With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to facilitate research ...in 3D computer vision. However, existing datasets either cover relatively small areas or have limited semantic annotations. Fine-grained understanding of urban-scale 3D scenes is still in its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km
2
. Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset. In addition to the more commonly encountered categories such as road and vegetation, urban-level categories including rail, bridge, and river are also included in our dataset. Based on this dataset, we further build a benchmark to evaluate the performance of state-of-the-art segmentation algorithms. In particular, we provide a comprehensive analysis and identify several key challenges limiting urban-scale point cloud understanding. The dataset is available at
http://point-cloud-analysis.cs.ox.ac.uk/
.
The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, ...satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field.
•Monitoring selective logging is difficult because only a few trees are felled.•We investigated the potential of a lightweight UAV to detect selective logging.•Repeatedly acquired digital aerial ...photographs capture selective logging accurately.•A lightweight UAV is a cost-effective approach to quantify selective logging.
Selective logging is one of the factors contributing to deforestation and forest degradation in tropical forests. A low-cost methodology to monitor selective logging is clearly required. However, this poses a challenge because only a few trees are felled at a given time. Here, we investigate the potential of using repeatedly acquired digital aerial photographs (DAPs) from a lightweight unmanned aerial vehicle (UAV) to detect selective logging in tropical forests in Myanmar. Selective logging was conducted within two 9-ha plots. DAPs were acquired immediately before and after selective logging using a lightweight UAV in this case study. The aboveground biomass (AGB) change related to selective logging was regressed against metrics expressing forest changes calculated at a 0.25-ha resolution from a photogrammetric point cloud created using the DAPs before and after selective logging. The root-mean-square error and coefficient of determination were 0.77 and 9.32 Mg/ha, respectively. This study demonstrates that repeated DAPs taken from a lightweight UAV can be used to estimate changes in the AGB linked to selective logging. This method could be used to quantify the impacts of both legal selective logging and illegal logging in tropical forests.
Obtaining accurate 3D descriptions in the thermal infrared (TIR) is a quite challenging task due to the low geometric resolutions of TIR cameras and the low number of strong features in TIR images. ...Combining the radiometric information of the thermal infrared with 3D data from another sensor is able to overcome most of the limitations in the 3D geometric accuracy. In case of dynamic scenes with moving objects or a moving sensor system, a combination with RGB cameras and profile laserscanners is suitable. As a laserscanner is an active sensor in the visible red or near infrared (NIR) and the thermal infrared camera captures the radiation emitted by the objects in the observed scene, the combination of these two sensors for close range applications are independent from external illumination or textures in the scene. This contribution focusses on the fusion of point clouds from terrestrial laserscanners and RGB cameras with images from thermal infrared mounted together on a robot for indoor 3D reconstruction. The system is geometrical calibrated including the lever arm between the different sensors. As the field of view is different for the sensors, the different sensors record the same scene points not exactly at the same time. Thus, the 3D scene points of the laserscanner and the photogrammetric point cloud from the RGB camera have to be synchronized before point cloud fusion and adding the thermal channel to the 3D points.
Accurate assessment of building damage is very important for disaster response and rescue. Traditional damage detection techniques using 2D features at a single observing angle cannot objectively and ...accurately reflect the structural damage conditions. With the development of unmanned aerial vehicle photogrammetric techniques and 3D point processing, automatic and accurate damage detection for building roof and facade has become a research hotspot in recent work. In this paper, we propose a building damage detection framework based on the boundary refined supervoxel segmentation and random forest–latent Dirichlet allocation classification. First, the traditional supervoxel segmentation method is improved to segment the point clouds into good boundary refined supervoxels. Then, non-building points such as ground and vegetation are removed from the generated supervoxels. Next, latent Dirichlet allocation (LDA) model is used to construct the high-level feature representation for each building supervoxel based on the selected 2D image and 3D point features. Finally, LDA model and random forest algorithm are employed to identify the damaged building regions. This method is applied to oblique photogrammetric point clouds collected from the Beichuan Country Earthquake Site. The research achieves the 3D damage assessment for building facade and roof. The result demonstrates that the proposed framework is capable of achieving around 94% accuracy for building point extraction and around 90% accuracy for damage identification. Moreover, both of the precision and recall for building damage detection reached around 89%. Concluded from comparison analysis, the proposed method improved the damage detection accuracy and the highest improvement ratio is over 8%.
The migration of Photogrammetry from the analog medium to the digital medium changed how photogrammetric surveys were carried out and how data are processed, allowing the automation of several steps ...used in the photogrammetric design workflow. There is extensive research in cartographic and geodetic sciences, involving the acquisition of data through photogrammetry, and through techniques such as Structure from Motion (SfM) and Laser Scanner survey. Such research and work involve the use of point clouds from Unmanned Aerial Vehicle (UAV) photogrammetric surveys and Terrestrial Laser Scanner (TLS) surveys, as well as the fusion of these point clouds. This work aims to study the integration of an aerial LiDAR point cloud with a Photogrammetric Point Cloud (PPC) from the UAV survey to minimize occlusion failures and building edges, densify the point cloud, and reduce spurious points. Statistical analyses were performed for the refinement, adjustment, and integration between the point clouds. The results showed that the correction map generated for integrating the clouds reached the objective proposed in this work. The integration of clouds increased from 7 pts/m² of the cloud coming from LiDAR to 140 pts/m² in the final.
Scaffolds always act as disturbances when reconstructing the 3D scene of the construction site due to occlusions, similarities with buildings in color and height as well as their adjacent positions ...to wall surfaces. Since scaffolds are commonly utilized to assist the construction and maintenance of building structures, professionals can estimate the overall progress and temporal objects of construction projects by assessing the status or arrangement of the scaffolds. Its thin, repeating and complex structures also make it a valuable dataset for testing related algorithms and approaches for the reconstruction of 3D construction site scene. To this end, we present a data-driven workflow for the detection and reconstruction of scaffolding components, including tubes, toeboards, and decks, given a photogrammetric point cloud. Our workflow consists of two parts: one part concerns the strategy based on projection and methods of grouping and slicing planar surfaces for detecting and extracting points of scaffolds from the construction site. The other part relates to the point feature derivation using a novel 3D local feature descriptor LSSHOT, designed for extracting features in the classification of points. Specifically, our workflow is implemented by five major steps, including preprocessing of the point cloud, division of building facades, classification of points, geometric modeling and refinement of results. To evaluate our proposed descriptor, a series of simulated experiments using synthetic datasets is conducted via shape matching tests. A real application is also carried out to validate the feasibility and effectiveness of our workflow using the photogrammetric point cloud of a construction site. Results of simulated experiments reveal that our proposed descriptor outperforms the original SHOT descriptor in the simulated test, especially when dealing with point clouds having a large percentage of noise. Regarding the real application of reconstructing scaffolds, points of scaffolds are successfully detected, extracted, and reconstructed. For a facade having enough points, over 70% of the scaffolding elements are reconstructed. For the classification of points using LSSHOT descriptor and a random forest classifier, the accuracy of results for the points of two major scaffolding elements reaches more than 70% in our test examples.
•An algorithm for detecting scaffolds from the point cloud of the construction site is proposed.•A framework for reconstructing geometric objects in a construction site is proposed and validated.•A novel 3D local feature descriptor for delineating linear straight shape objects is designed.
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.
•Free/open-source tool to automatically extract building footprint from aerial point cloud.•Application of geometric features of the photogrammetric point cloud.•Machine learning-based point cloud classification.•K-Means clustering for the segregation of building points.•Vectorized building polygons extracted from an aerial 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.
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