Search Results (1,604)

Accurate and wide-area estimation of seaweed biomass is essential for evaluating blue carbon. Conventional diver surveys and two-dimensional (2D) aerial imagery analysis face challenges such as intensive labor and biomass underestimation. While Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-LiDAR) provides dense 3D spatial data, classifying point clouds in extremely shallow coastal waters with dense kelp and artificial structures remains difficult. This study establishes a high-accuracy biomass estimation method using UAV-LiDAR and PointNet. A heuristic hybrid filtering approach combining physical constraints and local statistics was developed to automatically generate high-quality reference data. The trained PointNet successfully segmented complex point clouds into four classes with an overall accuracy of 94.2%. To calculate biomass, we introduced a volume correction model based on point cloud density (coverage) to mitigate overestimation caused by internal canopy gaps. This correction yielded estimated wet weights nearly identical to the in situ measurements (an approximate 3% difference), confirming highly accurate biomass reproduction. Furthermore, while the conventional 2D maximum likelihood method underestimated total biomass, our 3D point cloud analysis successfully quantified the dense, overlapping canopy. This framework significantly improves the efficiency and accuracy of blue carbon monitoring. Full article

This study evaluates the accuracy of various preprocessing methods applied to scanned archival aerial photographs for reconstructing historical terrain in the Czech Republic. Seven workflows were tested on identical imagery and control points, varying parameters such as resolution unification, brightness normalization, focal length calibration, and AI-based denoising. Accuracy was assessed using GNSS checkpoints and high-resolution LiDAR data. Results show that basic brightness correction reduced the vertical RMSE by 59% (to 5.69 m). In contrast, standalone AI preprocessing was associated with increased geometric instability (RMSE 16.48 m) due to over-smoothing and the loss of essential micro-texture. However, the evaluated “Fusion AI” workflow—combining AI enhancement with strict focal length constraints—successfully mitigated this degradation. By restricting the internal orientation, it stabilized the vertical accuracy at 6.48 m, closely matching the best traditional approaches. Statistical analysis revealed strong spatial autocorrelation and non-normal error distributions, highlighting the need for robust validation. Ultimately, this study confirms that AI can be effectively utilized to enhance visual clarity in data-scarce historical reconstruction without sacrificing spatial reliability, provided it is strictly geometrically constrained. This offers an optimal compromise and a tested, reproducible workflow that supports heritage preservation and long-term environmental analysis. Full article

Multi-view point cloud registration is a fundamental technology for 3D reconstruction and indoor robot navigation and remains a core challenge for robust environmental perception. Its key difficulty lies in achieving globally consistent alignment of multiple partially overlapping point clouds efficiently and reliably. To address the limitations of existing methods, including low registration accuracy under small overlaps, severe error accumulation in long sequences, and the difficulty of balancing computational efficiency with global consistency, this paper proposes a multi-view point cloud registration framework that integrates spanning tree-based global topology constraints with a multi-scale pyramid-based local refinement strategy, specifically validated for indoor environments. First, a Voxel-Guided Normal Consistency Keypoint Extraction (VG-NCKE) method is presented. It leverages voxel grids to guide stable computation of local geometric features and filters candidate keypoints using a neighborhood normal direction consistency metric, effectively improving keypoint repeatability and spatial uniformity on unevenly distributed point clouds. Second, a coarse registration strategy with global constraints is constructed based on the Overlap Confidence-weighted Minimum Spanning Tree (OC-WST). It quantifies inter-frame overlap reliability as edge weights and employs Prim’s algorithm to build the minimum spanning tree as the topological skeleton for global registration. By prioritizing high-overlap frame pairs, the method suppresses error propagation and reduces the complexity of multi-view registration. Additionally, a multi-scale pyramid ICP fine registration algorithm is designed. It adopts a point-to-plane error model instead of the traditional point-to-point distance metric and performs progressive optimization through a three-layer point cloud pyramid from coarse to fine. This expands the convergence basin and gradually improves alignment accuracy, mitigating the sensitivity of single-scale ICP to initial poses. Extensive experiments on the indoor 3DMatch dataset and real indoor LiDAR sequences demonstrate that the proposed method outperforms competing approaches in terms of registration accuracy, computational efficiency, and long-sequence robustness, validating its effectiveness for indoor multi-view point cloud registration tasks. Full article

Preserving cultural heritage sites requires an interoperable digital framework capable of integrating heterogeneous spatial data and supporting immersive interaction for inspection and management. This study investigates the integration of multiple heritage data representations—including IFC-based Heritage Building Information Modeling (HBIM), terrestrial and UAV LiDAR point clouds, and 3D Gaussian Splatting reconstructions—into a unified digital management environment for the East Hall (Aula Timur) heritage site within the Bandung Institute of Technology (ITB) campus. A semantic–spatial interoperability workflow is proposed to harmonize BIM, point cloud, and landscape-scale data within a common georeferenced context, supported by a CityGML-based base map of the surrounding site. An immersive virtual environment was implemented using a head-mounted display to enable walkthrough-based inspection and damage annotation. All datasets were georeferenced within a unified coordinate system, allowing spatial registration between digital objects and the physical heritage site. The results demonstrate that multi-source heritage datasets can be integrated with high geometric accuracy, achieving TLS registration errors of approximately 2 mm and georeferencing residuals within 11.1 cm (horizontal) and 0.95 cm (vertical), while preserving semantic information and ensuring spatial coherence across HBIM, GIS, and immersive environments. The system is implemented in VR, with an architecture designed to support future MR-based on-site annotation and visualization. The proposed framework establishes a foundation for future heritage digital twin deployments and supports informed conservation decisions. Full article

Integration of LiDAR and thermal sensing has become increasingly important in robotics, infrastructure diagnostics, environmental monitoring, and autonomous perception systems. LiDAR sensors provide accurate three-dimensional geometric information but do not directly capture thermal properties of observed objects, whereas thermal cameras provide temperature distributions without explicit spatial structure. Fusion of both sensing modalities enables thermally augmented 3D scene reconstruction and spatial localization of temperature anomalies. This paper presents a practical LiDAR–thermal fusion framework for three-dimensional localization of heat sources using an Ouster OS1 LiDAR sensor and a FLIR A70 thermal camera. The proposed framework includes intrinsic thermal-camera calibration, extrinsic LiDAR–thermal calibration, multimodal data synchronization, projection of LiDAR points onto the thermal image plane, and assignment of temperature values to spatial points. Additionally, a dedicated thermally distinguishable calibration target is proposed to enable reliable multimodal feature extraction under low-contrast LWIR imaging conditions. The developed framework was experimentally validated using real radiometric thermal data and LiDAR point clouds acquired under laboratory conditions. Quantitative evaluation demonstrated reprojection errors below 1 pixel and a mean hottest-point localisation error of approximately 4.1 cm at a distance of 12.3 m. The results confirm that accurate spatial localisation of thermal anomalies can be achieved using a geometry-based multimodal fusion approach without relying on computationally expensive learning-based methods. The proposed framework emphasises practical deployment, deterministic calibration, and applicability in scenarios where limited training data or constrained computational resources make learning-based approaches difficult to apply. The proposed system may be applied to building energy diagnostics, industrial inspection, technical infrastructure monitoring, and robotic perception systems that require reliable spatial localisation of heat sources under real measurement conditions. Full article

Forests worldwide are impacted by tropical cyclones which alter their structure and ecological functions. In this study, we investigated repeat track data from ICESat-2’s (Ice, Cloud and land Elevation Satellite-2’s) land and vegetation height product (ATL08) to quantify structural changes in forests, with a focus on coastal forests in Alabama and Florida affected by Hurricane Sally (2020). We evaluated pre-hurricane ATL08 along-track canopy estimates at the ATL08 100 m segment scale and 20 m sub-segment scale and quantified structural canopy changes using exact pre- and post-repeated tracks. Results demonstrated strong agreement between ATL08’s 98th percentile canopy height (RH98) and reference airborne LiDAR-derived RH98 at both spatial scales, with improved performance at the 20 m sub-segment scale (mean bias: −1.16 m; MAE: 2.28 m; RMSE: 3.44 m; r: 0.80). Samples over evergreen forests provided reduced bias (−2 m to −0.55 m), reduced RMSE (4.02 m to 2.96 m), and improved correlation (0.77 to 0.83) than woody wetlands for canopy height acquisition. Post-hurricane analyses revealed height reductions in tall canopy (20–30 m) of 1.51 m, while smaller trees (0–10 m) increased by 0.77 m, reflecting growth. Overall, findings highlight ICESat-2’s ability to monitor canopy height changes and offer prospects for integrating ICESat-2 data for damage assessments. Full article

Airborne LiDAR data acquired before and after the 2024 Noto Peninsula earthquake in Japan were used to estimate three-dimensional (3D) ground-surface displacements based on the Iterative Closest Point (ICP) algorithm. Digital elevation (terrain) models (DEMs) were generated from pre-earthquake point cloud data acquired by Ishikawa Prefecture and compared with post-earthquake DEMs developed by the Forestry Agency of Japan. Three-dimensional coseismic displacements were derived from the spatial correlations between pre- and post-event DEMs for 50 m × 50 m tiles. The results depend on the tile size and are influenced by ground movements within and surrounding each tile. Therefore, moving-average windows of 250 m and 550 m were applied to the 50 m tiles to obtain continuous 3D displacement fields across the ground surface. A comparison between GNSS-measured displacements and the corresponding moving-average estimates for tiles containing triangulation points and continuously operating reference stations (CORSs) showed that the accuracy of the estimated displacements in all three components was within 0.2 m in terms of the root mean square error (RMSE). Full article

Urban canopy height is an essential indicator for characterizing vegetation structure and carbon sequestration, yet satellite LiDAR often lacks sufficient spatial resolution, airborne LiDAR is costly, and SAR has limited sensitivity to vegetation structure. This study proposes a canopy height inversion framework using high-resolution stereo pairs from the Gaofen-7 (GF-7) satellite. A 0.65 m Digital Surface Model (DSM) was generated from GF-7 data, and a relative surface height was derived by differencing the GF-7 DSM from a coarse 30 m DSM reference. Key features were selected via Boruta and Random Forest Recursive Feature Elimination (RF-RFE), and six models—linear, polynomial, support vector machine, backpropagation neural network, XGBoost, and RF—were compared. The results showed that the Boruta feature set improved average R² by 8.2%. Among all models, RF performed best (test set R² = 0.71, RMSE = 1.70 m) and exhibited the strongest resistance to overfitting. Canopy heights within Beijing’s Fifth Ring Road showed an “outer-high, inner-low” pattern: large parks exceeded 30 m, while the Central Business District remained below 3 m. GF-7 stereo pairs enable efficient and cost-effective retrieval of canopy height in fragmented urban green spaces, supporting ecological parameter quantification and urban green-space management. Full article

Accurate forest height reference points are essential for large-scale forest canopy mapping and carbon stock estimation. Currently, spaceborne Light Detection and Ranging (LiDAR) systems, primarily GEDI and ICESat-2, serve as the main data sources for acquiring global forest height reference points. To ensure data quality, conventional processing often relies on strict physical parameter filtering, such as retaining only nighttime and strong (full power) beam observations, which considerably reduces the available data density. Moreover, gross errors caused by signal attenuation or solar background noise often remain, limiting the accuracy of subsequent spatial modeling. To address the trade-off between measurement accuracy and data density, this study proposes a physically constrained outlier filtering strategy for spaceborne LiDAR retrievals, assisted by a priori continuous canopy height model (CHM) products. Aiming to maximize data retention, this method introduces a morphologically consistent global continuous CHM (such as the 10 m Pauls CHM) as a prior spatial envelope. By calculating the local height difference distribution and applying a 1$\sigma$ adaptive truncation, outliers are effectively removed. Comparative validations in the Genhe (coniferous forest, China) and HARV (mixed broadleaf forest, USA) study areas indicate that: (1) traditional filtering results in a data loss of over 80% while yielding limited accuracy; (2) after relaxing the initial filtering conditions, the proposed strategy reduces the overall root mean square error (RMSE) of GEDI and ICESat-2 retrievals by 12.6% to 36.0%; (3) owing to the effective removal of gross errors, the conventionally discarded daytime and weak (or coverage) beam data achieve substantially reduced error levels, sometimes even lower than those of traditional nighttime strong beam observations. Consequently, the spatial density of high-quality reference points is increased by 1.5 to 4.4 times. This study demonstrates the application value of low signal-to-noise ratio (SNR) spaceborne observations and provides a practical approach for obtaining high-quality, high-density control points for large-scale forest structure mapping. Full article

Environmental monitoring in hazardous indoor zones conventionally relies on fixed-sensor networks or manual inspections, both of which suffer from spatial blind spots and increased human exposure risks. This paper addresses the problem of transforming sparse, mobile sensor measurements into spatially resolved risk assessments in GPS-denied environments. We propose a Hazard Index (HI) framework that normalizes environmental parameters against established safety thresholds into a unified, graduated risk metric with $O\left(N\right)$ computational complexity, where N is the number of monitored parameters. The framework is designed for multi-parameter hazard assessment; the present work validates the computational pipeline, spatial mapping methodology, and classification logic through single-parameter CO₂ detection ($N=1$) deployed on a LiDAR-guided robotic platform integrating an MQ-135 gas sensor interfaced via a NodeMCU ESP8266 microcontroller. Experimental validation across a 144 sq ft indoor area achieved a trajectory-following RMSE of 0.54 ft relative to planned waypoints using Hector SLAM without odometry, detected CO₂ concentrations ranging from 0.02% to 0.25%, and identified a hazardous region encompassing eight measurement points ($\mathrm{HI}\ge 1.0$) using a three-tier classification scheme (Safe, Elevated, Hazardous) within 225 s of active mapping. The framework provides a lightweight computational footprint suitable for real-time evaluation on an NVIDIA Jetson Nano. The proposed approach establishes a cost-effective, reproducible methodology for autonomous indoor environmental monitoring, with the modular architecture designed for future expansion to multi-parameter sensing. Full article

Forest disturbances significantly affect the terrestrial carbon cycle, yet high-resolution detection, driver attribution, and carbon loss quantification remain challenging in cloudy and complex terrains. Here, we investigated the Northeast China and Southwest Hengduan Mountains forest regions from 2021 to 2024. We developed a Bayesian Model Averaging (BMA) framework integrating multi-source remote sensing (Sentinel-1/2, Landsat 8/9) and multi-algorithm ensembles (LandTrendr, CCDC, 1D-CNN) to extract 10 m disturbance features. Automated driver attribution and carbon loss quantification were achieved utilizing the Fire Information for Resource Management System (FIRMS), Dynamic World, and GEDI L4B LiDAR data. Validation yielded overall spatial accuracies of 91.15% in the Northeast and 89.62% in the Hengduan Mountains, with corresponding ensemble F1-Scores of 0.92 in both regions. Results indicated the disturbed area in the Northeast (1084.58 ha) significantly exceeded the Hengduan region (133.48 ha). Natural degradation dominated both regions (Northeast: 72.25%; Hengduan: 88.43%), though the Northeast experienced more wildfires and anthropogenic activities. Topographically, Northeast disturbances clustered on low-lying, gentle landscapes, whereas Hengduan events occurred on steep, high-altitude terrains. Due to denser per-pixel carbon storage, the Hengduan area exhibited higher carbon emission costs per unit area. Ultimately, this framework provides a quantitative technical foundation supporting high-resolution forest conservation and spatial evaluations for carbon neutrality commitments. Full article

Multi-modal land cover classification plays an important role in remote sensing applications such as urban monitoring and environmental analysis. By integrating complementary information from hyperspectral imagery (HSI) and LiDAR data, multimodal learning can significantly improve classification performance. However, existing Transformer-based fusion methods often suffer from high computational complexity and inefficient cross-modal interaction modeling, which limits their applicability in resource-constrained scenarios. To address these challenges, we propose LMFusion, an efficient framework for multimodal feature learning. Specifically, LMFusion enables efficient bidirectional feature interaction through a linear-complexity cross-attention mechanism and enhances long-range spatial-spectral representation learning with Mamba-based state space modeling, thereby achieving effective multimodal dependency modeling with linear computational complexity. In addition, a selective quantization-aware optimization strategy is introduced to support multiple bit-width settings (down to 1-bit), yielding a more compact and efficient model while improving representation robustness under low-bit constraints. Extensive experiments on the Houston2013, MUUFL, and Augsburg datasets demonstrate the effectiveness of LMFusion. It achieves overall accuracies of 95.84%, 94.95%, and 99.05%, respectively, consistently outperforming representative multimodal classification methods and showing strong potential for accurate and efficient multimodal remote sensing classification. Full article

Joint classification of hyperspectral images (HSI) and light detection and ranging (LiDAR) data is important for land-cover recognition, as it can exploit both spectral discrimination and structural elevation information. However, existing methods mainly focus on visual feature fusion and insufficiently utilize class-level semantic priors, which limits their discriminative capability in complex boundaries, visually similar categories, and limited-sample scenarios. To address these issues, this paper proposes a text-guided multimodal semantic fusion network for HSI–LiDAR classification. Specifically, a Channel-Modulated Mobile Convolution Module (CMMC) is designed to extract modality-specific features, a Spatial–Frequency Feature Enhancement Module (SFFE) is introduced to enhance spatial-boundary and frequency-domain structural representations, and a Bidirectional Cross-Modal Fusion Module (BCMF) is developed to promote complementary interaction between spectral and structural information. Meanwhile, class-level textual descriptions are constructed from class names, color attributes, and geographical contexts, and a text encoder is employed to obtain semantic prototypes. Furthermore, a multi-branch vision–text semantic alignment mechanism projects HSI features, LiDAR features, and fused features into a shared semantic space for joint constraints, improving semantic consistency and class separability. Experiments on the Houston2013, Augsburg, and Trento datasets demonstrate the effectiveness of the proposed method. It achieves an overall accuracy of 98.76% on Houston2013, with improvements of 0.62%, 0.52%, and 0.67 in overall accuracy, average accuracy, and Kappa coefficient × 100 over the best competing results, respectively. The proposed method also obtains the best overall metrics on Augsburg and Trento, and ablation studies verify the effectiveness of the proposed components. Full article

Recent advances in roadside sensing technologies, including camera-based systems, radar, and LiDAR, have enabled high-resolution sampling of vehicle trajectories, overcoming the temporal and spatial limitations of traditional data collection methods. Among these, LiDAR sensing has been widely adopted for traffic monitoring and surrogate safety analysis due to its high spatial accuracy and temporal resolution. However, sensor noise and occlusion in roadside LiDAR frequently introduce tracking point offsets and trajectory discontinuities, reducing the reliability of vehicle counts, traffic state estimation, and conflict analysis. To address these challenges, this study proposes a post-processing method based on time–space analysis to detect and correct occlusion-induced trajectory discontinuities. By exploiting the inherent spatiotemporal consistency of vehicle movements, the proposed approach identifies fragmented trajectories, reconstructs continuous vehicle paths, and recovers realistic traffic patterns. Validated on real-world LiDAR data collected at an urban intersection in Reno, Nevada, across four 30 min traffic periods covering AM and PM peak conditions on weekdays and weekends, the proposed method achieves an average precision of 0.989 and an average F1-score of 0.948, outperforming IMM, GNN-RM, and HMM + Viterbi benchmark methods. Count accuracy improved from 85.5% to 97.4% across all evaluated periods, confirming the method’s effectiveness under occlusion conditions. Full article

The spatial distribution and productivity of subsistence resources are central to food security, nutrition, and cultural vitality in circumpolar Indigenous communities. Yet few studies incorporate Indigenous Knowledge in methodology to monitor subsistence plant species. Here, we apply participatory action research to develop a monitoring system for the culturally and nutritionally important Rubus chamaemorus (atsalugpiaq, salmonberry) near the Yup’ik village of Quinhagak in southwest Alaska. With support from community members, two ground-truth surveys assessed berry productivity at nine sites within Quinhagak’s Traditional Land Use Area. Seventeen interviews identified key themes related to subsistence harvest and highlighted winter meteorological factors important for analysis. We compiled a multi-year dataset including PlanetScope eight-band SuperDove imagery (3 m GSD); airborne LiDAR and satellite-derived DEMs; and four meteorological parameters. Linear regression and multiple adaptive regression splines were tested to evaluate relationships among vegetation health, climate, landscape features, and berry productivity. Model outputs identified chlorophyll-related vegetation indices, particularly MTCI, as strong predictors of harvest outcomes, with higher flowering-season MTCI values associated with greater berry abundance. This work establishes a foundational, scalable approach for the long-term monitoring of Arctic subsistence plants in conjunction with Arctic communities and demonstrates the value of multi-layer data integration in regions historically challenging for remote sensing and ground surveys improving outcomes for regional harvest predictions and increased understanding of possible mechanisms controlling berry productivity in Arctic regions. Full article