Light Detection And Ranging Data Research Articles

Study of the application of UAV and LiDAR to topographic survey of mountainous forest areas – Case study of Win 3 Wind Power Plant Project, Huong Hoa District, Quang Tri Province, Vietnam

Abstract Unmanned Aerial Vehicle (UAV) technology in conjunction with aerial photography and aerial Light Detection and Ranging (LiDAR) was used for topographic surveying of the Win 3 Wind Power Project area, a complex mountainous terrain situated in the Huong Hoa district of Quang Tri province, Vietnam with the primary objective to produce a map with a scale of 1:1,000 with a contour interval of 1.0 m, accompanied by 3D point clouds, a Digital Elevation Model (DEM), and an orthomosaic. Employing the UAV LiDAR system of DJI Matrice 300 RTK, we conduct ed survey flights, achieving a high point density laser scanning of 229 points/m2 and capturing ultra-high-resolution imagery at 4.22 cm/pixel. The integration of aerial LiDAR and photographic data results in a rich and detailed information repository regarding the surveyed terrain demonstrating that UAV LiDAR technology is a robust tool for geographic data collection, particularly in challenging terrain conditions in Vietnam. We also evaluated the accuracy of the created topographic map, achieving results that surpass the technical requirements and quality standards of the project. This underscores the potential and effectiveness of UAV LiDAR technology in the field of large-scale geographic data collection and mapping in Vietnam.

A Novel Spatial Data Pipeline for Streaming Smart City Data

Point cloud data from light detection and ranging (LiDAR) is often used for its spatial qualities, particularly in smart city projects involving vehicles and pedestrians. In this paper, the authors introduce a streaming and an on-demand pipeline for capturing LiDAR data from Velodyne Ultra Pucks placed along nine northern Nevada intersections known as the Living Lab within smart city project for the City of Reno. This pipeline is an iteration of a previously proposed pipeline with several feature enhancements. A streaming point cloud service was implemented to stream Point Cloud Data (PCD), LASzip (LAZ), and Google Draco. Also, two web services were built for the packet capture (PCAP) and ROS 2 bag files that enables acquisition of these formats for LiDAR data. A metadata service tracks edge device states and a GraphQL service interfaces with multiple services across the Living Lab. Draco provided the best processing time and had more options that affected the quality of the point cloud. To evaluate this pipeline, a discussion is provided, with an analysis of the point cloud formats.

A Hierarchical Coarse–Fine Adaptive Fusion Network for the Joint Classification of Hyperspectral and LiDAR Data

Hyperspectral image (HSI) and light detection and ranging (LiDAR) data have gained significant attention due to their excellent complementarity, which can be combined to improve the accuracy of land cover classification. However, there are still many challenges, such as unrelated physical characteristics, different data structures, and a lack of labeled samples. Many methods fail to leverage the full potential of multi-source data, particularly hierarchical complementary information. To address these problems, a hierarchical coarse–fine adaptive (HCFA) fusion network with dynamic convolution and a transformer is proposed for multi-source remote sensing land cover classification. Although fusing hierarchical information can improve the classification accuracy of the model, improper hierarchical feature selection and optimization may negatively affect the classification results. Therefore, a coarse–fine mutual learning strategy is proposed to dynamically fuse hierarchical information. Additionally, the disparity between multi-source data continues to prevent the realization of effective fusion. To tackle this challenge, cross-tokenization and cross-token attention are implemented to enhance information interaction. Furthermore, to improve the model representation with limited computational cost, we combine the advantages of dynamic convolution with a transformer. Validation on three standard datasets demonstrates that HCFA achieves high accuracy with just 1% of the training set while maintaining low computational costs.

Advancing Forest Degradation and Regeneration Assessment Through Light Detection and Ranging and Hyperspectral Imaging Integration

Integrating Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) enhances the assessment of tropical forest degradation and regeneration, which is crucial for conservation and climate mitigation strategies. This study optimized procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across 12 sites in the Brazilian Amazon, covering various environmental and anthropogenic conditions. Four forest classes (undisturbed, degraded, and two stages of second-growth) were identified using Landsat time series (1984–2017) and auxiliary data. Metrics from 600 samples were analyzed with three classifiers: Random Forest, Stochastic Gradient Boosting, and Support Vector Machine. The combination of LiDAR and HSI data improved classification accuracy by up to 12% compared with single data sources. The most decisive metrics were LiDAR-based upper canopy cover and HSI-based absorption bands in the near-infrared and shortwave infrared. LiDAR produced significantly fewer errors for discriminating second-growth from old-growth forests, while HSI had better performance to discriminate degraded from undisturbed forests. HSI-only models performed similarly to LiDAR-only models (mean F1 of about 75% for both data sources). The results highlight the potential of integrating LiDAR and HSI data to improve our understanding of forest dynamics in the context of nature-based solutions to mitigate climate change impacts.

Active Fault Interpretation in the Northern Segment of the Red River Fault Based on Multisource Remote Sensing Data

High-resolution topographic and geomorphic data are important basic data for the study of active structures. Here, multisource remote sensing data were used to reinterpret the active faults in the northern segment of the Red River Fault (China). First, we obtained airborne light detection and ranging (LiDAR) data, high-resolution GaoFen-7 (GF-7) remote sensing image data, and historical aerial photographs, and a high-resolution digital elevation model (DEM) was generated based on the airborne LiDAR data and GF-7 data. According to the remote sensing interpretation, the main active faults were identified. We subsequently verified the faults in the field and constrained the geographic locations. The current activity was confirmed to be dominantly normal faulting, with some dextral strike-slip components, and the latest active age was the Late Holocene. It reflects the coordination of structural deformation between the rotation of the secondary block and the sliding of the boundary fault within the Sichuan–Yunnan Block. The results show that airborne LiDAR and GF-7 remote sensing data have a great application value in providing high-resolution topographic and geomorphologic data for the study of active structures. The comprehensive application of multisource remote sensing data can greatly improve the reliability of active fault interpretations and provide a reference for follow-up research within the study area.

KOALA: A Modular Dual-Arm Robot for Automated Precision Pruning Equipped with Cross-Functionality Sensor Fusion

Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications.

An Improved Adaptive Grid-Based Progressive Triangulated Irregular Network Densification Algorithm for Filtering Airborne LiDAR Data

Ground filtering is crucial for airborne Light Detection and Ranging (LiDAR) data post-processing. The progressive triangulated irregular network densification (PTD) algorithm and its variants outperform others in accuracy, stability, and robustness, using grid-based seed point selection, TIN construction, and iterative rules for ground point identification. However, these methods still face limitations in removing low points and accurately preserving terrain details, primarily due to their sensitivity to grid size. To overcome this issue, a novel PTD filtering algorithm based on an adaptive grid (AGPTD) was proposed. The main contributions of the proposed method include an outlier removal method using a radius outlier removal algorithm and Kd-tree, a method for establishing an adaptive two-level grid based on point cloud density and terrain slope, and an adaptive selection method for angle and distance thresholds in the iterative densification processing. The performance of the AGPTD algorithm was assessed based on widely used benchmark datasets. Results show that the AGPTD algorithm outperforms the classical PTD algorithm in retaining ground feature points, especially in reducing Type I error and average total error significantly. In comparison with other advanced algorithms developed in recent years, the novel algorithm showed the lowest average Type I error, the minimal average total error, and the greatest average Kappa coefficient, which were 1.11%, 2.28%, and 90.86%, respectively. Additionally, the average accuracy, precision, and recall of AGPTD were 97.69%, 97.52%, and 98.98%, respectively.

Estimating vegetation and litter biomass fractions in rangelands using structure-from-motion and LiDAR datasets from unmanned aerial vehicles

ContextThe invasion of annual grasses in western U.S. rangelands promotes high litter accumulation throughout the landscape that perpetuates a grass-fire cycle threatening biodiversity.ObjectivesTo provide novel evidence on the potential of fine spatial and structural resolution remote sensing data derived from Unmanned Aerial Vehicles (UAVs) to separately estimate the biomass of vegetation and litter fractions in sagebrush ecosystems.MethodsWe calculated several plot-level metrics with ecological relevance and representative of the biomass fraction distribution by strata from UAV Light Detection and Ranging (LiDAR) and Structure-from-Motion (SfM) datasets and regressed those predictors against vegetation, litter, and total biomass fractions harvested in the field. We also tested a hybrid approach in which we used digital terrain models (DTMs) computed from UAV LiDAR data to height-normalize SfM-derived point clouds (UAV SfM-LiDAR).ResultsThe metrics derived from UAV LiDAR data had the highest predictive ability in terms of total (R2 = 0.74) and litter (R2 = 0.59) biomass, while those from the UAV SfM-LiDAR provided the highest predictive performance for vegetation biomass (R2 = 0.77 versus R2 = 0.72 for UAV LiDAR). In turn, SfM and SfM-LiDAR point clouds indicated a pronounced decrease in the estimation performance of litter and total biomass.ConclusionsOur results demonstrate that high-density UAV LiDAR datasets are essential for consistently estimating all biomass fractions through more accurate characterization of (i) the vertical structure of the plant community beneath top-of-canopy surface and (ii) the terrain microtopography through thick and dense litter layers than achieved with SfM-derived products.

GASSF-Net: Geometric Algebra Based Spectral-Spatial Hierarchical Fusion Network for Hyperspectral and LiDAR Image Classification

The field of multi-source remote sensing observation is becoming increasingly dynamic through the integration of various remote sensing data sources. However, existing deep learning methods face challenges in differentiating between internal and external relationships and capturing fine spatial features. These models often struggle to effectively capture comprehensive information across remote sensing data bands, and they have inherent differences in the size, structure, and physical properties of different remote sensing datasets. To address these challenges, this paper proposes a novel geometric-algebra-based spectral–spatial hierarchical fusion network (GASSF-Net), which uses geometric algebra for the first time to process multi-source remote sensing images, enabling a more holistic approach to handling these images by simultaneously leveraging the real and imaginary components of geometric algebra to express structural information. This method captures the internal and external relationships between remote sensing image features and spatial information, effectively fusing the features of different remote sensing data to improve classification accuracy. GASSF-Net uses geometric algebra (GA) to represent pixels from different bands as multivectors, thus capturing the intrinsic relationships between spectral bands while preserving spatial information. The network begins by deeply mining the spectral–spatial features of a hyperspectral image (HSI) using pairwise covariance operators. These features are then extracted through two branches: a geometric-algebra-based branch and a real-valued network branch. Additionally, the geometric-algebra-based network extracts spatial information from light detection and ranging (LiDAR) to complement the elevation data lacking in the HSI. Finally, a genetic-algorithm-based cross-fusion module is introduced to fuse the HSI and LiDAR data for improved classification. Experiments conducted on three well-known datasets, Trento, MUUFL, and Houston, demonstrate that GASSF-Net significantly outperforms traditional methods in terms of classification accuracy and model efficiency.

ML Approaches for the Study of Significant Heritage Contexts: An Application on Coastal Landscapes in Sardinia

Remote Sensing (RS) and Geographic Information Science (GIS) techniques are powerful tools for spatial data collection, analysis, management, and digitization within cultural heritage frameworks. Despite their capabilities, challenges remain in automating data semantic classification for conservation purposes. To address this, leveraging airborne Light Detection And Ranging (LiDAR) point clouds, complex spatial analyses, and automated data structuring is crucial for supporting heritage preservation and knowledge processes. In this context, the present contribution investigates the latest Artificial Intelligence (AI) technologies for automating existing LiDAR data structuring, focusing on the case study of Sardinia coastlines. Moreover, the study preliminary addresses automation challenges in the perspective of historical defensive landscapes mapping. Since historical defensive architectures and landscapes are characterized by several challenging complexities—including their association with dark periods in recent history and chronological stratification—their digitization and preservation are highly multidisciplinary issues. This research aims to improve data structuring automation in these large heritage contexts with a multiscale approach by applying Machine Learning (ML) techniques to low-scale 3D Airborne Laser Scanning (ALS) point clouds. The study thus develops a predictive Deep Learning Model (DLM) for the semantic segmentation of sparse point clouds (<10 pts/m2), adaptable to large landscape heritage contexts and heterogeneous data scales. Additionally, a preliminary investigation into object-detection methods has been conducted to map specific fortification artifacts efficiently.

Interactive transformer and CNN network for fusion classification of hyperspectral and LiDAR data

ABSTRACT The Transformer has become pivotal for the integrated analysis of multi-source remote-sensing (RS) data in Earth observation, particularly in applications such as the fusion classification of hyperspectral images (HSI) and Light Detection and Ranging (LiDAR) data. However, Transformers are often employed as effective feature extractors by adopting similar processing blocks for different modalities from multi-source sensors, overlooking differences in imaging principles and data characteristics. Moreover, in the feature extraction process across different sensor data, there is a lack of necessary cross-modal information interaction, leading to insufficient utilization of complementary information between different sensors and resulting in suboptimal fusion outcomes. In this paper, we propose an interactive Transformer and CNN network for the fusion classification of HSI and LiDAR data. Specifically, a heterogeneous three-branch network architecture is designed for HSI and LiDAR data, where Transformers and CNNs encapsulate global contextual spatial and spectral information for HSI and capture geometric elevation patterns for LiDAR data, respectively. Elevation-Spatial Interaction (ESI) and Spectral-Spatial Interaction (SSI) modules are then introduced for multi-stage feature interaction. ESI enables the CNN-Transformer network to focus on essential local elevation details while simultaneously modelling global contextual spatial information. SSI facilitates the Transformer-Transformer network to cyclically intertwine spectral and spatial information for long-range spectral-spatial feature fusion. Finally, the interacted elevation, spatial, and spectral features undergo the Gated Fusion module to achieve hierarchical fusion adaptively, resulting in an elevation-spatial-spectral representation. Experiments conducted on three benchmark HSI-LiDAR datasets demonstrate the effectiveness of our proposed approach.

Vehicle and Pedestrian Traffic Signal Performance Measures Using LiDAR-Derived Trajectory Data.

Light Detection and Ranging (LiDAR) sensors at signalized intersections can accurately track the movement of virtually all objects passing through at high sampling rates. This study presents methodologies to estimate vehicle and pedestrian traffic signal performance measures using LiDAR trajectory data. Over 15,000,000 vehicle and 170,000 pedestrian waypoints detected during a 24 h period at an intersection in Utah are analyzed to describe the proposed techniques. Sampled trajectories are linear referenced to generate Purdue Probe Diagrams (PPDs). Vehicle-based PPDs are used to estimate movement level turning counts, 85th percentile queue lengths (85QL), arrivals on green (AOG), highway capacity manual (HCM) level of service (LOS), split failures (SF), and downstream blockage (DSB) by time of day (TOD). Pedestrian-based PPDs are used to estimate wait times and the proportion of people that traverse multiple crosswalks. Although vehicle signal performance can be estimated from several days of aggregated connected vehicle (CV) data, LiDAR data provides the ability to measure performance in real time. Furthermore, LiDAR can measure pedestrian speeds. At the studied location, the 15th percentile pedestrian walking speed was estimated to be 3.9 ft/s. The ability to directly measure these pedestrian speeds allows agencies to consider alternative crossing times than those suggested by the Manual on Uniform Traffic Control Devices (MUTCD).

Mapping percent canopy cover using individual tree- and area-based procedures that are based on airborne LiDAR data: Case study from an oak-hickory-pine forest in the USA

Canopy cover (CC) is the proportion of a forest floor covered by the vertical projection of tree crowns. Recently, it has become common to utilize LiDAR (light detection and ranging) canopy metrics to estimate CC over large areas. However, these metrics are primarily related to canopy density rather than the specific definition of CC. Here, two processes that employed individual tree segmentation (ITS) and area-based procedures based on LiDAR data are presented to estimate CC across the Talladega Division of the Talladega National Forest (93,694 ha) at the plot, stand, and landscape levels. The two analytical procedures were assessed using the results of a plot/grid method as a reference dataset, which focused on CC estimates within 255 field measurement fixed-area sample plots. The accuracy of a third process, employing an imagery-based visual CC assessment, was also compared against the two procedures and the reference dataset. The LiDAR-based analytical procedures were able to provide estimates of CC with an RMSE of approximately 15 %, which is acceptable for landscape-level assessments. Based on the results of this study, we conclude that CC maps, when created using LiDAR data, may be suitable for various operational tasks such as assessing the impact of forest disturbances and helping to determine the habitat suitability for certain wildlife species.

A novel BH3DNet method for identifying pine wilt disease in Masson pine fusing UAS hyperspectral imagery and LiDAR data

Pine Wilt Disease (PWD) is a forest infectious disease that inflicts substantial economic losses to China’s forestry. Its rapid spread and the significant challenges associated with its control make early detection of infected trees crucial for disaster prevention. Unmanned aerial systems (UASs) hyperspectral imaging (HSI) and light detection and ranging (LiDAR) technologies provide high-resolution spectral diagnostic information coupled with intricate three-dimensional structural data, which has potential for fine grained monitoring of PWD. However, how to fuse HSI and LiDAR data to identify the early infected individual trees is still a challenge. This study presents a novel instance segmentation network, BH3DNet, to identify individual trees at different PWD-infected stages by extracting high-level abstract features based on the fusion of drone HSI and LiDAR data. BH3DNet introduces the PointNet++ model as the base network, and incorporates a shared encoder and twin parallel decoders to align semantic category prediction and instance segmentation of individual trees in an end-to-end approach. By applying an enhanced point cloud dataset that fuses drone HSI and LiDAR point cloud data, this model facilitates the identification of PWD infection stages at the individual tree scale. We evaluated the proposed model in a Masson pine forest stand sparsely mixed with broadleaf trees in a variety of infection states ranging from healthy to severely infected by PWD, and compared the performance of the model using the RGB bands, full HSI bands and screened bands as inputs, respectively. BH3DNet achieves an overall accuracy of 89.65 % with a Kappa × 100 of 87.29 for identifying individual trees using screened HSI bands and LiDAR point cloud, significantly outperforming the Mask R-CNN using only HSI data (overall accuracy: 70.81 %, Kappa × 100: 64.16). Moreover, BH3DNet’s accuracy at the early infection stage reaches 83.75 %. It proves that fusing HSI and point cloud data reflects the information of individual trees distribution and infection status, and the BH3DNet is suitable for high-precision monitoring of PWD.

Active remote sensing data and dispersal processes improve predictions for an invasive aquatic plant during a climatic extreme in Great Lakes coastal wetlands

Invasive aquatic plants pose a significant threat to coastal wetlands. Predicting suitable habitat for invasive aquatic plants in uninvaded yet vulnerable wetlands remains a critical task to prevent further harm to these ecosystems. The integration of remote sensing and geospatial data into species distribution models (SDMs) can help predict where new invasions are likely to occur by generating spatial outputs of habitat suitability. The objective of this study was to assess the efficacy of utilizing active remote sensing datasets (synthetic aperture radar (SAR) and light detection and ranging (LiDAR) with multispectral imagery and other geospatial data in predicting the potential distribution of an invasive aquatic plant based on its biophysical habitat requirements and dispersal dynamics. We also considered a climatic extreme (lake water levels) during the study period to investigate how these predictions may change between years. We compiled a time series of 1628 field records on the occurrence of Hydrocharis morsus-ranae (European frogbit; EFB) with nine remote sensing and geospatial layers as predictors to train and assess the predictive capacity of random forest models to generate habitat suitability in Great Lakes coastal wetlands in northern Michigan, USA. We found that SAR and LiDAR data were useful as proxies for key biophysical characteristics of EFB habitat (emergent vegetation and water depth), and that a vegetation index calculated from spectral imagery was one of the most important predictors of EFB occurrence. Our SDM using all predictors yielded the highest mean overall accuracy of 88.3% and a true skill statistic of 75.7%. Two of the most important predictors of EFB occurrence were dispersal-related: 1) distance to the nearest known EFB population (m), and 2) distance to nearest public boat launch (m). The area of highly suitable habitat (pixels assigned ≥0.8 probability) was 74% larger during a climatically extreme high water-level year compared to an average year. Our findings demonstrate that active remote sensing can be integrated into SDM workflows as proxies for important drivers of invasive species expansion that are difficult to measure in other ways. Moreover, the importance of a proxy variable for endogenous dispersal (distance to nearest known population) in these SDMs indicates that EFB is currently spreading, and thereby less influenced by within-site dynamics such as interspecific competition. Lastly, we found that extreme climatic conditions can dramatically change this species’ niche, and therefore we recommend that future studies include dynamic climate conditions in SDMs to more accurately forecast the spread during early invasion stages.

Classification of hyperspectral and LiDAR data by transformer-based enhancement

ABSTRACT The integration of multi-modal data allows for a more accurate representation of the ground characteristics. For a comprehensive interpretation of remote sensing data, existing multi-modal data fusion research mainly focuses on the joint utilization of 3D Light Detection and Ranging (LiDAR) and 2D Hyperspectral Image (HSI) data. However, existing algorithms do not pay much attention to the interaction of high-level semantic information between different modal data before fusion. This paper proposes a novel multi-modal data fusion deep learning network with the Cross-Modal Self-Attentive Feature Fusion Transformer (SAFFT). The framework employs a multi-head self-attention layer to fuse various attention information from multiple heads, effectively enhancing advanced feature information from different modalities for comprehensive integration. Experimental results on the Houston 2013 dataset demonstrate the effectiveness of the proposed method, which achieves an overall accuracy (OA) of 94.3757% in classifying 15 semantic classes.

Accuracy Evaluation of LiDAR-SLAM Based 2-Dimensional Modelling for Indoor Environment: A Case Study

The rapid development of sensor technologies has led to smaller sensor sizes and lower costs. Today, the easy purchase of sensors such as cameras, Light Detection and Ranging (LiDAR), Radio Detection and Ranging (RADAR), Inertial Measurement Unit (IMU), and Global Navigation Satellite Systems (GNSS) receivers has caused significant developments in many applications such as robotics and unmanned vehicles. Raw data is transformed into information and products thanks to the methods in which the sensor data are used alone or with sensor integration. Simultaneous Localization and Mapping (SLAM) is one of the critical methods in which the vehicle's location is determined and the environment is modeled. This method can realize applications using cameras, LiDAR, or RADAR sensors as detection sensors. This study aimed to model an indoor area with a two-dimensional (2D) LiDAR sensor placed on an Unmanned Ground Vehicle (UGV) and analyzed the accuracy of the produced model. Normal Distribution Transform (NDT) - Particle Swarm Optimization (PSO) algorithm was used to generate the 2D model from the collected LiDAR data. The NDT-PSO algorithm was run on the Robot Operating System (ROS) installed on the Jetson Nano Developer Kit, and a real-time 2D model of the working area was processed. The reference lengths of the 75 facades in the 232 m2 indoor space were measured using a total station and calculated with CAD software. Percent error values were evaluated by comparing the reference and model lengths of the facades.

Urban tree health assessment using multifaceted remote sensing datasets: A case study in Hong Kong

Although climate change is impacting various aspects of our environment, it is important to note that the overall risk to trees remains low, especially in urban areas like Hong Kong where the benefits of trees to society are significant. The trees planted in an urban setting are isolated and have several limiting factors including, excessive run-off, urban pollution, physical damage and limited root growth, which sometimes lead for tree failure incidents. The conventional on-site tree health assessment method is time consuming thus, requiring a remote sensing based method to effectively and routinely monitor the health status of urban trees. In this study several types of remote sensing datasets have been exploited to assess the health status of more than 700 Old and Valuable Trees (OVTs) and Stone Wall Trees (SWTs) around Hong Kong. These datasets include the data from Terrestrial LiDAR (Light Detection and Ranging) Surveys (TLS), Handheld Laser Scanner (HLS), Airborne LiDAR Surveys (ALS) and airborne multispectral data. For validation purpose, the in situ tree parameters data was also obtained from the Tree Management Office (TMO) of the Greening, Landscape & Tree Management Section (GLTMS) under the Development Bureau of the Hong Kong SAR Government. The results have indicated that over the period of four years (2017–2020) there has been a decline in the health of some target trees which can be attributed to the increased infestation rate in trees and severe weather conditions. The usage of LiDAR data has supported the fact that different tree structural forms can effectively be extracted and can help making informed decisions on the precise health conditions of urban trees.

Aerial Hybrid Adjustment of LiDAR Point Clouds, Frame Images, and Linear Pushbroom Images

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging due to the limited number of observations available to estimate the exterior image orientations. However, data from these three sensors conceptually provide information to estimate the same trajectory corrections, which is favorable for solving the problems of image depth estimation or the planimetric correction of LiDAR point clouds. Thus, our purpose with the presented study is to jointly estimate corrections to the trajectory and interior sensor states in a scalable hybrid adjustment between 3D LiDAR point clouds, 2D frame images, and 1D LP images. Trajectory preprocessing is performed before the low-frequency corrections are estimated for certain time steps in the following adjustment using cubic spline interpolation. Furthermore, the voxelization of the LiDAR data is used to robustly and efficiently form LiDAR observations and hybrid observations between the image tie-points and the LiDAR point cloud to be used in the adjustment. The method is successfully demonstrated with an experiment, showing the joint adjustment of data from the three different sensors using the same trajectory correction model with spline interpolation of the trajectory corrections. The results show that the choice of the trajectory segmentation time step is not critical. Furthermore, photogrammetric sub-pixel planimetric accuracy is achieved, and height accuracy on the order of mm is achieved for the LiDAR point cloud. This is the first time these three types of sensors with fundamentally different acquisition techniques have been integrated. The suggested methodology presents a joint adjustment of all sensor observations and lays the foundation for including additional sensors for kinematic mapping in the future.

A cross-modal feature aggregation and enhancement network for hyperspectral and LiDAR joint classification

Advancements in Earth observation technologies have greatly enhanced the potential of integrating hyperspectral (HS) images with Light Detection and Ranging (LiDAR) data for land use and land cover classification. Despite this, most existing methods primarily focus on employing deep network layers to extract features from two heterogeneous data modalities, often overlooking a gradual modeling data representation approach from shallow to deep layers. Furthermore, excessive network layers can result in the deterioration of modality-specific features, therefore lowering the classification performance. The paper proposed a novel cross-modal feature aggregation and enhancement network for the joint classification of HSI and LiDAR data. Initially, a cross-modal feature fusion module is developed to exploit spatial scale consistency to complete the interchange and fusion of feature embedding at the pixel level, preserving the original information from the two heterogeneous modalities to a certain degree. Then two straightforward strategies (i.e., addition and concatenation) are employed in the shallow network layers before being sent to the transformer encoder. The former facilitates the model’s ability to discern more subtle distinctions and refine spatial location details. The latter ensures the preservation of information integrity, effectively mitigating the risk of feature loss. Moreover, invertible neural networks and a feature enhancement module are introduced, leveraging the complementary information of HSI and LiDAR data to enhance the detail and texture information extracted in deeper layers. Extensive experiments on Houston2013, Trento, and MUUFL datasets demonstrate that the proposed method outperforms several state-of-the-art models in three evaluation metrics, achieving an accuracy improvement of up to 2%. The proposed model brings new inspirations for HSI and LiDAR classification, which is critical for accurate environmental monitoring, urban planning, and precision agriculture. The source code is publicly accessible at https://github.com/zhangyiyan001/CMFAEN.