The present study proposes an integrated, multi-source workflow combining terrestrial laser scanning (TLS), unmanned aerial vehicle (UAV) photogrammetry, and 3D camera interior scanning. This workflow was employed to document and evaluate the Casa Rusănescu monument in Craiova, Romania. The following processes were incorporated: coordinated acquisition, processing, alignment, evaluation of geometric consistency and deviation-based diagnosis. The diagnosis process include measuring the distance between data clouds and analyzing surface roughness, curvature, planarity and linearity. The workflow was designed to be applicable in real urban conditions, ensuring the coverage of façades, interiors and roof structures. The final, combined dataset contained over 235 million points and includes both interior and exterior geometries. This process helped identify various types of damage, such as cracks, exfoliation, plaster detachment, moisture-related changes, and geometric deformations. An additional AI-assisted validation step (Twinspect) was used to cross-check the degradation indicators derived from point-cloud analyses. The findings suggest that using multiple sensors improves spatial completeness, enhances anomaly detection, and establishes a reliable baseline prior to restoration interventions and long-term monitoring. This methodology facilitates the development of digital twins and GIS-based risk assessments, thereby providing a scalable solution for heritage preservation. Full article

Unmanned aerial vehicles (UAVs) increasingly share low-altitude airspace with birds, making early distinguishing between drones and biological targets critical for safety and security. This work addresses long-range scenarios where objects occupy only a few pixels and appearance-based recognition becomes unreliable. We develop a model-driven simulation pipeline that generates synthetic data with a controlled camera model, atmospheric background and realistic motion of three aerial target types: multicopter, fixed-wing UAV and bird. From these sequences, each track is encoded as a time series of image-plane coordinates and apparent size, and a bidirectional long short-term memory (LSTM) network is trained to classify trajectories as drone-like or bird-like. The model learns characteristic differences in smoothness, turning behavior and velocity fluctuations, and to achieve reliable separation between drone and bird motion patterns on synthetic test data. Motion-trajectory cues alone can support early distinguishing of drones from birds when visual details are scarce, providing a complementary signal to conventional image-based detection. The proposed synthetic data and sequence classification pipeline forms a reproducible testbed that can be extended with real trajectories from radar or video tracking systems and used to prototype and benchmark trajectory-based recognizers for integrated surveillance solutions. The proposed method is designed to generalize naturally to real surveillance systems, as it relies on trajectory-level motion patterns rather than appearance-based features that are sensitive to sensor quality, illumination, or weather conditions. Full article