UAV-based very high resolution point cloud, digital surface model and orthomosaic of the Chã das Caldeiras lava fields (Fogo, Cabo Verde)

Abstract. Permafrost landscapes in the Arctic are highly vulnerable to warming, with rapid changes underway. High-resolution remote sensing, especially aerial datasets, offers valuable insights into current permafrost characteristics and thaw dynamics. Here, we present a new dataset of very high resolution orthomosaics, point clouds, and digital surface models that we acquired over permafrost landscapes in northwestern Canada and northern and northwestern Alaska for the purpose of better understanding the impacts of climate change on permafrost landscapes. The imagery was collected with the Modular Aerial Camera System (MACS) during aerial campaigns conducted by the Alfred Wegener Institute in the summers of 2018, 2019, and 2021. The MACS was specifically developed by the German Aerospace Center (DLR) for operation under challenging light conditions in polar environments. It features cameras in the optical and the near-infrared wavelengths with up to a 16 MP resolution. We processed the images to four-band (blue–green–red–near-infrared) orthomosaics and digital surface models with spatial resolutions of 7 to 20 cm as well as 3D point clouds with point densities of up to 41 points m−2. The dataset collection features 102 subprojects from 35 target regions (1.4–161.1 km2 in size). Project sizes range from 4.8 to 336 GB. In total, 3.17 TB were published. The horizontal precision of the datasets is in the range of 1–2 px and vertical precision is better than 0.10 m. The datasets are not radiometrically calibrated. Overall, these very high resolution images and point clouds provide significant opportunities for mapping permafrost landforms and generating detailed training datasets for machine learning, can serve as a baseline for change detection for thermokarst and thermo-erosion processes, and help with upscaling of field measurements to lower-resolution satellite observations. The dataset is available on the PANGAEA repository at https://doi.org/10.1594/PANGAEA.961577 (Rettelbach et al., 2024).

Point cloud is widely used because of its vectorized and compact information, but the unorder and discrete characteristics reduce the accuracy of point cloud reconstruction from images. In this paper, we propose a novel method to reconstruct high-resolution object point cloud by image redescription and point cloud upsampling. We first combine reconstruction and upsampling networks to generate high-resolution point cloud and achieve joint optimization through phased training. Then we present an image redescription mechanism to achieve the bidirectional correlation and enhance the semantic consistency between images and point clouds. The experiments on the ShapeNet dataset demonstrate the superiority of the proposed method over the state-of-the-art methods.

Abstract. Low cost UAV systems are a flexible and mobile platform for very detailed spatial high-resolution point cloud and surface height mapping projects. This study investigates the potential of the DJI Phantom 4 Pro 3D point clouds and derived crown surface height information in combination with RGB spectral information for mapping of deciduous tree species in the Hainich national park area. RGB image data was captured in August, early October and November 2018 to create a multi seasonal spectral dataset for a 100 ha test area. The flight campaigns were controlled from the Hainich flux tower platform in 40 m height owned and operated by University of Göttingen in the central part of the park area. Absolut georeferencing accuracy of the datasets was improved using 7 DGPS measured control points within the stand structure on small forest clearings. Image files and ground control points were processed to a dense point cloud model with 2.6 billion points (approximately 200k points per tree crown object) using the Agisoft Metashape cluster processing environment. Additionally, a digital surface model and a true ortho image mosaic with 3 cm spatial resolution was generated. For the differentiation of deciduous tree species, a reference data set with coordinates for the tree species Fagus sylvatica (beech), Fraxinus excelsior (ash), Acer pseudoplatanus (sycamore maple), Carpinus betulus (hornbeam) and dead trees and early defoliated trees was defined. The study site is however dominated by Fagus sylvatica and Fraxinus excelsior. We studied two different groups of features: tree crown surface height variability parameters using point cloud densities, point cloud height variance, local standard deviation of gaussian curvature, standard deviation of local point cloud roughness and multi temporal normalised spectral features using multi seasonal uncalibrated UAV RGB data. Analysis of feature separability showed that very high-resolution point cloud surface curvature properties with small neighbourhood radii can differentiate some tree species types but we also found multitemporal spectral ratios based on RGB data to be very successful in differentiating the main tree species.Results of this work show that super fine very dense point cloud models and derived roughness measures of mixed forest stand surfaces hold valuable information for deciduous species discrimination and will likely also be very useful for morphological analysis of tree crown types.

In computer-vision-based human computer interaction (HCI), higher-quality signal leads to better system performance. In this paper, we develop a real-time high-resolution 3D object scanning system based on structured light illumination (SLI). Our system fuses depth information with RGB texture to reconstruct high-resolution 3D point cloud. The point cloud preserves accurate surface geometry of the object (e.g., finger postures of hands, facial expressions, etc). Respectively, for a 640 × 480 video stream, our system can generate phase and texture video at 1500 frames per second (fps) and produce full 3D point clouds at 300 fps. For gesture recognition, we propose to combine the module of robust face recognition with the module of 3D point cloud classification. Moreover, rather than extracting sophisticated features, we leverage the accurate reconstruction and classify each point cloud by directly matching the whole 3D surface geometry with the templates of different classes. The proposed recognition system is robust to the scaling, translation, rotation and texture of objects. Finally, utilizing the system, we contribute to the research community two large-scale high-resolution 3D point cloud databases, i.e., SLI 3D Hand Gesture Database and SLI 3D Face Database. The proposed point cloud recognition approach achieves recognition rates up to 98.0% over the gesture database and 88.2% over the face database in our pilot study.

Digital elevation models (DEM) were generated from oblique stereo-images acquired with a handheld digital camera. Two model scenarios are considered. Firstly, at local outcrop scale, with easy access, and distances between camera and outcrop varying between c. 40 m and c. 120 m, a very dense and high resolution point cloud was produced. The quality of the point cloud was evaluated against a terrestrial laser scan derived model of the same outcrop. The deviation between the two datasets varies between 0.02 m and 0.09. This is negligible for most geological purposes and illustrates the potential of using terrestrial photogrammetry at local outcrop scale as an alternative to lidar generated elevation data. Secondly, the method is explored at a regional scale, where a set of oblique stereo-images of a remotely located steep inaccessible mountain cliff was collected from a helicopter at a distance of c. 2–5 km under challenging and unfavourable conditions. The quality of the point cloud was evaluated against two elevation models extracted from conventional aerial photographs. Compared to a DEM extracted from monochrome aerial photographs, such as are often the only available topographic source for remote regions, a clear improvement in resolution is observed. Comparison with a DEM extracted from high resolution coloured aerial photographs shows the two digital elevation models to be very similar in resolution and with root mean square deviation (RMSE of 6.0 m).

<p>Photogrammetric surveys from UAV and LiDAR surveys are two techniques that allow for the production of very high resolution point clouds. The use of these techniques result in a detailed reconstruction of difficult-to-access environments such as underground cavities. A rigorous georeferencing of the acquired data allows for a comparison of the hypogean development of the cave to the overlying territory. This study presents a case of integration between these two techniques, applied to the risk assessment of the collapse of the vaults in a natural cavity in the Trieste Karst (north east Italy). This site is particularly delicate given that on the slope above the cave there is an abandoned stone quarry. In order to survey the quarry above the cave, a flight was performed with UAV, while the cave was surveyed with Laser Scan from the ground. The flight was made using a UAV DJI Phantom RTK, which carried a 20 Mpixel 1“ sensor camera. 8 ha of terrain was surveyed, capturing about 733 high resolution images and surveying 22 GCPs (Ground Control Point) with a GNSS RTK receiver. It was possible to reduce the number of GCPs, since the drone recorded the shooting positions very accurately with the on-board GPS RTK. Data were analyzed using Agisoft Metashape Professional to produce an orthophoto and a DSM (Digital Surface Model) with a ground resolution of 0.02 m and 0.04 m respectively. The point cloud has a density of 586 points/m<sup>2</sup>. The LiDaR survey was carried out using an ILRIS 3D ER laser scanner from Optec. The point cloud has a density of approximately 2500 points/m<sup>2</sup> and 5 stations were needed to cover the underground development of the cavity. The georeferencing of the data was carried out by roto-translation on geo-referenced benchmarks, surveyed with GPS RTK and total station. The point cloud was processed using Terrascan software (Terrasolid). The two point clouds were aligned, geo-referenced and combined using Polyworks software (Innovmetric), in order to check the thicknesses of the material present above the vault of the cave. The integration of epigean and hypogean data made it possible to identify some critical points related to a vault thickness of approximately 1.5 meters, located at the quarry square. This work made it possible to highlight critical issues difficult to detect without the integrated approach of these different survey methodologies.</p>

In the last decade, the combination of rapid development of low cost and small Unmanned Aerial Vehicles (UAVs), improved battery technology and conventional sensors (Optical and LiDAR) in terms of cost and dimensions, led to new opportunities in environmental remote-sensing and 3D surface modelling. A long term monitoring campaign was performed in Ricasoli village, in the Upper Arno river Valley (Tuscany, Italy), to understand the possibility of this rising technology to characterize and to monitor landslides. The RGB and multispectral imageries were analyzed and combined using SfM (Structure from Motion) software, in order to obtain high resolution orthomosaics, point clouds and 3D digital terrain models (DTM). The comparative analysis of the obtained DTMs allowed a very accurate reconstruction and mapping of the detected landslides. The collected data also allowed to precisely detect some slope portions prone to failure and to evaluate the area and volume of the involved masses as well as displacement rates.

In this paper, we have described an accuracy analysis of MLS point clouds collected using the LARA3D prototype platform in an urban area. Accuracy of the MLS was achieved through comparison with other data sources more accurate that the studied system. The study has shown, that when compared with control points, collected by a Total Station, the prototype system LARA3D is able to produce data with an accuracy better then 0.3 m. However, taking into consideration the uncertainty in the identification of common points, this method is affected by man-made error and limited by point cloud resolutions. Meanwhile, the use of existing reference data, such as e.g. high resolution point clouds from static terrestrial laser scanning provides fast and reliable data evaluation. The subjective element of operator interpretation is also removed. Results achieved using ICP algorithm show, that our mobile mapping system suffers from limitations of the sensor quality and Kalman filter implementation. In the case of point clouds locally degraded, proper matching is impossible and the obtained result does not reflect the type and scale of deformation correctly. Meanwhile, another less time-consuming and more automated method for assessing data accuracy should be developed. That may be referred to using the existing spatial data as reference, such as e.g.: cadastre, ALS data, Topographic Data Base (TBD), Digital Terrain Model, orthophotos and so on.

Abstract. Forest inventory provides comprehensive information about the geometric and biometric state of forests as well as vegetated areas. In this study, a point-based 3D method is presented for tree detection as well as measuring of structural properties of forests such as the tree height, tree position and canopy area using high resolution point cloud which is provided by an Unmanned Aerial Vehicle (UAV)-based LiDAR sensor. The proposed method is based on the density of point cloud and 2D and 3D distance measurements. It includes three main steps as pre-processing, tree detection, and extraction of tree structural attributes. After generating a canopy height model, an image is created based on the density of point cloud. Next, points are classified based on 2D and 3D distance measurements, sequentially, from the highest to the lowest. According to the results, the rate of tree detection is about 95% and the main structural parameters of a tree such as the position, height, area and length of the canopy are estimated with the accuracy of 1.97 m, 0.36 m, 12.78 m2 and 0.79 m, respectively.

An optimized fuzzy K-means clustering method for automated rock discontinuities extraction from point clouds

Modern remote sensing technologies have enabled the creation of high-resolution 3D point clouds of infrastructure systems. In particular, photogrammetric reconstructions using Dense-Structure-from-Motion algorithm can now yield point clouds with the necessary resolution to capture small-strain displacements. By tracking changes in these point clouds over time, displacements can be measured, leading to strain and stress estimates for long-term structural evaluations. This study determines the accuracy of a comparative point cloud analysis technique for measuring deflections in high-resolution point clouds of structural elements. Utilizing a combination of a recently developed point cloud generation process and localized nearest-neighbors cloud comparisons, the analytical technique is designed for long-term field scenarios and requires no artificial tracking, targets, and camera calibrations. A series of flexural laboratory experiments were performed in order to test the approach. The results indicate sub-millimeter accuracy in measuring the vertical deflection, making it suitable for the small-displacement analysis of a variety of large-scale infrastructure systems. Ongoing work seeks to extend this technique for comparison with as-built and finite element models.

Aerial imaging systems increasingly gain oblique viewing capabilities. Through these passive systems, photogrammetric 3D point clouds of a scene become available in addition to traditional vertical 2.5D information. In the field of urban reconstruction, this complementary information seeks for robust and automated fusion methods in order to derive 3D building geometry as well as topology in larger scales. It is sequentially shown how to get from façade planes over building footprints to roof reconstruction including overhangs. Façade planes are extracted from a photogrammetric high-resolution 3D point cloud. Local regression methods in 2D space are used to determine the local direction and a criterion for the local linearity of the point cloud. Based on these two parameters, the 3D point cloud is segmented according to which façade it belongs to. From the segmented point cloud, building footprints are extracted as polygons. Similar to cadaster information, those polygons, along with a traditional digital surface model (DSM), serve for one thing as the basis for overhang determination which is performed by fitting polynoms on the outside of façades and using their inflection points as overhang boundary. For another thing, they serve as roof areas which are segmented, topologically described and geometrically modelled. Again local regression methods are used but this time in 3D space in order to segment roof parts. Subsequently, the roof topology is derived using region growing methods. The final building models hold both, geometrical and topological properties.

Gunumuzde hizli gelisen uzay ve hava kaynakli uzaktan algilama teknolojileri, harita, jeoloji, cevre ve madencilik gibi araziye bagli muhendislik disiplinleri icin vazgecilmez hale gelmistir. Dijital fotogrametri ve hava kaynakli lazer tarama (Airborne Laser Scanning-ALS) ile cok yuksek cozunurluklu (Very High Resolution – VHR), hizli ulasilabilen hassas nokta bulutlari sayesinde topografik yuzey tanimlamasi kolay hale gelmistir. Fotogrametrik tekniklerin en guncellerinden biri olan optik donanimli insansiz hava araclari (IHA), farkli amaclar icin oldukca revactadir. IHA’lar dusuk ucus irtifasinin avantajini kullanarak yuksek cozunurluklu veri saglamaktadir. Bu calismada, Bulent Ecevit Universitesi Kampusu icerisinde yer alan bir insaat calismasi ve onun cevresel etkileri el yapimi bir optik IHA ile gozlemlenmistir. Uygulamada, topografyaya ait es yukseklik egrileri, sayisal arazi modeli (Digital Terrain Model - DTM) ve fark DTM’leri (Differential DTM- DiffDTM) kullanilarak zamansal degisimler belirlenmistir. DiffDTM’ler kullanilarak, topografya uzerindeki degisimler, es yukseklik egrilerinin morfolojik yapinin degisimini gosteren renk yukseklik skalasi ile gorsellestirilmistir.

With the rapidly developing unmanned aerial vehicles, the requirements of generating maps efficiently and quickly are increasing. To realize online mapping, we develop a real-time dense mapping framework named DenseFusion which can incrementally generates dense geo-referenced 3D point cloud, digital orthophoto map (DOM) and digital surface model (DSM) from sequential aerial images with optional GPS information. The proposed method works in real-time on standard CPUs even for processing high resolution images. Based on the advanced monocular SLAM, our system first estimates appropriate camera poses and extracts effective keyframes, and next constructs virtual stereo-pair from consecutive frame to generate pruned dense 3D point clouds; then a novel realtime DSM fusion method is proposed which can incrementally process dense point cloud. Finally, a high efficiency visualization system is developed to adopt dynamic levels of detail (LoD) method, which makes it render dense point cloud and DSM smoothly. The performance of the proposed method is evaluated through qualitative and quantitative experiments. The results indicate that compared to traditional structure from motion based approaches, the presented framework is able to output both large-scale high-quality DOM and DSM in real-time with low computational cost.

SEABIM® is a patented scan to BIM process that can generate a reliable and complete 3D model of a rubble-mound breakwater precast protective layer (Xbloc®, Accropode™, Core-loc™ etc.). Using computer vision techniques, the known 3D shape of the Concrete Armour Units (CAU) is detected in a high-resolution point cloud, which allows to obtain the position and orientation of each unit relative to the cloud. By superimposing 3D models produced from different scans at different dates, the movement of each block can then be quantified and represented as a vector. For natural riprap armours, a point cloud segmentation algorithm is applied. Each rock is identified, and then geometric characteristics of the armour can be computed (apparent rock diameters, placement density), and the movement between each scan can be monitored. This tool has been applied to numerous real-scale projects since 2019, both in the monitoring of newly built infrastructures and in the asset management phase (Aberdeen South Harbour, Calais port 2015, Nouvelle Route du Littoral etc...). Breakwater physical models in laboratories, in wave flumes or basins, also require monitoring of the movement of reduced-scale blocks between each wave series. With SEABIM® this movement can be computed with accuracy for each CAU, allowing for a consistent evaluation of the armour layer response to wave loads. The automatic block detection process can be launched on the point cloud from a survey of the initial physical model, obtained either by photogrammetry or LiDAR. Then a new survey is done after each wave series in order to create a sequence of 3D models. Those models can be compared by computing the displacement vectors of the centers of gravity of the units. The vectors can be visualized using color graded arrows within the 3D model. All the information can then be exported to a spreadsheet for further analysis. Using this technique, settlement, rocking and sliding effects can be identified. In this paper we will discuss in detail the adaptation of the survey techniques from real scale to laboratory projects (difference in scanning technique, scale effect on point cloud density etc.). Then we will explain the 3D modeling process and the applications of the 3D models for engineering analysis purposes.