bluuebunny/crossref_metadata_2025 huggingface dataset:
Optically thin clouds in the trades
Authors: Mieslinger Theresa , Stevens Bjorn , Kölling Tobias , Brath Manfred , Wirth Martin , Buehler Stefan A.
Published: May 2022

Abstract. We develop a new method to describe the total cloud cover including optically thin clouds in trade wind cumulus cloud fields. Climate models and large eddy simulations commonly underestimate the cloud cover, while estimates from observations largely disagree on the cloud cover in the trades. Currently, trade wind clouds significantly contribute to the uncertainty in climate sensitivity estimates derived from model perturbation studies. To simulate clouds well, especially how they change in a future climate, we have to know how cloudy it is. In this study we develop a method to quantify the cloud cover from a cloud-free perspective. Using well-known radiative transfer relations we retrieve the cloud-free contribution in high-resolution satellite observations of trade cumulus cloud fields during EUREC4A. Knowing the cloud-free part, we can investigate the remaining cloud-related contributions consisting of areas detected by common cloud-masking algorithms and undetected areas related to optically thin clouds. We find that the cloud-mask cloud cover underestimates the total cloud cover by 33 %. Aircraft lidar measurements support our findings by showing a high abundance of optically thin clouds during EUREC4A. Mixing the undetected optically thin clouds into the cloud-free signal can cause an underestimation of the cloud radiative effect of up to −7.5 %. We further discuss possible artificial correlations in aerosol–cloud cover interaction studies that might arise from undetected optically thin low clouds. Our analysis suggests that the known underestimation of trade wind cloud cover and simultaneous overestimation of cloud brightness in models are even higher than assumed so far.
Towards reconstructing the Arctic atmospheric methane history over the 20th century: measurement and modelling results for the North Greenland Ice Core Project firn
Authors: Umezawa Taku , Sugawara Satoshi , Kawamura Kenji , Oyabu Ikumi , Andrews Stephen J. , Saito Takuya , Aoki Shuji , Nakazawa Takakiyo
Published: May 2022

Abstract. Systematic measurements of atmospheric methane (CH4) mole fractions at the northern high latitudes only began in the early 1980s. Although CH4 measurements from Greenland ice cores consistently covered the period before ∼ 1900, no reliable observational record is available for the intermediate period. We newly report a data set of trace
gases from the air trapped in firn (an intermediate stage between snow and glacial ice formation) collected at the NGRIP (North Greenland Ice Core Project) site in 2001. We also use a set of published firn air data at the NEEM (North Greenland Eemian ice Drilling) site. The two Arctic firn air data sets are analysed with a firn air transport model, which translates
historical variations to depth profiles of trace gases in firn. We examine a variety of possible firn diffusivity profiles, using a suite of measured trace gases, and reconstruct the CH4 mole fraction by an iterative dating method. Although the reconstructions of the Arctic CH4 mole fraction before the mid-1970s still has large uncertainties (> 30 ppb – parts per billion), we find a relatively narrow range of atmospheric CH4 history that is consistent with both depth profiles of NGRIP and NEEM. The atmospheric CH4 history inferred by this study is more consistent with the atmospheric CH4 scenario prepared for the NEEM firn modelling than that for the CMIP6 (Climate Model Intercomparison Project Phase 6) experiments. Our study shows that the atmospheric CH4 scenario used for the NEEM firn modelling is considered to be the current best choice for the Arctic CH4 history, but it should not be used to tune firn air transport models until being verified by further measurements from sources such as the Arctic ice cores. Given the current difficulty in reconstructing the CH4 history with low uncertainty from the firn air data sets from Greenland, future sampling and measurements of ice cores at a high-accumulation site may be the only way to accurately reconstruct the atmospheric CH4 trend over the 20th century.
1750 years of hydrological change in southern Australia: a bivalve oxygen isotope record from the Coorong Lagoon
Authors: Chamberlayne Briony Kate , Tyler Jonathan James , Haynes Deborah , Shao Yuexiao , Tibby John , Gillanders Bronwyn May
Published: May 2022

Abstract. Multi-centennial records of past hydroclimate change are essential to understanding the resilience of aquatic ecosystems to climatic events, in addition to guiding conservation and restoration efforts. Such data are also crucial for examining the long-term controls over regional hydroclimate, and the inherent variability in extreme droughts and floods. Here, we present a 1750-year record of hydroclimate variability in The Coorong South Lagoon, South Australia, part of an internationally significant wetland system at the mouth of Australia’s largest river, the Murray River. Oxygen isotope ratios were measured in Arthritica helmsi bivalve shells preserved in sediments. The oxygen isotope record shows periods of persistent low and high moisture balance from ~500–1050 years and ~1300–1800, respectively, which is consistent with other hydroclimate reconstructions from the region. The range of oxygen isotope values in the sedimentary shells do not differ significantly from the estimated range of modern specimens from the present day lagoon. These data suggest that the restricted and highly evaporated modern day conditions are not markedly different to the pre-impacted state over the last 1750 years, although the absence of A. helmsi in the contemporary lagoon is likely a response to increased salinity, nutrient loading, and anoxia during the last century. These insights are potentially useful both in guiding management efforts in the currently degraded South Lagoon, as well as for understanding long term water resource availability in the region.
Snow stratigraphy observations from Operation IceBridge surveys in Alaska using S/C band airborne ultra-wideband FMCW radar
Authors: Li Jilu , Rodriguez-Morales Fernando , Leuschen Carl , Paden John , Gomez-Garcia Daniel , Arnold Emily
Published: May 2022

Abstract. During the concluding phase of the NASA Operation IceBridge (OIB), we successfully completed two airborne measurement campaigns (in 2018 and 2021, respectively) using a compact S/C band radar installed on a Single Otter aircraft and collected data over Alaskan mountains, ice fields, and glaciers. We observed snow strata in ice facies, wet-snow/percolation facies and dry snow facies from radar data. This paper reports seasonal snow depths derived from our observations. We found large variations in seasonal radar-inferred depths assuming a constant relative permittivity for snow equal to 1.89. The majority of the seasonal depths observed in 2018 were between 3.2 m and 4.2 m, and around 3 m in 2021. We also identified the transition areas from wet-snow facies to ice facies for multiple glaciers based on the snow strata and radar backscattering characteristics. Our analysis focuses on the measured strata of multiple years at the caldera of Mount Wrangell to estimate the local snow accumulation rate. We developed a method for using our radar readings of multi-year strata to constrain the uncertain parameters of interpretation models with the assumption that most of the snow layers detected by the radar at the caldera are annual accumulation layers. At a 2004 ice core and 2005 temperature sensor tower site, the locally estimated average snow accumulation rate is ~2.89 m w. e. a-1 between the years 2002 and 2021. Our estimate of the snow accumulation rate between 2005 and 2006 is 2.82 m w. e. a-1, which matches closely to the 2.75 m w. e. a-1 inferred from independent ground-truth measurements made the same year. We also found a linear increasing trend of 0.011 m w. e. a-1 per year between the years 2002 and 2021. With this trend, we extrapolated the snow accumulation back to 1992 and obtained an average accumulation rate of 2.74 w. e. a-1 between the years 1992 and 2004, which agrees well with the value of 2.66 w. e. a-1 for the same period determined from the ice core data retrieved at the caldera in 2004. The results reported here verified the efficacy of our method, its assumption, and the interpretation models.
Calculation of soil water content using dielectric permittivity measurements; benefits of soil-specific calibration
Authors: Zawilski Bartosz Marian , Granouillac Franck , Claverie Nicole , Lemaire Baptiste , Brut Aurore , Tallec Tiphaine
Published: May 2022

Abstract. Soil Water Content (SWC) probes are widely used around the Earth for scientific and agricultural usage. Most of them are based on soil dielectric permittivity measurement as the dry soil relative dielectric permittivity ε, typically from 3 to 5, is much smaller than the water relative dielectric permittivity: about 80. The measure of dielectric permittivity in wet soils allows deducing the soil volumetric water content. Capacitance, Time Domain Reflectometry (TDR), Frequency Domain Reflectometry (FDR) and even remote sensing techniques such as Ground-Penetrating Radar (GPR) and microwave-based techniques are concerned. This study presents SWC measurements with commercial FDR probes on clayey soil highlighting the benefit of a meticulous soil-specific calibration although constructors indicate probes calibrated with generic constants as convenient. Locally, the use of the manufacturer’s transfer equation can lead to a strong overestimation or underestimation of the actual soil water  content. A simple protocol for clayey soil calibration is proposed. Without soil-specific calibration on clayey soil, we observe errors as important as 115 % with a factory-calibrated probe based on the real part of the dielectric permittivity and up to 245 % with the factory-calibrated probes based on the modulus of the dielectric permittivity.
LINE OF SIGHT AUTO-CALIBRATION FOR CO3D ROLLING SHUTTER MATRIX DETECTOR
Authors: Vidal B. , Laurent G. , Buffe F. , Binet R. , Zanga W. , Greslou D.
Published: May 2022

Abstract. The accurate calibration of the line of sight (LOS) is very important for a high-resolution imaging satellite, especially in a system as Co3D, whose mission is to produce 3D elevation models. A calibration error immediately translates into an altimetry error. Co3D has a matrix and the reading of the lines is done in rolling shutter, each line is read at a different date. So, the geometry of the image is affected by the dynamic perturbations of the attitude during the acquisition time.The objective of the study is to propose a calibration method accurate to 0.1 pixel at 90 percentile. The images needed for calibration must only require one or two satellite passes over the same site, and be compatible with the pointing agility. The method must be robust to residual errors in attitude knowledge.The assumptions of the simulations have been modified in this article to keep the performance of Co3D confidential.
ENABLING CITY DIGITAL TWINS THROUGH URBAN LIVING LABS
Authors: Hristov P. O. , Petrova-Antonova D. , Ilieva S. , Rizov R.
Published: May 2022

Abstract. The population density in urban areas is rapidly rising, leading to a constant need for new infrastructure and services for citizens. To reduce the time to implementation and optimise the monetary cost of various solutions, the plans and policies of local authorities and stakeholders would benefit from undergoing a series of virtual stress tests. To this end, prescriptive and predictive technologies are widely adopted to optimise city planning and to understand the urban processes and environment such as air pollution and transportation. Nevertheless, holistic sandboxes tightly integrated with cities are still largely lacking. The city digital twin is a promising concept that provides a tool for exploration of new solutions in a controlled environment before their deployment. The digital twin is a virtual replica of the real city, which collects data from the infrastructure, processes and services using not only the available systems, but also purposely built connected devices and sensors. In this context, the establishment of urban living labs facilitates the monitoring and understanding of urban processes and enriches the digital twin with highly-relevant data. This paper presents an urban living lab, under deployment in the district of Lozenets in Sofia, Bulgaria. It is part of a larger initiative for developing a city digital twin of Sofia to support the design, exploration, and experimentation of different solutions. The living lab is equipped with sensors for monitoring air quality, atmospheric parameters, noise pollution and pedestrian flows. In addition, a Light Detection and Ranging (LiDAR) system is realised as an edge computing facility at one of the busiest intersections of the district. Along with the equipment, the paper describes the architecture and components of the platform for data collection, storage, processing, and visualization. Finally, high-priority studies are presented, and their demographic and economic impact is discussed.
JOINT BUNDLE ADJUSTMENT OF THERMAL INFRA-RED AND OPTICAL IMAGES BASED ON MULTIMODAL MATCHING
Authors: Sledz A. , Heipke C.
Published: May 2022

Abstract. Despite the fact that Thermal Infrared (TIR) cameras have been in use for decades, processing of TIR images poses a variety of challenges when compared to optical images, which are captured in the visible part of the electromagnetic spectrum. The estimation of the exterior orientation of TIR cameras by bundle adjustment is a difficult task due to the limited geometric resolution of a TIR camera and the low image quality in terms of contrast and texture compared to optical images. Optical images have a potential to increase TIR external orientation accuracy by incorporating them into a joint bundle adjustment. However, because the modality gap between those two image types is large, classical point matching algorithms typically fail to find matches, making processing both image types in the joint bundle challenging. In order to locate matching points in both modalities, this study suggests using the Edge Histogram Descriptor (EHD) in the frequency domain representation of the images based on phase congruency. To properly allocate edges from the phase congruency, which are then employed in EHD, non-maximum suppression and hysteresis thresholding are used. Considering that both sensors are fixed rigidly to a single platform, the search region for the matching point candidate of the TIR image is determined based on stereo calibration of a thermal/optical stereo setup combined with geometric constraints. The final matching is based on the cosine distance, while RANdom SAmple Consensus (RANSAC) is used in order to eliminate outliers. The findings of this study show that using a joint bundle adjustment with optical images versus a bundle adjustment only with TIR images improves TIR image orientation, which is supported by the increased accuracy of the adjusted Ground Control Point (GCP) coordinates.
AN INTEGRATED INDOOR POSITIONING ALGORITHM FOR SMARTPHONE USING PEDESTRIAN DEAD RECKONING WITH MAGNETIC FINGERPRINT AIDED
Authors: Huang C.-H. , Chang Y.-F. , Tang Y.-T. , Tsai M.-L. , Chiang K.-W.
Published: May 2022

Abstract. Indoor positioning has gained increasing attention in previous decades. There are many wireless communication technologies replacing Global Navigation Satellite System (GNSS) because of the sheltered GNSS signal in indoor environment. Most of them need to set up external transceiver with high spatial density to get user's position, such as Wi-Fi, Ultra-Wideband (UWB), Bluetooth and so on. In order to reach high positioning accuracy, the cost of external transceiver becomes higher.This research focuses on low-cost pedestrian dead reckoning (PDR) without additional external equipment. Moreover, a magnetic fingerprint-based positioning is adopted to provide redundant observations of position and heading, using Extended Kalman Filter (EKF) to update the estimation. The proposed method reduces cumulative errors of PDR, achieving an improved algorithm. Since the geomagnetic field exists over the Earth, this technology doesn’t use external equipment either. The integration of PDR and magnetic fingerprint-based positioning, which only uses built-in sensors of a smartphone, should be a low-cost and wide coverage scheme.
A LOW-COST VISUAL RADAR-BASED ODOMETRY FRAMEWORK WITH MMWAVE RADAR AND MONOCULAR CAMERA
Authors: Lu Y.-E. , Tsai S. , Tsai M.-L. , Chiang K.-W.
Published: May 2022

Abstract. One of the most popular research areas is low-cost navigation and positioning systems for autonomous vehicles. Determining a vehicle's position within a lane is critical for achieving high automation. Vehicle navigation and positioning relied heavily on the Global Navigation Satellite System (GNSS) service in open-sky scenarios. Nonetheless, GNSS signals were easily degraded due to various environmental situations such as urban canyons caused by multi-path effects and Non-Line-of-Sight (NLOS) issues. To perform robustly in complex scenarios, sensor fusion is the most common solution. The following paper presents a radar visual odometry framework to improve the lack of scale factors for monocular cameras and poor angular resolution for radar. The framework is based on the characteristics of camera and radar sensors which have complementary advantages in each other. The results show that the proposed framework can be used to estimate general 2D motion in an indoor environment and correct the unknown scale factor of Monocular Visual Odometry in a real-world setting.
AUTOMOTIVE RADAR BASED LEAN DETECTION OF VEHICLES
Authors: Moussa A. , El-Sheimy N.
Published: May 2022

Abstract. One of the most critical features of autonomous vehicles is the detection of road active objects such as vehicles and pedestrians. The autonomous vehicles’ navigation planning and manoeuvre decision-making are aided by the detection of such active objects, resulting in safe and efficient navigation. Deep Convolutional Neural Networks (CNNs) have recently advanced to become one of the state-of-the-art ways to solving detection challenges, particularly in the autonomous vehicle area. Deep CNNs typically use a large number of processing layers with a high number of kernels per layer to enable detection of the target classes which also demands the use of powerful hardware units. In this research, we present a tailored lean detection strategy for vehicle detection using radar observations. The proposed method employs a compact set of convolutions, as well as pixel classification and a customized selection of kernels and kernel sizes, to provide an efficient technique that greatly decreases detection burden and enables real-time processing on average processing units. A training dataset is used to train the convolution window sizes and the pixel classifiers. Finally, the pixel classified grids are processed to identify the vehicles' bounding boxes. Experimental data sets have been collected using medium-range radar sensors mounted on top of a vehicle to evaluate the suggested approach, the Intersection over Union (IoU) values of the test scenes’ detections range from 0.51 to 0.78.
INDOOR MAPPING: EXPERIENCES WITH LIDAR SLAM
Authors: Suleymanoglu B. , Soycan M. , Toth C.
Published: May 2022

Abstract. Indoor mapping is gaining more interest in both research as well as in emerging applications. Building information systems (BIM) and indoor navigation are probably the driving force behind this trend. For accurate mapping, the platform trajectory reconstruction, or in other words sensor orientation, is essential to reduce or even eliminate for extensive ground control. Simultaneous localization and mapping (SLAM) is the computation problem of how to simultaneously estimate the platform/sensor trajectory while reconstructing the object space; usually, a real-time operation is assumed. Here we investigate the performance of two LiDAR SLAM tools based on using indoor data, acquired by a remotely controlled robot sensor platform. All comparisons were performed on similar datasets using appropriate metrics and encouraging results were obtained as a consequence of initial test studies yet further research is needed to analyse these tools and their accuracy comprehensively.
IMPACT OF DEEP LEARNING-BASED SUPER-RESOLUTION ON BUILDING FOOTPRINT EXTRACTION
Authors: He H. , Gao K. , Tan W. , Wang L. , Fatholahi S. N. , Chen N. , Chapman M. A. , Li J.
Published: May 2022

Abstract. Automated building footprints extraction from High Spatial Resolution (HSR) remote sensing images plays important roles in urban planning and management, and hazard and disease control. However, HSR images are not always available in practice. In these cases, super-resolution, especially deep learning (DL)-based methods, can provide higher spatial resolution images given lower resolution images. In a variety of remote sensing applications, DL based super-resolution methods are widely used. However, there are few studies focusing on the impact of DL-based super-resolution on building footprint extraction. As such, we present an exploration of this topic. Specifically, we first super-resolve the Massachusetts Building Dataset using bicubic interpolation, a pre-trained Super-Resolution CNN (SRCNN), a pre-trained Residual Channel Attention Network (RCAN), a pre-trained Residual Feature Aggregation Network (RFANet). Then, using the dataset under its original resolution, as well as the four different super-resolutions of the dataset, we employ the High-Resolution Network (HRNet) v2 to extract building footprints. Our experiments show that super-resolving either training or test datasets using the latest high-performance DL-based super-resolution method can improve the accuracy of building footprints extraction. Although SRCNN based building footprint extraction gives the highest Overall Accuracy, Intersection of Union and F1 score, we suggest using the latest super-resolution method to process images before building footprint extraction due to the fixed scale ratio of pre-trained SRCNN and low speed of convergence in training.
AUTOMATIC METHOD FOR GENERATING 3D BUILDING MODELS WITH TEXTURE FROM UAV IMAGES
Authors: Kim H. G. , Yoon W. , Rhee S. , Kim T.
Published: May 2022

Abstract. In this paper, we propose a method for automatically generating 3D building models from UAV image based point cloud data and for mapping building texture from UAV images. The proposed method generates dense point clouds from UAV images and isolates points from building areas through a statistical analysis. It then generates solid models as 3D building models by point cloud analysis per building area. Texture for 3D building solid models are created by mapping model face to UAV images. In order to verify the proposed method, various UAV image sets and point clouds were tested. As results, the possibility of generating cluster-type solid building models based on UAV images was confirmed. It is expected that this method can contribute to the active usage of UAV images in 3D spatial information generation. In the future, we plan to conduct research on improving the accuracy of curved building shapes and texturing accuracy.
DETECTION AND CORRECTION OF JITTER EFFECT FOR SATELLITE TDICCD IMAGERY
Authors: Zhang H. , Xie B. , Liu S. , Ding R. , Ye Z. , Tong X.
Published: May 2022

Abstract. For the on-orbit high resolution satellites, attitude jitter is a common and complex phenomenon which may deteriorate the image quality and mapping accuracy. In this paper, an image-based jitter inversion and estimation model is derived considering time delay integration (TDI) effect, and the attitude is then updated by taking into account the estimated jitter information. The imagery is finally resampled through virtual re-imaging using the updated accurate attitude to improve the image geometric quality. The proposed method is validated by experiments using the Chinese Ziyuan-3 (ZY-3) satellite imagery.
JOINT SUPER-RESOLUTION AND IMAGE RESTORATION FOR PLÉIADES NEO IMAGERY
Authors: Chouteau F. , Gabet L. , Fraisse R. , Bonfort T. , Harnoufi B. , Greiner V. , Le Goff M. , Ortner M. , Paveau V.
Published: May 2022

Abstract. Modern Earth Observation optical satellite systems, such as Airbus’s Pleiades Neo (PNeo) push the boundaries of high spatial resolution by providing commercial imagery products with up to 30cm ground sampling distance (GSD). To further enhance the quality of the images, the in-space imaging system is usually complemented by on-ground image restoration processing, such as deconvolution and denoising (Latry et al., 2012). Recent advances leverage Convolutional Neural Networks (CNNs) to improve the image restoration quality (K. Zhang et al., 2021a).Single Image Super-Resolution (SISR), or Zoom, the process of obtaining a higher resolution (HR) image from a lower resolved (LR) source, has recently gained traction for both medium resolution sensors such as Sentinel 2 (Lanaras et al., 2018) and high resolution such as Pléiades and GeoEye-1 (Zhu et al., 2020). This process further enhances the resolution of the image to improve downstream applications such as mapping (L. Zhang et al., 2021) and small objects recognition (Shermeyer and Van Etten, 2019). While SISR for remote sensing has been successfully tackled using CNNs (Rohith and Kumar, 2021) the main challenge for reaching acceptable image quality performance lies in the generation of realistic LR/HR training pairs (K. Zhang et al., 2021b). In this paper, we propose:a dedicated simulation chain leveraging extremely-high-resolution (EHR) aerial imagery to generate realistic 30cm Pléiades Neo images and their corresponding fully restored HR equivalent at 15cm GSDA residual-based CNN architecture which we train to jointly restore and zoom the images All contributions are assessed on real PNEO images.We deployed the trained models in a production context, to enhance the full Pléiades Neo products – with a swath of 47k pixels – in an efficient and scalable manner.
CNN BASED VEHICLE TRACK DETECTION IN COHERENT SAR IMAGERY: AN ANALYSIS OF DATA AUGMENTATION
Authors: Kuny S. , Hammer H. , Thiele A.
Published: May 2022

Abstract. The coherence image as a product of a coherent SAR image pair can expose even subtle changes in the surface of a scene, such as vehicle tracks. For machine learning models, the large amount of required training data often is a crucial issue. A general solution for this is data augmentation. Standard techniques, however, were predominantly developed for optical imagery, thus do not account for SAR specific characteristics and thus are only partially applicable to SAR imagery. In this paper several data augmentation techniques are investigated for their performance impact regarding a CNN based vehicle track detection with the aim of generating an optimized data set. Quantitative results are shown on the performance comparison. Furthermore, the performance of the fully-augmented data set is put into relation to the training with a large non-augmented data set.
MULTIMODE SATELLITE IMAGE HYBRID BLOCK-ADJUSTMENT AND ITS APPLICATION IN LARGE AREA ORTHOPHOTO IMAGE PROCESSING
Authors: Zhang H. , Yang Y. , Wang D.
Published: May 2022

Abstract. Different satellite images have different positioning accuracy. For example, stereo satellite images have higher positioning accuracy than resource survey satellite images. In addition, for a large number of non stereo satellite images, due to the inability to build a strong triangulation model, it is impossible to carry out block adjustment alone to improve the image positioning accuracy. High precision and high resolution Orthophoto Images are the basis of resource investigation and monitoring and basic geographic information updating. For example, China's third national land survey, national geographic situation monitoring and other national projects require that the survey base map must reach the accuracy of 1:10000 scale, that is, the mean square plane error of points is less than 5m. For most satellite images, a certain number of ground control points need to be deployed to achieve this accuracy. Due to the difficulty of obtaining high-precision ground control points and DEM data in difficult areas, high-precision mapping has always been an unsolved problem, such as Western China. In addition, due to the limited coverage of a single satellite image, to realize the complete coverage of an image in a large area requires the joint application of multiple satellite images. In this paper, the high-precision collaborative geometric processing model and technical method of high-resolution multi-source remote sensing satellite images are proposed. The high-precision collaborative geometric processing of more than ten kinds of high-resolution domestic satellite images is completed by integrating multi-source observation data. An automatic construction method of large-scale block adjustment model of remote sensing images from domestic satellites based on multivariate generalized control network is proposed, including key technologies such as automatic optimization of optimal tie points under different modes, automatic matching of multi-node parallelization tie points, multi-level gross error elimination and so on, which realizes the automatic and stable construction of aerial triangulation model. The test shows that the positioning accuracy of satellite images is better than the accuracy requirements of 1:10000 scale without ground control points, which solves the problem of geometric positioning of 1:10000 scale accuracy in areas where it is difficult to obtain ground control points in the field of Western China.
TREE CANOPY HEIGHT ESTIMATION AND ACCURACY ANALYSIS BASED ON UAV REMOTE SENSING IMAGES
Authors: Hao J. , Fang Z. , Wu B. , Liu S. , Ma Y. , Pan Y.
Published: May 2022

Abstract. The development of unmanned aerial vehicle (UAV) technology makes the traditional method of danger tree monitoring more digital and convenient. In order to explore the accuracy of tree height measurement by general consumer UAV, this paper takes a campus of University as the research area. The high-resolution images acquired by UAV were processed and compared the accuracy of extracting tree height based on the digital canopy height model (CHM) and point cloud information, respectively. The experimental results show that the mean absolute error of tree height based on point cloud extraction is 10.28cm, which is better than that based on CHM. The tree height extracted from CHM will be less than the measured height. In addition, the accuracy of extracting the height of round crown is better than that of cone crown. The correlation between the measured and true values of the two methods was 0.987 and 0.994, respectively, which indicates that the method of danger tree monitoring by UAV is feasible.
APPLICATION OF A SHELLNET BASED APPROACH TO SEMANTIC SEGMENTATION IN URBAN POINT CLOUD
Authors: Chen D. , Ma X. , Lu X. , Xiao J.
Published: May 2022

Abstract. In recent years, the popularity of airborne, vehicle-borne, and terrestrial 3D laser scanners has driven the rapid development of 3D point cloud processing methods. The 3D laser scanning technology has the characteristics of non-contact, high density, high accuracy, and digitalization, which can achieve comprehensive and fast 3D scanning of urban point clouds. To address the current situation that it is difficult to accurately segment urban point clouds in complex scenes from 3D laser scanned point clouds, a technical process for accurate and fast semantic segmentation of urban point clouds is proposed. In this study, the point clouds are first denoised, then the samples are annotated and sample sets are created based on the point cloud features of the category targets using CloudCompare software, followed by an end-to-end trainable optimization network-ShellNet, to train the urban point cloud samples, and finally, the models are evaluated on a test set. The method achieved IoU metrics of 89.83% and 73.74% for semantic segmentation of buildings and rods-like objects respectively. From the visualization results of the test set, the algorithm is feasible and robust, providing a new idea and method for semantic segmentation of large-scale urban scenes.
DARTBOARD BASED GROUND DETECTION ON 3D POINT CLOUD
Authors: Gigli L. , Marcotegui B. , Velasco-Forero S.
Published: May 2022

Abstract. LiDAR (Light Detection And Ranging) laser scanners acquire 3D point clouds of real environments. The process consists in sampling the scene with laser beams rotating around an axis. By construction, the point density decreases with the distance to the scanner. This density heterogeneity is a major issue, in particular for mobile systems in the context of autonomous driving, as usually a single scan is processed simultaneously (instead of mapping applications that can integrate several scans, reducing the density heterogeneity). We propose a dartboard grid with cell size increasing radially in order to adapt the grid size to the point density. The effectiveness of this strategy is demonstrated by means of a ground detection task, a fundamental step in many workflows of analysis of 3D point clouds.
ROBUST INDOOR POINT CLOUD CLASSIFICATION BY FUSING LSTM NEURAL NETWORKS WITH SUPERVOXEL CLUSTERING
Authors: Li M. J. , Wang L. H. , Cai Z. H. , Yang M. S. , Wu R. J. , Yao M. M.
Published: May 2022

Abstract. To address the problems of lack of training data and inaccurate classification of existing 3D point cloud data segmentation and classification methods, this paper proposes a high-precision classification algorithm for indoor point clouds by fusing LSTM neural network and super voxels. The algorithm first performs super voxel segmentation on the original point cloud and uses it as the basic unit for machine learning classification, and then introduces LSTM (Long Short-Term Memory) neural network to model the super voxel domain relationship and optimize the classification results. Finally, the accuracy of the proposed method is evaluated based on open dataset, and the experimental results show that 83.2% classification accuracy can be achieved in the open dataset.
CIRCULAR TARGET OBSERVATION OF POINT CLOUD USING LASER REFLECTION INTENSITY BY AN UNMANNED AERIAL VEHICLE EQUIPPED WITH A LASER SCANNER
Authors: Nakano K.
Published: May 2022

Abstract. Unmanned aerial vehicles (UAVs) equipped with laser scanners have been widely used for various purposes, such as in construction sites, forestry, and disaster management, as they can obtain high density point clouds with millimeter to centimeter scale accuracy. However, systematic errors in the height of a UAV relative to the earth's surface may occur owing to the method of direct georeferencing using the global navigation system satellite (GNSS) and inertial measurement unit (IMU). Therefore, to enable highly accurate surveying, adjustments must be made using ground control points. However, interpreting the ground control points of a discrete, inhomogeneous density point cloud requires a high degree of skill and effort. In this study, a high-end UAV laser scanner was used to obtain the point clouds of a site using a white circular target enclosed within a black frame in a measurement setup of 500 points/m2. The center coordinates of the circle, calculated using the conventional and proposed methods, were evaluated qualitatively and quantitatively. As a result, the average of 10 error distances was found to be 0.028 m for the weighted center of gravity method, 0.014 m for the fitting circle equation method, and 0.008 m for the proposed method. These results corresponded to one-fifth to one-half of the 0.045-m point intervals of the measurement plan. Thus, using the reflection intensity of point clouds, the circular target observation could be performed.
A METHOD FOR EXTRACTING DEFORMATION FEATURES FROM TERRESTRIAL LASER SCANNER 3D POINT CLOUDS DATA IN RGIPT BUILDING
Authors: Sharma V. B. , Dubey R. , Bhatt A. , Bharadwaj S. , Srivastava A. , Biswas S.
Published: May 2022

Abstract. With the advancement of measuring technology and the incorporation of photogrammetry into architectural applications, architectural documentation applications have taken on a new viewpoint. Terrestrial laser scanning is a modern approach employed in documentation research today in this field. The following are the most significant advantages of terrestrial laser scanning in this study.The point cloud data obtained by terrestrial laser scanners (TLS) allows for quick access to the correct data at the specified frequency, generating relevant and practical findings for the targeted study, and the ability to use scanners in a variety of working environments. Laser scanners have become one of the most popular tools for obtaining effective and successful outcomes in architectural documentation projects such as surveys, restitution, and renovation. Standardized building rules and design procedures have been developed to create structures that are safe for public usage. Structures are frequently subjected to severe loading situations and difficult environmental situations that were not anticipated during design, resulting in a long-term structural deformation. In this paper, the “RGIPT”, which is one of the educational institutes in Uttar Pradesh, India, was scanned with a terrestrial laser scanner for this study, and 3D sketching, and modelling were done with the SCENE software utilizing only 3D point cloud data. Few columns of the structure have been considered for this paper and their behavior with vibration loading is observed. To conduct this study variation of variances for X-direction and Y-direction is observed for millions of points with increasing height of the structure to predict the upcoming deformation in studied columns of the structure.
A TWO-STAGE APPROACH FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS
Authors: Zhang X. , Xue R. , Soergel U.
Published: May 2022

Abstract. Although deep learning has greatly improved the semantic segmentation accuracy of point clouds, the segmentation of rare classes in large-scale urban scenes has not been targeted in available methods. This paper proposes a two-stage segmentation framework with automated workflows for imbalanced rare classes based on general semantic segmentation. The proposed approach includes two stages: general semantic segmentation and object-based refined semantic segmentation. Firstly, general segmentation networks are utilized to segment general large objects. Secondly, refined semantic segmentation is conducted by an automated workflow: 3D clustering and bounding box (BBox) generation are applied to the point cloud of rare fine-grained objects during the training, followed by object detection to extract fine-grained objects. Afterwards, as the constraints, the extracted BBoxes further refine the segmentation results. Our approach is evaluated on the Hessigheim High-Resolution 3D Point Cloud (H3D) Benchmark and obtains state-of-the-art 89.35% overall accuracy and outstanding 75.70% mean F1-Score. Furthermore, rare classes Vehicle and Chimney achieve breakthroughs from zero to 63.63% and 52.00% in F1-score, respectively.
AUTOMATIC TEXTURE MAPPING METHOD FOR 3D MODELS TO CIRCUMVENT OCCLUSION THROUGH 3D SPATIAL THROUGH-VIEW RELATIONSHIPS BETWEEN IMAGES,MODELS, AND POINT CLOUDS
Authors: Liu H. , Xie L. , Li X. , Wang W. X.
Published: May 2022

Abstract. Photorealistic 3D models play an important role in various applications related to smart cities. Texture mapping plays a crucial role as the last step of 3D reconstruction. The texture of the model directly reflects the visualization and realism of the model. In complex city scenes, objects often have varying degrees of occlusion from one another. Traditional methods to circumvent occlusion by image or model data are not very effective. In this paper, we propose an automatic texture mapping method for 3D models to circumvent occlusion through 3D spatial through-view relationships between images, models, and point clouds. First of all, calculate the density and voxel resolution of the point clouds, the point clouds here can be either dense matching point clouds or laser point clouds. Voxelization is based on point clouds, voxels generated by voxelization can reflect the spatial location of the point clouds. Secondly, to determine whether there is an occlusion of voxels between the image and the model vertices based on the spatial through-view relationship between the image, the model, and the voxels, and filter out the unobstructed images. Finally, by optimizing the data items of the Markov random field, the shading function is added to the original data items for evaluating the shading condition, based on which the most suitable image is selected for texture mapping of a triangular surface of the model. Loop computation to complete the unobstructed texture mapping of the entire model. In this paper, the public test datasets released by the International Society for Photogrammetry and Remote Sensing (ISPRS) and several urban area datasets collected by ourselves are incorporated to test the performance of the proposed method. Both qualitative and quantitative cross-sectional comparisons with existing texture mapping algorithms are conducted and presented. Experimental results indicate that our method can effectively circumvent occlusion for automated texture mapping, and generate 3D models with fewer obstructed textures, which largely improves the visual quality and realism of the models.
ISRAELI NATIONAL AERIAL DATA CAPTURING STANDARD FOR GENERATION OF DIGITAL GEOSPATIAL PRODUCTS AT NATIONAL SCALE
Authors: Keinan E. , Shwarts N. , Siman Tov O. , Ronen H. , Regev R.
Published: May 2022

Abstract. Digital camera systems are a key component in the production of reliable, geometrically accurate, high-resolution geospatial products in a national scale. These systems have replaced film imaging in photogrammetric data capturing. Today, we are witnessing a proliferation of imaging sensors, collecting images in different ground resolutions, spectral bands, swath sizes, radiometric characteristics and accuracies being carried on varied mobile platforms. In addition, these imaging sensors combined with navigational tools (such as GPS and IMU), active sensors such as laser scanning and powerful processing tools, to obtain high quality geospatial products. The quality of these geospatial products based on the utilization of calibrated, high-quality digital camera systems.The new Survey of Israel (SOI) regulations specify the quality requirements for each geospatial product including maps at different scales and for different purposes, elevation models, ortho imagery etc. In addition, the regulations require that digital camera systems utilized for mapping purposes should be certified, using a rigorous mapping systems certification and validation process, which are specified in the SOI Director General Instructions (DGI).In 2019 the SOI has updated the camera certification and validation procedure and published a new national aerial capturing standard for digital geospatial products as a basis for the public bid in three main fields:Aerial data capturing including ground control points (GCP) measurements and aerial triangulation.Ortho imagery in several ground resolution distance (GSD) and several projections.Digital Elevation models including: points clouds, Digital Surface Model (DSM) and Digital Terrain Model (DTM). This article provides the details of the Israeli Directive for camera certification and Standard for digital geospatial products including the main technical requirements of the public bid that was carried out.
A NOVEL GEOMETRIC KEY-FRAME SELECTION METHOD FOR VISUAL-INERTIAL SLAM AND ODOMETRY SYSTEMS
Authors: Azimi A. , Hosseininaveh A. , Remondino F.
Published: May 2022

Abstract. Given the importance of key-frame selection in determining the positioning accuracy of Simultaneous Localization And Mapping (SLAM) and Odometry algorithms, and the urgent need in this field for a flexible key-frame selection algorithm, this paper proposes a novel and geometric method for key-frame selection built on top of ORB-SLAM3. It takes a key-frame in a completely robust and flexible way regardless of the environment, data and scene conditions, and according to the physics and geometry of the environment. In the proposed method, the camera sensor and IMU take key-frames simultaneously and in parallel. While selecting a key-frame, an adaptive threshold first decides whether the geometric condition of the frame is appropriate based on the degree of change in the orientation of the point visibility vector from the last key-frame to the current frame. Then the quality of the frame is evaluated by examining the distribution of points inside the frame by a balance criterion. A new key-frame will be created if both conditions provide a positive answer. In addition, if the IMU sensor detects large changes in acceleration, a key-frame independently chosen. The proposed method is evaluated qualitatively and quantitatively on the EuRoC dataset by comparing the algorithm trajectory to a reference trajectory and usig the Absolute Trajectory Error (ATE) and the processing time as metrics. The evaluation results indicate a 26% improvement in the positioning of the algorithm although it has a 9% increase in the processing time due to its geometric key-frame selection process.
DEVELOPING TRANSFERABLE SPATIAL PREDICTION MODELS: A CASE STUDY OF SATELLITE BASED LANDCOVER MAPPING
Authors: Ludwig M. , Bahlmann J. , Pebesma E. , Meyer H.
Published: May 2022

Abstract. The mapping of environmental information based on remote sensing requires a workflow that involves image processing, model training usually based on machine learning, as well as model application and validation. Remote sensing data processing capabilities are nowadays simplified by cloud computing platforms. State of the art machine learning methods for spatial data which involve a reduction of spatial overfitting, handling of extrapolation situations and a spatially explicit error assessment, however, are currently mostly implemented in local computation frameworks. Here we present a workflow that combines the improved processing capabilities of the cloud computation platform openEO with state-of-the-art machine learning model development in R. OpenEO is used for standardized imagery acquisition and preprocessing to provide predictors for model training. To reduce overfitting, predictors which are meaningful for the mapping are identified via spatial variable selection as implemented in R packages. The mapping accuracy is assessed via spatial cross-validation and predictions are limited to the ’Area of Applicability’ of the model. The workflow is designed to enhance and assess the spatial transferability of machine learning models which is demonstrated by a case study of a landcover classification based on Sentinel-2 imagery.
CLASSIFICATION OF SELECTED CORINE CLASSES USING SENTINEL-2 DATA
Authors: Stara L. , Halounova L.
Published: May 2022

Abstract. This paper addresses the classification of CORINE classes. Three land use classes (arable land, pastures, and natural grassland) report similar spectral responses which make it challenging to separate. Therefore, we adopted a multitemporal and multispectral approach using Sentinel-2 satellite imagery in combination with the NDVI vegetation index, Haralick’s textural measures, and topographic information. The workflow identifies a methodology for combining various groups of input data (optical, NDVI, textural, topographic) and explores the suitable use of the Random Forest classifier for the task. The classification was carried out in three different European locations. The results present a strong separation of arable land (F1 score over 96%) from the other two classes. Pastures and natural grassland classes achieved F1 in the range of 76% to almost 85% in both cases. In conclusion, we discuss the suitability of the CORINE database for such classification problems.