Automatic Segmentation of Lidar Data

The detailed representation of the topography is an important aspect when dealing with any flood simulation model. A Digital Surface Model (DSM) with fine spatial resolution can capture topographic object boundaries with a high level of detail. Light detection and ranging (LiDAR) is a technology that can rapidly collect elevation data of a relatively large topographic area with fine spatial resolution (< 0.5m ∼1m). Information about physical objects on the DSM is critical for storm water management as the propagation of flood water is influenced by both the terrain and the objects on the terrain. The processing of LiDAR data is an important task for generating an accurate DSM suitable to running a flood simulation model. Filtering is the first step in processing LiDAR data. In the LiDAR community, the term “filtering” means the task of separating LiDAR data into ground (bare earth) and non-ground points (buildings and trees). However, the choice of appropriate filtering algorithm is crucial in order to detect micro objects such as boundary walls, fences, and thin embankments that have significant impact on the surface flow pattern. This study offers an innovative approach for developing a LiDAR filtering algorithm that is flexible in nature and able to detect not only buildings and trees but also the micro objects. The filtering algorithm is applied to two study areas, and the output suggests close correspondence to reality.

A high percentage of forest fires take place around roads. These infrastructures provide an escape route for the population in case of fire. Optimization of forest management in the surroundings of roads is a necessary task in terms of wildfires prevention and mitigation of their effects. Therefore, it is essential to avoid the horizontal continuity of vegetation across roads. A methodology for the measurement of canopy area over roads is developed and based on mobile LiDAR (Light Detection and Ranging) point clouds. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from the University of Vigo. The methodology is automated using LiDAR data processing (M-estimator Sample Consensus and near neighbour algorithms) and image processing techniques (rasterization and binarization). The developed algorithms are tested on a study area, the DP-3606 road (Spain). Results are compared with ground truth data of the canopy projected on the road. The best obtained results present a mean geometric error of 2.82% for 0.25 m resolution and 104.02% for 2 m resolution. Furthermore, the higher the pixel size, the greater the error was obtained with a linear correlation value of 0.99.

Civil engineering includes a wide range of applications that require three-dimensional (3D) information. With the advent of LiDAR (Light Detection And Ranging) technology, obtaining 3D terrain information accurately in a cost and time effective manner became more popular. In this paper, three case studies demonstrate the roles and applications of airborne LiDAR data including highway, transmission line and hydropower dam development projects. In first case study, the highway alignment was considerably changed after the initial design stage. LiDAR data was able to do the re-alignment survey quickly, allowing for prompt changes in design. In the next case study, a comparison is made between conventional transmission route alignment design and the design using PLS-CADD with LiDAR data. Utilizing the LiDAR data in PLSCADD allows transmission line engineers to model the construction costs of several alignments options effectively and efficiently compared to the conventional method. The last case study uses LiDAR for obtaining the necessary information for proposed dam. Using LiDAR data maximizes topological accuracy and volume measurements while minimizing survey time and costs. Furthermore, utilizing the multi-temporal LiDAR data can reveal valuable information such as topography changes. LiDAR survey technology has proven that it is the best method to capture accurate data from the earth surface which provides maximum density of topographical &amp; digital imagery information for various applications.

Chapter 5 - Data Preprocessing and Feature Extraction

Accurate estimation of the fraction of absorbed photosynthetically active radiation (fPAR) is important for maize growth and yield estimations. Light detection and ranging (LiDAR)-derived canopy vertical structural and hyperspectral image-derived vegetation spectral information are complementary for vegetation fPAR estimation. This study explores the potential of artificial neural networks (ANNs) with two types of data to estimate maize fPAR. First, 45 metrics were derived from LiDAR data and 13 from a hyperspectral image. Then, the ANNs and stepwise multiple linear regression (SMLR) methods were used to estimate the fPAR. Finally, model validity was assessed using in-situ data. Results showed that the ANNs performed better in fPAR inversion (R 2 = 0.910, adj. R 2 = 0.921, RMSE = 0.046, RRMSE = 0.056, where R 2 is the coefficient of determination, adj. R 2 the adjusted coefficient of determination, RMSE the root mean squared error, and RRMSE the relative root mean squared error) than SMLR (R 2 = 0.638, adj. R 2 = 0.609, RMSE = 0.077, RRMSE = 0.092) and SMLR with the natural logarithm of data (R 2 = 0.855, adj. R 2 = 0.825, RMSE = 0.067, RRMSE = 0.081). This study is helpful for guiding the accurate estimation of maize fPAR using remote sensing techniques.

The Light detection and ranging (LiDAR) sensor is used for perceiving the environment of an autonomous vehicle. LiDAR data or point cloud is processed to get the obstacles and their speed around an autonomous vehicle. Based on the information retrieved from LiDAR data and the data from other sensors, real-time decisions are taken for the proper navigation. Hence, the time taken in LiDAR data processing should be minimized. One of the important steps for LiDAR data processing is the segmentation of the obstacles. In this paper, we present a quantitative comparison between two different approaches for point cloud segmentation, Euclidean distance-based Cluster Extraction and Cylindrical range image-based method. Based on the simulation performed on ROS (Robot Operating System) platform, we found that the second method is much faster as compared to the first method. In addition to that, the second method can perform proper segmentation at a larger distance from the sensor.

Automatic building extraction is an important topic for many applications such as urban planning, disaster management, 3D building modeling and updating GIS databases. Its approaches mainly depend on two data sources: light detection and ranging (LiDAR) point cloud and aerial imagery both of which have advantages and disadvantages of their own. In this study, in order to benefit from the advantages of each data sources, LiDAR and image data combined together. And then, the building boundaries were extracted with the automated active contour algorithm implemented in MATLAB. Active contour algorithm uses initial contour positions to segment an object in the image. Initial contour positions were detected without user interaction by a series of image enhancements, band ratio and morphological operations. Four test areas with varying building and background levels of detail were selected from ISPRS’s benchmark Vaihingen and Istanbul datasets. Vegetation and shadows were removed from all the datasets by band ratio to improve segmentation quality. Subsequently, LiDAR point cloud data was converted to raster format and added to the aerial imagery as an extra band. Resulting merged image and initial contour positions were given to the active contour algorithm to extract building boundaries. In order to compare the contribution of LiDAR to the proposed method, the boundaries of the buildings were extracted from the input image before and after adding LiDAR data to the image as a layer. Finally extracted building boundaries were smoothed by the Awrangjeb (Int J Remote Sen 37(3): 551–579. https://doi.org/10.1080/01431161.2015.1131868 , 2016) boundary regularization algorithm. Correctness (Corr), completeness (Comp) and accuracy (Q) metrics were used to assess accuracy of segmented building boundaries by comparing extracted building boundaries with manually digitized building boundaries. Proposed approach shows the promising results with over 93% correctness, 92% completeness and 89% quality.

This paper presents a model to predict the GDOP value (Geometric Dilution of Precision) and the probability of its occurrence at a point in space and time using airborne LiDAR (Light Detection and Ranging) data and the ultra-rapid product (URP) available from the International GPS Service. LiDAR data help to classify the terrain around a GPS (Global Positioning System) receiver into categories such as ground, opaque objects, translucent objects and transparent regions as per their response to the transmission of GPS signal. Through field experiments it is established that URP can be used satisfactorily to determine GDOP. Further experiments have shown that the translucent objects (mainly trees here) lower the GDOP quality as they obstruct the GPS signal. LiDAR data density on trees is used as a measure of the translucency of trees to the GPS signal. Through GPS observations taken in field a relationship has been established between LiDAR data density on trees and the probability that a satellite which is behind the tree is visible at the GPS receiver. A model is presented which, for all possible combinations of visible satellites, computes the GDOP value along with the probability of occurrence of this GDOP. A few results are presented to show the performance of the model developed and its possible application in location based queries.

Foresters are increasingly interested in remote sensing data because they provide an overview of landscape conditions, which is impractical with field sample data alone. Light Detection and Ranging (LiDAR) provides exceptional spatial detail of forest structure, but difficulties in processing LiDAR data have limited their application beyond the research community. Another obstacle to operational use of LiDAR data has been the high cost of data collection. Our objectives in this study were to summarize, at the stand level, both LiDAR- and Landsat (satellite)-based predictions of some common structural and volume attributes and to compare the cost of obtaining such summaries with those obtained through traditional stand exams. We found that the accuracy and cost of a LiDAR-based inventory summarized at the stand level was comparable to traditional stand exams for structural attributes. However, the LiDAR data were able to provide information across a much larger area than the stand exams alone.

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

Automated feature extraction based on prototypes is only partially successful when applied to remotely sensed imagery of natural scenes due to the complexity and unpredictability of the shape and geometry of natural features. Here, a new method is developed for extracting the locations of treetops by applying GIS (Geographical Information System) overlay techniques and morphological functions to high spatial resolution airborne imagery. This method is based on the geometrical and spatial properties of tree crowns. Airborne data of the study site in the New Forest, UK included colour aerial photographs, LIDAR (Light Detection And Ranging) and ATM (Airborne Thematic Mapper) imagery. A DEM (Digital Elevation Model) was generated from LIDAR data and then subtracted from the original LIDAR image to create a Canopy Height Model (CHM). A set of procedures using image contouring and the manipulation of the resulting polygons was implemented to extract treetops from the aerial photographs and the CHM. Criteria were developed and threshold values were set using a supervised approach for the acceptance or rejection of features based on field knowledge. Tree species were mapped by classifying the ATM data and these data were co‐registered with the treetop layer. For broadleaved deciduous plantations the success of treetop extraction using aerial photographs was 91%, but was much lower using LIDAR data. For semi‐natural forests, the LIDAR produced better results than the aerial photographs with a success of 80%, which was considered high, given the complexity of these uneven aged stands. The methodology presented here is easy to apply as it is implemented within a GIS and the final product is an accurate map with information about the location, height and species of each tree.

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

The Surface Water and Ocean Topography (SWOT) altimeter will perform a continuous global water survey with unprecedented resolution and accuracy across its 3-year mission. After being launched on December 16th 2022 with a SpaceX Falcon 9 rocket from Vandenberg in California, it was successfully commissioned followed by a Calibration and Validation (Cal/Val) phase that lasted approximately between April and July 2023. During this period, numerous in-situ measurements were performed across the globe to assess the altimeter&amp;#8217;s performance. Airborne Light Detection And Ranging (LiDAR) campaigns were conducted off the coasts of Normandy, France as part of other measurements in this region. We carried out 4 different missions, 2 in May and 2 in June, using a Leica ALS 60 airborne sensor aboard 2 different planes, a Piper Navajo and a Swearingen Fairchild Merlin. The flight plans were designed below the SWOT Ka-band Radar Interferometer (KaRIn) along and across the 1-day fast sampling ground track. Ground Control Points (GCP) were acquired under the LiDAR coverage, close to the city of Cherbourg. The plane&amp;#8217;s trajectory was processed using CNES GINS software, using the integer Precise Point Positioning (iPPP) mode, resulting in centimetric antenna phase positioning. LiDAR data were calibrated using the GCPs with a millimetric average accuracy. First results between SWOT data and airborne LiDAR indicate very good consistency. Indeed, the differences between the SWOT LR 2 km pre-cal product and LiDAR data, averaged over a similar 2 km grid, gives centimetric standard deviation.

With the increasing number of remote sensing (RS) data sources, the joint utilization of multimodal data in Earth observation tasks has become a crucial research topic. As a typical representative of RS data, hyperspectral images (HSIs) provide accurate spectral information, while rich elevation information can be obtained from light detection and ranging (LiDAR) data. However, due to the significant differences in multimodal heterogeneous features, how to efficiently fuse HSI and LiDAR data remains one of the challenges faced by existing research. In addition, the edge contour information of images is not fully considered by existing methods, which can easily lead to performance bottlenecks. Thus, a joint classification network of HSI and LiDAR data based on Mamba (HLMamba) is proposed. Specifically, a gradient joint algorithm (GJA) is first performed on LiDAR data to obtain the edge contour data of the land distribution. Subsequently, a multimodal feature extraction module (MFEM) was proposed to capture the semantic features of HSI, LiDAR, and edge contour data. Then, to efficiently fuse multimodal features, a novel deep learning (DL) framework called Mamba, was introduced, and a multimodal Mamba fusion module (MMFM) was constructed. By efficiently modeling the long-distance dependencies of multimodal sequences, the MMFM can better explore the internal features of multimodal data and the interrelationships between modalities, thereby enhancing fusion performance. Finally, to validate the effectiveness of HLMamba, a series of experiments were conducted on three common HSI and LiDAR datasets. The results indicate that HLMamba has superior classification performance compared to other state-of-the-art DL methods. The source code of the proposed method will be available publicly at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Dilingliao/HLMamba</uri>.

Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction