Photogrammetric Point Clouds Research Articles

LFEA-Net: semantic segmentation for urban point cloud scene via local feature extraction and aggregation

Considering the increasing prominence of 3D real city construction technology, 3D urban point cloud scene data merit further investigation. However, achieving finegrained semantic segmentation of urban scenes remains highly challenging due to the natural orderlessness and unstructured nature of acquired point clouds, along with their large-scale points and non-uniform distributions. In this study, we present LFEA-Net, a novel neural network specifically designed for semantic segmentation of large-scale urban point cloud scenes. The network comprises two main components: (1) The local feature extraction (LFE) module, which fully exploits local spatial, color and semantic information to enhance and preserve crucial information, consisting of bilateral feature encoding unit and multidimensional feature encoding unit. (2) The local feature aggregation (LFA) module, designed to bridge the semantic gap between local information and emphasize both local significant features and the entire local neighbor, consisting of soft cross operation and united pooling operation. We have evaluated the performance of LFEA-Net with state-of-the-art networks using the photogrammetric point cloud dataset SensatUrban, achieving 61.6 of mIoU score. The results demonstrate the superior efficacy of LFEA-Net in accurately segmenting and classifying large-scale urban point cloud scenes, highlighting its potential to advance environmental information perception.

Automatic building footprint extraction from photogrammetric and LiDAR point clouds using a novel improved-Octree approach

Extracting building footprints from optical data is a time-consuming process. Automatic extraction of building footprints from point clouds is a challenging problem in terms of geometric irregularities, noisy points, points density, and accuracy. The aim of this paper is to automatically extract and regularize building footprints using point clouds with a new approach called Improved Octree (I-Octree) by modifying the Octree method. The method consists of the separation of ground and above ground objects from the point cloud by Simple Morphological Filter (SMRF), the removing noisy points from point cloud with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, the automatic extraction of building footprints by I-Octree, and the regularization of the building footprints with Automatic Building Outline Regularization (ABORE) method. The proposed approach was implemented on photogrammetric and Light Detection and Ranging (LiDAR) in four test areas. Ground truth maps were utilized as reference data for accuracy analysis by using pixel-based accuracy method. The accuracy results were above 90 % for the photogrammetric point clouds and above 97 % for the LiDAR point cloud. It was proven that the proposed approach can extract and regularize the selected buildings with high accuracy compared the studies in literature. In conclusion, it was demonstrated that the proposed approach enables the automatic extraction and regularization of building footprints from point clouds. Consequently, the map production process with point cloud data is facilitated to be both more efficient and rapid, and the results confirm the high efficacy of the proposed approach.

A CRITICAL ANALYSIS OF INTERNAL RELIABILITY FOR UNCERTAINTY QUANTIFICATION OF DENSE IMAGE MATCHING IN MULTI-VIEW STEREO

Abstract. Nowadays, photogrammetrically derived point clouds are widely used in many civilian applications due to their low cost and flexibility in acquisition. Typically, photogrammetric point clouds are assessed through reference data such as LiDAR point clouds. However, when reference data are not available, the assessment of photogrammetric point clouds may be challenging. Since these point clouds are algorithmically derived, their accuracies and precisions are highly varying with the camera networks, scene complexity, and dense image matching (DIM) algorithms, and there is no standard error metric to determine per-point errors. The theory of internal reliability of camera networks has been well studied through first-order error estimation of Bundle Adjustment (BA), which is used to understand the errors of 3D points assuming known measurement errors. However, the measurement errors of the DIM algorithms are intricate to an extent that every single point may have its error function determined by factors such as pixel intensity, texture entropy, and surface smoothness. Despite the complexity, there exist a few common metrics that may aid the process of estimating the posterior reliability of the derived points, especially in a multi-view stereo (MVS) setup when redundancies are present. In this paper, by using an aerial oblique photogrammetric block with LiDAR reference data, we analyze several internal matching metrics within a common MVS framework, including statistics in ray convergence, intersection angles, DIM energy, etc. We associate these metrics to the per-point errors evaluated through LiDAR reference data and discuss their potential contributions in estimating internal reliabilities of point clouds derived from DIM algorithms. The experimental results show that ray convergence and DIM energy are relevant indicators for the accuracy of the generated point clouds. Initial investigation shows that these two indicators could be further utilized to infer the measurement errors without reference data, which could potentially estimate the reliabilities of point clouds through error propagation.

Unsupervised Methodology for Large-Scale Tree Seedling Mapping in Diverse Forestry Settings Using UAV-Based RGB Imagery

Mapping and monitoring tree seedlings is essential for reforestation and restoration efforts. However, achieving this on a large scale, especially during the initial stages of growth, when seedlings are small and lack distinct morphological features, can be challenging. An accurate, reliable, and efficient method that detects seedlings using unmanned aerial vehicles (UAVs) could significantly reduce survey costs. In this study, we used an unsupervised approach to map young conifer seedlings utilising spatial, spectral, and structural information from UAV digital aerial photogrammetric (UAV-DAP) point clouds. We tested our method across eight trial stands of radiata pine with a wide height range (0.4–6 m) that comprised a total of ca. 100 ha and spanned diverse site conditions. Using this method, seedling detection was excellent, with an overall precision, sensitivity, and F1 score of 95.2%, 98.0%, and 96.6%, respectively. Our findings demonstrated the importance of combining spatial, spectral, and structural metrics for seedling detection. While spectral and structural metrics efficiently filtered out non-vegetation objects and weeds, they struggled to differentiate planted seedlings from regenerating ones due to their similar characteristics, resulting in a large number of false positives. The inclusion of a row segment detection algorithm overcame this limitation and successfully identified most regenerating seedlings, leading to a significant reduction in false positives and an improvement in overall detection accuracy. Our method generated vector files containing seedling positions and key structural characteristics (seedling height, crown dimensions), offering valuable outputs for precision management. This automated pipeline requires fewer resources and user inputs compared to manual annotations or supervised techniques, making it a rapid, cost-effective, and scalable solution which is applicable without extensive training data. While serving as primarily a standalone tool for assessing forestry projects, the proposed method can also complement supervised seedling detection methods like machine learning, i.e., by supplementing training datasets.

Classification of rock slope cavernous weathering on UAV photogrammetric point clouds: The example of Hegra (UNESCO World Heritage Site, Kingdom of Saudi Arabia)

The analysis of three-dimensional point cloud data is becoming one of the most used approaches to assess instabilities processes affecting rock slopes. With the increased collection of point cloud data, there is an increasing demand for rapid computational point cloud segmentation techniques to format data for rock fall risk analysis. However, there is a scarcity of semantic segmentation research focusing on geological data and the practical issues they pose. In this study, we propose a novel application to automatically define the spatial extent and intensity of weathered rock slope areas. The studied rock slopes are in Hegra Archaeological Site (al-Hijr / Madā ͐ in Ṣāliḥ), the first UNESCO World Heritage area inscribed in the Kingdom of Saudi Arabia (KSA) in 2008. Here, cavernous weathering processes, with honeycombs and tafoni features, affect the carved rock slope and increase the rockfall hazard. With the spread and improvement of close-range methods, such as Terrestrial Laser Scanner (TLS) and Unmanned Aerial Vehicle Digital Photogrammetry (UAV-DP), it is possible to obtain fast and accurate high-resolution point cloud data of the studied scenario, i.e., rock mass. This paper presents the automated mapping results obtained by a combination of explainable artificial intelligence (XAI) algorithms and three-dimensional geometric analysis of a point cloud using the geometric feature tools available for free on CloudCompare (CC) open-source software. Three-dimensional data were obtained using the UAV-DP technique. The proposed workflow allows, using random forest modelling (RF), to quantify the cavernous weathering extent and estimate the relative intensity on the rock slope, faster than using traditional time-consuming manual segmentation processes. The RF model was able to accurately forecast the cavernous weathering regions (accuracy of up to 85%), with a slight propensity to underpredict, as indicated by the somewhat low recall (∼ 63%), and a limited number of false positives, as indicated by the high precision (up to 84%). This approach is easily reproducible at low cost in terms of computational, human, and financial resources. The results highlight the reliability of the proposed method for the preliminary assessment of cavernous erosional features with the aim of future assessment, monitoring, and deeper analysis of rock-carved slopes.

Building footprint extraction from aerial photogrammetric point cloud data using its geometric features

Unmanned aerial vehicles (UAVs) can quickly acquire high-resolution datasets. Generally, UAVs or drones have high-resolution optical cameras onboard to obtain aerial images. These images are processed to provide various output products, including point cloud, digital surface model (DSM), digital terrain model (DTM), and ortho-mosaiced image. This study uses point cloud data obtained from UAV data processing to extract buildings automatically. It utilizes the geometric features obtainable from the point cloud data in a defined neighbourhood to classify the point cloud data. Normalized DSM (nDSM) is also used as an input to identify above-ground features more accurately. Random Forest (RF) algorithm has been used to classify the point cloud data into different classes available in the dataset. After classification, buildings are separated from the point cloud data, and K-Means clustering is performed to segregate different building clusters. These clusters are rasterized, and morphological operations are applied to refine the building edges. Then the boundaries of the building clusters are identified to provide the vector data. Accuracy assessment of the automatically extracted shapes is done by comparing their area, perimeter, and centroid location to the reference building polygons generated through the total station survey. The methodology is tested over the dataset acquired through UAV. An open-source GUI (graphical user interface) based tool has been developed in Python to extract the vectorized building shapes from photogrammetric point cloud data and compute areas automatically. It will reduce manual interventions significantly and benefit many users, professionals and researchers.

Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data

Automatic damage assessment by analysing UAV-derived 3D point clouds provides fast information on the damage situation after an earthquake. However, the assessment of different damage grades is challenging given the variety in damage characteristics and limited transferability of methods to other geographic regions or data sources. We present a novel change-based approach to automatically assess multi-class building damage from real-world point clouds using a machine learning model trained on virtual laser scanning (VLS) data. Therein, we (1) identify object-specific point cloud-based change features, (2) extract changed building parts using k-means clustering, (3) train a random forest machine learning model with VLS data based on object-specific change features, and (4) use the classifier to assess building damage in real-world photogrammetric point clouds. We evaluate the classifier with respect to its capacity to classify three damage grades (heavy, extreme, destruction) in pre-event and post-event point clouds of an earthquake in L’Aquila (Italy). Using object-specific change features derived from bi-temporal point clouds, our approach is transferable with respect to multi-source input point clouds used for model training (VLS) and application (real-world photogrammetry). We further achieve geographic transferability by using simulated training data which characterises damage grades across different geographic regions. The model yields high multi-target classification accuracies (overall accuracy: 92.0%–95.1%). Classification performance improves only slightly when using real-world region-specific training data (< 3% higher overall accuracies). We consider our approach especially relevant for applications where timely information on the damage situation is required and sufficient real-world training data is not available.

COMBINED 360° VIDEO AND UAV RECONSTRUCTION OF SMALL-TOWN HISTORIC CITY CENTERS

Abstract. Small-town historic urban environments increasingly face different pressures that may prevent their future sustainability. To promote a recovery plan for those centers, an interdisciplinary methodology is needed to orient future actions toward innovative solutions. However, the development of such a plan needs the definition of a dedicated methodology to drive the definition of recovery actions. A key role is the possibility of low-cost methods for mapping and classifying the built heritage. Both ground and aerial (UAV) solutions can be used to create 3D city models for further classification and analysis. However, integrating those two data in small city centers poses specific challenges. This paper presents a combined approach using ground and aerial (UAV) data to comprehensively reconstruct a part of a small-town city center. Ground data are video acquired with 360° (spherical) cameras. Previous works proved 360° videos can reconstruct with good metric accuracy and complete building façades of historic city centers. The combined reconstruction is tested in the so-called “Casa Teatro”, an abandoned building in the city center of Vogogna, a small town in the Ossola Valley (Northern Italy). The results of the combined image orientation are validated by using ground truth data (Check Points) acquired with a total station. In contrast, the final 3D model reconstruction results are compared with a laser scanning acquisition of the building to evaluate the accuracy and completeness of the photogrammetric point cloud. In addition, matching among 360° frames and UAV frames is studied by analysing connectivity graphs.

Integration of UAV and GF-2 Optical Data for Estimating Aboveground Biomass in Spruce Plantations in Qinghai, China

More refined and economical aboveground biomass (AGB) monitoring techniques are needed because of the growing significance of spruce plantations in climate change mitigation programs. Due to the challenges of conducting field surveys, such as the potential inaccessibility and high cost, this study proposes a convenient and efficient alternative to traditional field surveys that integrates Gaofen-2 (GF-2) satellite optical images and unmanned aerial vehicle (UAV)-acquired optical and point cloud data to provide a reliable and refined estimation of the aboveground biomass (AGB) in spruce plantations. The feasibility of using data produced from the semiautomatic processing of UAV-based images and photogrammetric point clouds to replace conventional field surveys of sample plots in a young spruce plantation was evaluated. The AGB in 53 sample plots was estimated using data extracted from the UAV imagery. The UAV plot data and GF-2 optical data were used in four regression models to estimate the AGB in the study area. The coefficient of determination (R2), root-mean-square error (RMSE), mean percent standard error (MPSE), and Lin’s concordance correlation coefficient (LCCC) were calculated through five-fold cross-validation and stratified random sampling to evaluate the models’ efficacies. In the end, the most accurate model was used to generate the spatial distribution map of the AGB. The results revealed the following: (1) the individual-tree height (R2 = 0.90) and crown diameter (R2 = 0.74) extracted from UAV data were accurate enough to replace field surveys used to obtain the AGB at the plot levels; (2) the random forest (RF) model (R2 = 0.86; RMSE = 1.75 t/ha; MPSE = 15.75%; LCCC = 0.91) outperformed the ordinary least-squares (OLS) model (R2 = 0.68; RMSE = 2.49 t/ha; MPSE = 22.94%; LCCC = 0.81), artificial neural network (ANN) model (R2 = 0.67; RMSE = 2.54 t/ha; MPSE = 21.48%; LCCC = 0.80), and support vector machine (SVM) model (R2 = 0.60; RMSE = 2.84 t/ha; MPSE = 31.73%; LCCC = 0.76) in terms of the estimation accuracy; (3) an AGB map generated by the random forest model was in good agreement with field surveys and the age of the spruce plantations. Therefore, the method proposed in this study can be used as a refined and cost-effective way to estimate the AGB in young spruce plantations.

A PERFORMANCE COMPARISON BETWEEN SEGNET AND DEEPLABV3+ ON THE SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS

Abstract. During the last decade, the use of machine and deep learning tools to support 3D semantic segmentation of point clouds remarkably increased and their impressive results have led to the application of such methods to the semantic modeling of heritage buildings. Nevertheless, a standard procedure to deal with such problem is still missing, and several significant challenges, caused by the complexity of heritage building scenario, have still to be faced. This paper aims at comparing the overall performance of two convolutional neural network architectures, named SegNet and Deeplabv3+, for the semantic segmentation of heritage point clouds throughout a multiview approach. More specifically, the two architectures have been tested to obtain 2D segmentation maps of the related photogrammetric images of the buildings, and then the output maps have been projected to the photogrammetric point cloud by means of the interior and exterior camera parameters. Experiments to test the effectiveness of the proposed approach have been conducted on the case study of Spedale del Ceppo in Pistoia, Italy. Despite the results shown a remarkable performance of both the architectures, Deeplabv3+ outperformed SegNet in terms of accuracy, memory consumption and training time.

UAV-derived photogrammetric point clouds and multispectral indices for fuel estimation in Mediterranean forests

Sensors attached to unmanned aerial vehicles (UAVs) allow estimating a large number of forest attributes related to forest fuels. This study assesses photogrammetric point clouds and multispectral indices obtained from a fixed-wing UAV for the classification of Prometheus fuel types in 82 forest plots in Aragón (NE Spain). Images captured by an RGB camera and a multispectral sensor allowed generating high density photogrammetric point clouds (RGB: 3000 points/m2; multispectral: 85 points/m2), which were normalized using alternatively a Digital Elevation Model (DEM) of 0.5, 1, and 2 m resolution. A set of structural and textural variables were derived from the normalized point cloud heights, and for the latter, the gray-level co-occurrence matrix (GLCM) approach was used. Multispectral images were also used to create seven spectral vegetation indices. The most relevant structural, textural, and spectral variables to introduce into the fuel types classification models were selected using Dunn's test, which included: the vegetation height at the 50th percentile, the coefficient of variation of the heights, the percentage of returns above 4 m, the mean textural dissimilarity, and the mean of the Green Chlorophyll Index. Three different data samples were introduced in the models: i) the relevant structural and textural variables from the RGB camera (RGB data sample); ii) the relevant structural, textural, and spectral variables from the multispectral sensor (MS data sample); and iii) the relevant structural and textural variables from the RGB camera plus the relevant spectral variable from the multispectral sensor (integrated data sample). After comparing three machine learning classification techniques (Random Forest, and Linear and Radial Support Vector Machine), the best results were obtained with Random Forest with k-fold cross-validation (k-10) and the integrated data sample with normalized point clouds at 0.5 m DEM resolution (overall accuracy = 71%). The variables successfully identified the Prometheus main fire carriers (i.e., shrubs or trees) and confusions were mainly located within the fuel types of the same dominant stratum, especially in fuel types 3 and 6. These results demonstrate the ability of UAV imagery to classify forest fuels in Mediterranean environments when RGB and multispectral data are combined.

Robust registration of aerial and close‐range photogrammetric point clouds using visual context features and scale consistency

AbstractPoint cloud registration is of great significance to the reconstruction of high‐precision 3D city models. There are some challenges when aligning aerial and close‐range photogrammetric point clouds, such as huge view differences caused by the different sights of the sensors, massive noisy points due to the error in dense matching, and scale uncertainty since there are no control points for absolute orientation. To achieve complementary advantages of aerial and close‐range point clouds, in this paper, a robust cross‐source point clouds registration method is proposed using image visual context features and scale consistency. First, a cross‐view image matching method based on image visual context features is proposed to obtain corresponding points. Second, to overcome the challenges of noisy points and scale differences, an outlier filtering method is designed based on scale consistency. Finally, the dual quaternions model considering the scale factor is introduced to solve the spatial transformation model. To analyze the feasibility of this method qualitatively and quantitatively, experiments are conducted using the public scene dataset of Dortmund, Germany, and the scene dataset of Zhengzhou City, China. Three kinds of cross‐source point clouds registration experiments are conducted in this paper, including aerial and close‐range point clouds registration in Dortmund, and aerial and ground point clouds registrations in both Dortmund and Zhengzhou. The chamfer distances of the three sets of experiments are 4.48 m, 5.97 m and 4.78 m, respectively. The ablation study shows that the outlier filtering method and the dual quaternions model improve the accuracy by at least 14% and 25%, respectively. The experiments demonstrate that the method accomplishes the cross‐source point clouds registration in large‐scale scenes accurately and efficiently, providing a solid foundation for subsequent fine 3D reconstruction.

Canopy and surface fuel estimations using RPAS and ground-based point clouds

Abstract Forest management activities intended to reduce wildfire risk rely on accurate characterizations of the amount and arrangement of canopy and surface fuels. Metrics that describe these fuels are typically estimated with various systems that transform plot-level field data into metrics that can be used within fire behaviour models. Remote sensing data have long been used to estimate these metrics across large spatial scales, but more advanced, high-density point clouds have the potential to estimate these metrics with higher accuracy. This study collected LiDAR and digital aerial photogrammetric (DAP) point clouds from a remotely piloted aerial system (RPAS), as well as mobile laser scanning (MLS) point clouds from a mobile ground-based system, and compared their ability to estimate fuel metrics. This involved the extraction of predictor variables from each point cloud, of which small subsets were used to estimate various fuel metrics. These included six overstory canopy metrics (stand height, canopy cover, tree density, canopy fuel load, canopy bulk density and canopy base height), three diameter at breast height (DBH)–related metrics (stand density index, basal area and quadratic mean diameter) and three surface fuel metrics (total woody debris (TWD), coarse woody debris (CWD) and fine woody debris (FWD)). Overall, canopy metrics were estimated most accurately by the RPAS LiDAR models, although none of the point clouds were able to accurately estimate DBH-related metrics. For the other six canopy metrics, RPAS LiDAR models had an average R2 value of 0.70; DAP – 0.63 and MLS – 0.63. CWD (&amp;gt;7 cm) and TWD loads were estimated most accurately by the MLS models (average R2 values – 0.70), followed by the RPAS LiDAR – 0.38 and DAP – 0.13. None of these models were able to accurately estimate FWD loads (≤7 cm in diameter), with the three types of point clouds having a maximum R2 value of 0.08. Overall, this research shows the relative ability of three types of high-density point clouds to estimate metrics relevant for fire behaviour modeling.

A co-learning method to utilize optical images and photogrammetric point clouds for building extraction

A co-learning method to utilize optical images and photogrammetric point clouds for building extraction

Tree Branch Skeleton Extraction from Drone-Based Photogrammetric Point Cloud

Calculating the complex 3D traits of trees such as branch structure using drones/unmanned aerial vehicles (UAVs) with onboard RGB cameras is challenging because extracting branch skeletons from such image-generated sparse point clouds remains difficult. This paper proposes a skeleton extraction algorithm for the sparse point cloud generated by UAV RGB images with photogrammetry. We conducted a comparison experiment by flying a UAV from two altitudes (50 m and 20 m) above a university orchard with several fruit tree species and developed three metrics, namely the F1-score of bifurcation point (FBP), the F1-score of end point (FEP), and the Hausdorff distance (HD) to evaluate the performance of the proposed algorithm. The results show that the average values of FBP, FEP, and HD for the point cloud of fruit tree branches collected at 50 m altitude were 64.15%, 69.94%, and 0.0699, respectively, and those at 20 m were 83.24%, 84.66%, and 0.0474, respectively. This paper provides a branch skeleton extraction method for low-cost 3D digital management of orchards, which can effectively extract the main skeleton from the sparse fruit tree branch point cloud, can assist in analyzing the growth state of different types of fruit trees, and has certain practical application value in the management of orchards.

AUTOMATIC EXTRACTION OF CONTROL POINTS FROM 3D LIDAR MOBILE MAPPING AND UAV IMAGERY FOR AERIAL TRIANGULATION

Abstract. Installing targets and measuring them as ground control points (GCPs) are time consuming and cost inefficient tasks in a UAV photogrammetry project. This research aims to automatically extract GCPs from 3D LiDAR mobile mapping system (L-MMS) measurements and UAV imagery to perform aerial triangulation in a UAV photogrammetric network. The L-MMS allows to acquire 3D point clouds of an urban environment including floors and facades of buildings with an accuracy of a few centimetres. Integration of UAV imagery, as complementary information enables to reduce the acquisition time of measurement as well as increasing the automation level in a production line. Therefore, a higher quality measurements and more diverse products are obtained. This research hypothesises that the spatial accuracy of the L-MMS is higher than that of the UAV photogrammetric point clouds. The tie points are extracted from the UAV imagery based on the well-known SIFT method, and then matched. The structure from motion (SfM) algorithm is applied to estimate the 3D object coordinates of the matched tie points. Rigid registration is carried out between the point clouds obtained from the L-MMS and the SfM. For each tie point extracted from the SfM point clouds, their corresponding neighbouring points are selected from the L-MMS point clouds, and then a plane is fitted and then a tie point was projected on the plane, and this is how the LiDAR-based control points (LCPs) are calculated. The re-projection error of the analyses carried out on a test data sets of the Glian area in Iran show a half pixel size accuracy standing for a few centimetres range accuracy. Finally, a significant increasing of speed up in survey operations besides improving the spatial accuracy of the extracted LCPs are achieved.

A COMPARISON THROUGH TREE EXTRACTION IN IMAGE-SPACE AND OBJECT-SPACE

Abstract. In various studies trees have been extracted and their conditions have been examined through different detection algorithms from two main data sources including (a) point cloud and (b) raster data. The output of tree extraction is the input of the next processing steps, and the importance of these outputs is proved more than before. Tree Extraction (TE) has many applications in biomass estimation, CHM extraction, etc. All of which require high accuracy and the correct position of the trees. therefore, in this study, a comparison between tree extraction algorithms in two common sources of data has been conducted. As for the raster data, all bands are first co-registered. Afterward, the trees are separated from the background by using image processing techniques such as changing the image color space and weighted averaging on different bands. Finally, TE algorithms such as watershed segmentation, valley following, local maxima, and image binarization were applied. As for the point cloud data, TE can be conducted in the object space to compensate for the methods used in the raster space with object detection algorithms e.g., the coherence between the two trees, etc. which have been discussed in detail in this paper. In the object space, three algorithms, region-based, surface normal, and Euclidean segmentation, were implemented and discussed on the same raster data set in the photogrammetric point cloud. The results show the higher accuracy of the region-based algorithm in object-space by more than 26% in comparison with the valley following algorithm in image space.

Reconstruction of LoD-2 Building Models Guided by Façade Structures from Oblique Photogrammetric Point Cloud

Due to the façade visibility, intuitive expression, and multi-view redundancy, oblique photogrammetry can provide optional data for large-scale urban LoD-2 reconstruction. However, the inherent noise in oblique photogrammetric point cloud resulting from the image-dense matching limits further model reconstruction applications. Thus, this paper proposes a novel method for the efficient reconstruction of LoD-2 building models guided by façade structures from an oblique photogrammetric point cloud. First, a building planar layout is constructed combined with footprint data and the vertical planes of the building based on spatial consistency constraints. The cells in the planar layout represent roof structures with a distinct altitude difference. Then, we introduce regularity constraints and a binary integer programming model to abstract the façade with the best-fitting monotonic regularized profiles. Combined with the planar layout and regularized profiles, a 2D building topology is constructed. Finally, the vertices of building roof facets can be derived from the 2D building topology, thus generating a LoD-2 building model. Experimental results using real datasets indicate that the proposed method can generate reliable reconstruction results compared with two state-of-the-art methods.

Tree attribute assessment in urban greenwood using ground-based LiDAR and multiseasonal aerial photography data

Advances in LiDAR and unmanned aerial vehicle technology have made high-resolution data available, which can be used for individual tree detection and assessing tree attributes. The accuracy of these assessments is still not clear for stands with high tree species diversity as well as leaf-off and leaf-on conditions. The aim of this study was to assess the quality of tree top detection and individual tree heights extracted from photogrammetric point clouds and canopy height models as well as ground-based LiDAR clouds in mixed and coniferous forest stands depending on the phenological stage. The study has been carried out in the Botanical Garden of the Petrozavodsk State University (Republic of Karelia, Russia). Four flight missions (in 2019–2021) using Phantom 4 Pro quadcopter were conducted in the arboretum (&gt; 200 tree species) during periods of leafless, leaf biomass growth, full foliage and autumn leaf colouration. A single ground-based laser scanning was performed using a Leica BLK 360. Multiseasonal ultra-high resolution orthophoto mosaics (1.1–2.8 cm/pixel), photogrammetric point clouds (average density is 4200 points/m2), as well as LiDAR clouds (11 600 points/m2) were obtained. Further analysis was performed on three sites differing in tree species composition, tree density and site area. Tree tops were automatically detected from photogrammetric point clouds and their heights were estimated using R environment software. We found that most of the trees (78.9%) were correctly detected by algorithms based on photogrammetric data collected in periods of full foliage and autumn colouration. We also found that the number of false positive (FP) and false negative (FN) cases increased with decreasing in green biomass on deciduous trees. Compared with an average value, tree detection quality increased by 9.4% for coniferous trees with cone-shaped crowns (Abies sibirica, A. balsamea, A. fraseri, Picea abies, P. pungens, P. omorika, Pseudotsuga menziesii, Larix sibirica) regardless of the tree density, and tree detection quality decreased by 10% for coniferous trees with an ellipsoidal-shaped crowns (e.g. Thuja occidentalis, genus Pinus) or in cases for broad-leaved trees with high tree density. The lowest value of tree detection quality (F = 0.49) was found for the leafless period. High values (F = 0.84) obtained for periods of full foliage and autumn colouration indicates that tree detection quality was well in general. For the biomass growth period, this value (F = 0.69) also indicates a high quality of tree detection results. We also found that tree heights estimated using photogrammetric data well matched with tree heights measured on LiDAR clouds (R2 = 0.99). The highest accuracy was obtained for coniferous trees with cone-shaped crowns. We also estimated the height increments of different tree species between 2019 and 2021 based on photogrammetric point clouds. The highest annual height increment was obtained for Pinus sibirica (52 cm), and the lowest for Pseudotsuga menziesii (32 cm). Overall, our results have shown the potential to use photogrammetric and LiDAR data for tree mapping and estimating tree attributes in multi-species forest stands of arboretums or urban parks, as well as in natural forests.

A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR

ABSTRACT Surface roughness plays an important role in microwave remote sensing. In the agricultural domain, surface roughness is crucial for soil moisture retrieval methods that use electromagnetic surface scattering or microwave radiative transfer models. Therefore, improved characterization of Soil Surface Roughness (SSR) is of considerable importance. In this study, three approaches, including a standard pin profiler, a LiDAR point cloud generated from an iPhone 12 Pro, and a Structure from Motion (SfM) photogrammetric point cloud, were applied over 24 surface profiles with different roughness variations to measure surface roughness. The objective of this study was to evaluate the capability of smartphone-based LiDAR technology to measure surface roughness parameters and compare the results of this technique with the more common approaches. Results showed that the iPhone LiDAR technology, when point cloud data is captured in a fine-resolution mode, has a significant correlation with SfM photogrammetry (R 2 = 0.70) and a relatively close agreement with pin profiler (R 2 = 0.60). However, this accuracy tends to be greater for random surfaces and rough profiles with row structure orientations. The results of this study confirm that smartphone-based LiDAR can be used as a cost-effective, fast, and time-efficient alternative tool for measuring surface roughness, especially for rough, wide, and inaccessible areas.