bluuebunny/crossref_metadata_2025 huggingface dataset:
Microphysical, macrophysical and radiative responses of subtropical marine clouds to aerosol injections
Authors: Chun Je-Yun , Wood Robert , Blossey Peter , Doherty Sarah J.
Published: May 2022

Abstract. Ship tracks in subtropical marine low clouds are simulated and investigated using large eddy simulations. Five variants of a shallow subtropical stratocumulus-topped marine boundary layer (MBL) are chosen to span a range of background aerosol concentrations and variations in free-tropospheric (FT) moisture. Idealized time-invariant meteorological forcings and approximately steady-state aerosol concentrations constitute the background conditions. We investigate processes controlling cloud microphysical, macrophysical and radiative responses to aerosol injections. For the analysis, we use novel methods to decompose the liquid water path (LWP) adjustment into changes in cloud properties, and the cloud radiative effect (CRE) into contributions by cloud macro- and microphysics. The key results are that (a) the cloud top entrainment rate increases in all cases, with stronger increases for thicker than thinner clouds; (b) the drying and warming induced by increased entrainment is offset to differing degrees by corresponding responses in surface fluxes, precipitation and radiation; (c) MBL turbulence responds to changes caused by the aerosol perturbation, and this significantly affects cloud macrophysics; (d) across two days' simulation, clouds were brightened in all cases. In a pristine MBL, significant drizzle suppression by aerosol injections results not only in greater water retention, but also in turbulence intensification, leading to a significant increase in cloud amount. In this case, Twomey brightening is strongly augmented by an increase in cloud thickness and cover. In addition, a reduction in the loss of aerosol through coalescence scavenging more than offsets the entrainment dilution. This interplay precludes estimation of the lifetime of the aerosol perturbation. The combined responses of cloud macro- and microphysics lead to 10–100 times more effective cloud brightening in these cases relative to those in the non-precipitating MBL cases. In moderate and polluted MBLs entrainment enhancement makes the boundary layer drier, warmer and more stratified, leading to a decrease in cloud thickness. Counterintuitively, this LWP response offsets the greatest fraction of the Twomey brightening in a moderately moist free troposphere. This finding differs from previous studies which found larger offsets in a drier free troposphere, and results from a greater entrainment enhancement in initially thicker clouds, so the offsetting effects are weaker. The injected aerosol lifetime in cases with polluted MBLs is estimated as 2–3 days, which is longer than the estimates from satellite images.
Rapid Sea Ice Changes in the Future Barents Sea
Authors: Rieke Ole , Årthun Marius , Dörr Jakob Simon
Published: May 2022

Abstract. Winter Arctic sea ice loss is strongest in the Barents Sea. The anthropogenic ice decline is superimposed by pronounced internal variability that represents a large source of uncertainty in future climate projections. A notable manifestation of internal variability are periods of rapid ice loss or growth that greatly exceed the anthropogenic trend. These rapid ice change events are associated with large displacements of the sea ice edge which could potentially have both local and remote impacts on the climate system. In this study we accordingly present the first investigation of the frequency and drivers of these rapid ice change events in the future Barents Sea, using multi-member ensemble simulations from CMIP5 and CMIP6. A majority of rapid sea ice changes are triggered by trends in ocean heat transport or surface heat fluxes. Rapid ice change events are a common feature of the future Barents Sea until the region becomes close to ice free. As their evolution over time is closely tied to the average sea ice conditions, rapid ice changes in the Barents Sea serve as a precursor for future changes in adjacent seas.
Evaluation of WRF-Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean
Authors: Georgiou George K. , Christoudias Theodoros , Proestos Yiannis , Kushta Jonilda , Pikridas Michael , Sciare Jean , Savvides Chrysanthos , Lelieveld Jos
Published: May 2022

Abstract. We describe and evaluate a high-resolution real-time air quality forecast system over the Eastern Mediterranean, based on a regional, online coupled atmospheric chemistry and aerosol model. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is used to perform daily, 3 d forecasts of regulated pollutants (NO2, O3, PM2.5) over the Eastern Mediterranean, applying three nested domains with horizontal resolutions of 50, 10 and 2 km, the latter focusing on Cyprus. Natural (dust, sea-salt, biogenic) emissions are calculated online, while anthropogenic emissions are based on the Emissions Database for Global Atmospheric Research – Hemispheric Transport of Air Pollution (EDGAR-HTAP) global emission inventory. A high spatial (1 km) and temporal (hourly) anthropogenic emission inventory is used for the island of Cyprus in the innermost domain. The model skill in forecasting the concentrations of atmospheric pollutants is evaluated using measurements from a network of nine ground stations in Cyprus and compared with the forecasting skill of the EU Copernicus Atmosphere Monitoring Service (CAMS). The forecast of surface temperature, pressure, and wind speed is found to be accurate, with minor discrepancies between the modelled and observed 10 m wind speed at mountainous and coastal sites attributed to the limited representation of the complex topography of Cyprus. Compared to CAMS, the WRF-Chem model predicts with higher accuracy the NO2 mixing ratios at the residential site with a normalized mean bias (NMB) of 7 % during winter and −44 % during summer, whereas the corresponding biases for CAMS are −81 % and −84 %. Due to the high temporal resolution of the anthropogenic emission inventory, the WRF-Chem model captures more accurately the diurnal profiles of NO2 and O3 mixing ratios at the residential site. Background PM2.5 concentrations influenced by long-range transport are overestimated by the WRF-Chem model during winter (NMB = 54 %), whereas the corresponding NMB for CAMS is 11 %. Our results support the adoption of regional, online coupled air quality models over chemical transport models for real-time air quality forecasts.
SENSOR INTEGRATION AND APPLICATION OF LOW-SIZED MOBILE MAPPING PLATFORM EQUIPPED WITH LIDAR, GPR AND PHOTOGRAMMETRIC SENSORS
Authors: Bakuła K. , Lejzerowicz A. , Pilarska-Mazurek M. , Ostrowski W. , Górka J. , Biernat P. , Czernic P. , Załęgowski K. , Kleszczewska K. , Węzka K. , Gąsiewski M. , Dmowski H. , Styś N.
Published: May 2022

Abstract. The article aims to analyse the possibilities of GPR, LiDAR, and photogrammetric sensors integration in a specific application, considering the various combination of sensor and their parameters. This text also discusses the possibility of using LiDAR sensors in a low-sized mobile platform for the inventory of the road lane in a dense urban area. The text presents the opportunities and recommendations of GPR and LiDAR sensors for their selection and the possibility of using them. In the case of LiDAR and photogrammetric data, two planned applications were indicated: platform georeferencing and mapping. The accuracy and noise of the Livox Avia LiDAR sensor and point cloud obtained from the Sony A7R camera with image-matching were analysed for a surface inventory. Despite the sufficient density and detail of the data, the intensity distinguishing different surfaces, the noise of LiDAR data at the level of 2 cm was too high to do the inventory of minor damages and analyse road surfaces. Higher accuracy was achieved at the level of 1 cm for photogrammetric point clouds. The article also presents the concept of integrating multi-source data visualised into the form of an oriented point cloud showing both what is above and below the earth's surface, which enables the synergy effect and joint analysis of data with entirely different characteristics.
THE USE OF A WIDE FOV LASER SCANNING SYSTEM AND A SLAM ALGORITHM FOR MOBILE APPLICATIONS
Authors: Castanheiro L. F. , Tommaselli A. M. G. , Machado M. V. , Santos G. H. , Norberto I. S. , Reis T. T.
Published: May 2022

Abstract. This paper presents the assessment of a wide-angle laser scanner and a simultaneous localisation and mapping (SLAM) algorithm to estimate the trajectory and generate a 3D map of the environment. A backpack platform composed of an OS0-128 Ouster (FoV 90° × 360°) laser scanner was used to acquire laser data in an area with urban and forest features. Web SLAM, an online SLAM algorithm implemented by Ouster, Inc., was used to estimate the trajectory and generate a 3D map in a local reference system. Then, the 3D point clouds were transformed into the ground coordinate system with a rigid body transformation. Three datasets were used: (I) the entire trajectory consisting of forwarding and backwards paths, (II) only forward path, and (III) only backward path. Visual analysis showed a double mapping error in the point cloud of dataset I. Therefore, the point cloud registration was performed only for datasets II and III, achieving a centimetric accuracy, which is compatible with the OS0-128 laser scanner accuracy (∼5cm).
PERFORMANCE ANALYSIS OF SEMANTIC REFRESH INDOOR NAVIGATION FOR SMARTPHONE’S SENSORS USING INS/VINS INTEGRATION SCHEME
Authors: Lin C.-X. , Zeng J.-C. , Hung M.-C. , Tsai M.-L. , Chiang K.-W.
Published: May 2022

Abstract. Positioning and Orientation System (POS), which integrates Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS), is widely used to accomplish outdoor navigation missions for land vehicles. However, the positioning accuracy would become worse in GNSS-hostile environments (Chiang et al., 2013), which is quite challenging to accomplish indoor navigation environments. Nevertheless, smartphones are contained many embedded sensors, including GNSS, IMU, camera, which have the potential to be an ideal personal navigation device. In this research, we mainly propose an integrated scheme of INS/VINS/object detection refresh (ODR) for indoor challenging environments. The goal is to achieve indoor navigation for vehicular applications only using smartphones. The algorithm is developed based on the smartphone. By the conventional inertial navigation system, which is integrated with two designed processes to further improve the performance. First is assistance from the visual-inertial navigation system (VINS). The long-term drift caused by the INS could be decreased effectively, and complete the extended Kalman filter (EKF) composition. The second is to apply neural network, YOLO-v3 (Redmon et al.,2018), to detect objects and provide the object's describer information to refresh the proper position. Therefore, the proposed method uses visual estimation and recognition methods to assist the smartphone platform to obtain a more accurate solution.Finally, we use the navigation-grade IMU as the reference system for accuracy verification. The accuracy comparisons of the three integration solutions are analysed reasonably. The position accuracy is reasonable. Compared with the original smartphone INS integration method, the proposed integration scheme improves the accuracy from the horizontal direction by 78.5%.
POINT CLOUD SIMULATOR FOR SPACE IN-ORBIT CLOSE RANGE AUTONOMOUS OPERATIONS
Authors: González-deSantos L. M. , González-Jorge H. , Sanjurjo-Rivo M. , Michinel H.
Published: May 2022

Abstract. In recent years, many different in-orbit close-range autonomous operations have been developed for multiple purposes, such as rendezvous and docking operations or ADR operations. In both cases, the systems have to calculate the relative position between the spacecraft and the target in order to control the orbital manoeuvres and the physic interaction between both systems. One of the sensors used for the pose calculation for these operations are LiDAR sensors, developing pose calculation algorithms that process the point cloud acquired by these sensors. One of the main problems for the development and testing of these algorithms is the lack of real data acquired in orbit and the difficulty of acquiring this data. This makes it fundamental to develop a simulator to generate realistic point clouds that can be used to develop and test pose calculation algorithms. This work presents a simulator developed for this purpose, that is the generation of realistic point clouds for algorithm development for pose calculation using LiDAR sensors for space in-orbit close range autonomous operations. The simulator uses the LiDAR sensor specifications, in order to introduces measurement errors and the scanning pattern, and 3D model of the satellite or object that is scanned.
IMAGE MOTION COMPENSATION – THE VEXCEL APPROACH
Authors: Dohr S. , Muick M. , Schachinger B. , Gruber M.
Published: May 2022

Abstract. Motion compensation in general and forward motion compensation in particular was an important milestone in aerial imaging when presented for film-based camera systems in the late 90ts of the last century. It focused on the forward motion compensation to enhance the image quality when flight speed and image scale produce such motion blur even at short exposure time. Another development and milestone in aerial photogrammetry, the active mount, contributed as well to reduce motion blur.When digital aerial cameras replaced the film-based camera systems in the first decade of the 21st century, forward motion compensation (FMC) could be implemented as an electronic feature of the CCD sensors, namely the time delayed integration (TDI) feature, which worked fine and did not require a mechanical component. Not all cameras could make use of that but large format frame cameras like DMC and UltraCam were able to compensate forward motion blur exploiting this feature of the electronic sensor component.Since CMOS sensors were replacing the CCD sensor component of digital aerial cameras there was a need to implement the FMC mechanism by another solution. One approach was based on a mechanical device able to move the sensor along the flight path of the aircraft like it was the approach for film cameras.At that time Vexcel Imaging decided to develop a more versatile solution based on software and without any additional mechanical part in the camera body. This solution was designed to not only compensate for a uniform compensation to the forward motion but also for angular motion blur and for different scales in one and the same image. This is especially important for the oblique viewing direction of a camera when foreground and background of an oblique scene show different scales in one and the same image.The need to compensate for motion blur is evident when large scale aerial imaging is required, and best image quality is expected. Motion blur is caused from the speed of the aircraft over ground, the image scale, and an angular component – the angular motion blur - caused from turbulences if they exist.The magnitude of the forward motion blur can be estimated when multiplying aircraft speed and image scale and exposure time (e.g. speed over ground 75 m/sec, scale 1/10000 and exposure time 0,001 seconds leads to 0,0075 mm or 7,5 µm in the image). Different image scales result in different magnitude of motion blur. This is evident for oblique camera systems.
RELATIVE RADIOMETRIC CALIBRATION OF UAV IMAGES FOR IMAGE MOSAICKING
Authors: Ban S. , Kim T.
Published: May 2022

Abstract. Since UAV images are taken at a low altitude, compared to satellite or aerial images, they usually have narrow ground coverage. In order to use them over a large area of interest, it is essential to mosaic them into a large image. In addition, UAV images may have different brightness values at the same ground locations according to different weather conditions and relative position between sun and sensor at the time of photographing. To mosaic a large number of UAV images, it is essential to calibrate different brightness values through a relative radiometric calibration method. In this paper, we propose a relative radiometric calibration method of UAV images capable of reducing over-calibration and minimizing error transfers during image mosaicking. We applied a gain compensation technique to minimize the effect of exposure difference when generating mosaic image. We also applied an image blending technique to remove seamlines among adjacent images to merge. As a result, smooth mosaic images were generated without any noticeable brightness difference around seamlines. For quantitative validation, differences in brightness values in overlapping areas were calculated. The differences have been decreased after applying the proposed method. The results indicated that the proposed method could generate a natural mosaic image by correcting differences in brightness values and removing seamlines of UAV images. Our method has an advantage of being able to calibrate differences in brightness values only with DN values.
UAV-BASED GLACIER MONITORING: GNSS KINEMATIC TRACK POST-PROCESSING AND DIRECT GEOREFERENCING FOR ACCURATE RECONSTRUCTIONS IN CHALLENGING ENVIRONMENTS
Authors: Belloni V. , Fugazza D. , Di Rita M.
Published: May 2022

Abstract. Continuous monitoring of glaciers is of key importance to understand their morphological evolution over time and monitor the impact of climate change. Recently, Unmanned Aerial Vehicles (UAVs) have proven to be ideal candidates for glacier monitoring thanks to their flexibility and ease of processing with software packages. Traditionally, for high-accurate and geodetically relevant results, Ground Control Points (GCPs) need to be homogeneously distributed over the area of interest and manually identified in the imagery to guarantee accurate reconstructions. However, the GCP setup is always time consuming and, in many cases, a difficult operation due to logistic constraints. Nowadays, many UAVs offer GNSS Real Time Kinematic (RTK) capabilities that usually highly improve 3D reconstructions. However, there are circumstances in which an RTK solution cannot be directly achieved in the field. This is particularly frequent in challenging mountain environments such as glaciers. In such cases, post-processing UAV GNSS kinematic tracks could represent a powerful approach for improving the quality of 3D models. The goal of this work is to investigate the potential of UAV track post-processing combined with direct georeferencing for accurate 3D reconstructions without the need for GCPs in a complex environment of an Alpine glacier. The study area is Forni Glacier in the Rhaetian Alps, Italy. The data were acquired during two campaigns performed in August 2020 and August 2021 and include UAV images captured using a DJI Phantom 4 RTK and target positions measured with Leica GS18 I receivers. The data were processed using a pipeline entirely implemented in the Leica Infinity software that combines GNSS post-processing, a standard photogrammetric pipeline and a new tool to post-process GNSS kinematic tracks of UAVs. The approach based on UAV track post-processing and direct georeferencing was assessed using the acquired targets as Check Points (CPs) and compared to a standard photogrammetric approach in terms of glacier height loss computation. The results show Root Mean Square Errors (RMSEs) of the CPs below 4 cm for both the 2020 and 2021 campaigns. As for glacier height loss computation, the DPCs generated from the two surveys using a standard photogrammetric approach and a workflow based on UAV track post-processing and direct georeferencing were differentiated to compute the height differences of the glacier surfaces over one year. The two investigated approaches show similar results with an average height loss of 5 metres measured on the glacier tongue and demonstrate that UAV track post-processing can compensate for the RTK signal loss allowing accurate 3D reconstruction and eliminating the need for GCPs, especially if pre-calibration is performed.
MULTI SENSOR AND SMART GIMBAL FOR ENHANCED POWERLINE MONITORING ON HELICOPTER AND UAVS
Authors: Kunz N. , Kanand T. , Bochmann P. , Kemper G.
Published: May 2022

Abstract. The inspection of overhead high voltage power lines is important because power supply has to be seen as a critical infrastructure. Monitoring power lines using helicopters and UAVs requires a setup of various sensors. The combination of high-resolution aerial cameras for capturing better than 2 mm GSD, LiDAR collecting 200 point/m2, thermal cameras detecting hot spots in cm accuracy of anomalies and UV-cameras checking for corona discharges are important elements in the newest monitoring setups. The combination of online analytics with the Corona Camera (defined as a combination of UV detector and RGB monitoring camera) and the Smart Gimbal technology enhances the quality and speed of the inspection. Besides that, it reduces the need of too many sensors. We show different integrations and their pros and cons and as an outlook where the technology with respect of entering AI technologies will go. A basic task is the handling of the enormous data volume, at this point online analytics may help to reduce the data-volume beside a rapid response.
APPLICATION OF UAV SWARM SEMI-AUTONOMOUS SYSTEM FOR THE LINEAR PHOTOGRAMMETRIC SURVEY
Authors: Singh C. , Mishra V. , Harshit H. , Jain K. , Mokros M.
Published: May 2022

Abstract. Unmanned Aerial Vehicles (UAVs) are used as stand-alone systems for a variety of purposes from agriculture, and environmental monitoring through architecture, and construction to humanitarian missions. The advantage of UAV is high spatial and temporal resolution but on the other hand, the disadvantage is the small area of cover and time demanding data collection. As a result of technological advancements, the complexity of systems to unprecedented levels these disadvantages can be solved by UAV Swarm systems. A UAV Swarm system is defined as the utilization of more than one UAV that are cooperating together in a semi-autonomous or autonomous manner to achieve a common goal. There are numerous factors in play while designing a system as advanced as a UAV Swarm. In our experiment, we focused on the semi-autonomous concept of creating and deploying a UAV Swarm with three Small UAVs in master-slave architecture for high-resolution fine-scale mapping. We demonstrate the implementation of collective behaviour of UAV swarm for river bed mapping that considers all on-board systems, including high resolution georeferenced aerial photography and navigation using high accuracy GPS. The testing field for this study was a 13.3 ha linear area of Solani River in the Haridwar district of the state of Uttarakhand, India. Images were captured by all three UAVs (one leader and two followers) and 5 ground control points (GCP) were used for geo-referencing. Aerial Triangulation and Bundle Block adjustment were processed by photogrammetric software Pix4DMapper. This UAV swarm mapping concept generates standard accurate geospatial results of 1.24 cm GSD and RMS Error 0.023 meter. Assessing the proposed system's efficiency and accuracy after such processes are taken into account reduces the time and cost manifolds of the UAV surveying.
NEW DEVELOPMENTS IN LIDAR UAS SURVEYS. PERFORMANCE ANALYSES AND VALIDATION OF THE DJI ZENMUSE L1
Authors: Teppati Losè L. , Matrone F. , Chiabrando F. , Giulio Tonolo F. , Lingua A. , Maschio P.
Published: May 2022

Abstract. Thanks to the latest technological developments LiDAR (Light Detection And Ranging) sensors are no longer an exclusive feature of manned airborne platforms but they are close to becoming a commercial solution in the UAS (Uncrewed Aerial Systems) domain. The release on the market of the Zenmuse L1 by DJI (Dà-Jiāng Innovations) is a step further in this direction, thanks also to a substantial work of enhancement made by the Chinese company not only on the hardware side, but also on the software one. The research presented in this work is focused on the use of the L1 LiDAR for the 3D survey of built heritage, analysing the results of different tests to highlight first considerations on its performances and the point cloud quality. Considering its recent release, this sensor is still yet to be thoroughly analysed and validated and its performances to be assessed. LiDAR data has been acquired on a selected test site, documented also with traditional Terrestrial Laser Scanner (TLS) and UAS photogrammetry. The latter techniques (supported also by a topographic survey) will thus be exploited to generate the ground reference and to assess the quality and accuracy of the L1 dataset.
TIN-BASED ROBUST MOSAICKING OF UAV IMAGES WITH CONSECUTIVE IMAGE CONNECTION
Authors: Yoon S. , Kim T.
Published: May 2022

Abstract. UAV image mosaicking could be divided into processing in an absolute space and in a relative space. Among them, the latter technique generates a mosaicked image using only UAV’s EOP (Exterior Orientation Parameters) without additional spatial data. Since this technique uses only images, it is easy to use, but sometimes exhibits unstable quality. In this study, we adopted the mosaicking in a relative space by introducing the TIN (Triangulated Irregular Network) structure for consecutive image connectivity. This method refines a point cloud and extracts optimal images for mosaicking as a pre-processing. After that, this generates a TIN, and performs triangular unit mosaicking and image unit mosaicking to produce a mosaicked image. We evaluated the proposed method with datasets acquired by fixed wing and rotary wing UAVs. We compared this method’s results with Pix4Dmapper’s and the previously developed method’s results. Through the experimental results, we were able to verify that this method was less affected by external factors such as ground topology and UAV status. Moreover, we could confirm this method reduced over-transformation, and mismatch of the junction area between images on mosaicked images. It showed stable quality despite of using only images. Through the above results, our method also showed similar performance to Pix4Dmapper.
AUTOMATIC GEOMETRIC INSPECTION IN DIGITAL FABRICATION
Authors: Mawas K. , Maboudi M. , Gerke M.
Published: May 2022

Abstract. Additive Manufacturing in construction allows to create complex objects of different materials. Accordingly, an appropriate co-registration and comparison between the printed object and its digital model is needed for several purposes: quality control (QC) to ensure that tolerances are maintained and the realisation of a digital twin which holds the actual geometry. In this paper, we introduce an automated robotic data capturing and direct co-registration method. That is, a terrestrial laser scanner (TLS) is mounted on a robot to be moved freely in the printing room. In addition, various strategies are employed for quality control. The scanner is also mounted on the tripods, to validate the accuracy of our data capturing and co-registration solution. Furthermore, the experiment on real data is conducted for two different objects: a shotcrete printed object and a wax material object. Our experiments revealed that there is almost no influence on the scanner position accuracy compared to classical setup (mounting the TLS on tripod). The scanner position accuracy when mounted on tripods is 0.76 mm and the accuracy achieved by mounting the scanner on the robot is 1.03 mm. From the simulation it is noticed that C2M cannot detect missing extruded parts. To cope this problem, we used C2C and M3C2 algorithms. For real data scenario, without edge trimming and surface finishing it is challenging to interpret the data directly. However, M3C2 provided better results but it requires parameters tuning. In case of the surface finished object C2M and M3C2 algorithms have almost similar results.
OVERVIEW OF THE TERRESTRIAL ECOSYSTEM CARBON MONITORING SATELLITE LASER ALTIMETER
Authors: Li G. , Gao X. , Hu F. , Guo A. , Liu Z. , Chen J. , Liu C. , Nie S. , Fu A.
Published: May 2022

Abstract. The Terrestrial Ecosystem Carbon Monitoring satellite will be the first satellite of China with multi-beams laser altimeter and repetition frequency equal to GLAS on the world first laser altimetry satellite ICESat, but smaller diameter than GLAS. This satellite will be a milestone of Chinese satellite laser altimetry from single elevation control application to multi-scene applications, such as the forest height, water level measurement and so on. In this paper, the basic parameters of multi-beams laser altimeter on the terrestrial ecosystem carbon monitoring satellite are introduced and compared with other satellite laser altimeters, especially the GLAS and CALIOP, and the working mode is illustrated, which is equal to the GF-7 satellite but different to ICESat. The laser altimetry data products and data processing flow to the standard product is designed. Moreover, the geometric calibration method without field site for the multi-beams full waveform laser altimeter is proposed, which will be continued and improved from the GF-7 satellite laser altimeter geometric calibration without field by the terrain matching and waveform matching, and the high resolution multi-spectral nadir camera will be calibrated and used as the new footprint image with more high resolution and spectral information. The simulated sample data is introduced and illustrated for better understanding of the satellite laser altimetry data. At last, the application of this satellite laser altimetry data product is prospected, and the standard product SLA03 will be produced and released in the Land Satellite Remote Sensing Application Center.
ROBUST APPROACH FOR URBAN ROAD SURFACE EXTRACTION USING MOBILE LASER SCANNING 3D POINT CLOUDS
Authors: Nurunnabi A. , Teferle F. N. , Lindenbergh R. C. , Li J. , Zlatanova S.
Published: May 2022

Abstract. Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road scanning opportunity. Many methods are available for road pavement, curb and roadside way extraction. Most of them use classical approaches that do not mitigate problems caused by the presence of noise and outliers. In practice, however, laser scanning point clouds are not free from noise and outliers, and it is apparent that the presence of a very small portion of outliers and noise can produce unreliable and non-robust results. A road surface usually consists of three key parts: road pavement, curb and roadside way. This paper investigates the problem of road surface extraction in the presence of noise and outliers, and proposes a robust algorithm for road pavement, curb, road divider/islands, and roadside way extraction using MLS point clouds. The proposed algorithm employs robust statistical approaches to remove the consequences of the presence of noise and outliers. It consists of five sequential steps for road ground and non-ground surface separation, and road related components determination. Demonstration on two different MLS data sets shows that the new algorithm is efficient for road surface extraction and for classifying road pavement, curb, road divider/island and roadside way. The success can be rated in one experiment in this paper, where we extract curb points; the results achieve 97.28%, 100% and 0.986 of precision, recall and Matthews correlation coefficient, respectively.
DEEP LEARNING-BASED METHOD TO EXTEND THE TIME SERIES OF GLOBAL ANNUAL VIIRS-LIKE NIGHTTIME LIGHT DATA
Authors: Wang L. , Tan W. , Xu H. , He H. , Chen N. , Li D. , Chapman M. A. , Li J.
Published: May 2022

Abstract. The nighttime light (NTL) remote sensed imagery has been applied in monitoring human activities from many perspectives. As the two most widely used NTL satellites, the Defense Meteorological Satellite Program (DMSP) Operational Linescan System and the Suomi National Polar-orbiting Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) have different spatial and radiometric resolutions. Thus, some long-time series analysis cannot be conducted without effective and accurate cross-calibration of these two datasets. In this study, we proposed a deep-learning based model to simulate VIIRS-liked DMSP NTL data by integrating the enhanced vegetation index (EVI) data product from MODIS. By evaluating the spatial pattern of the results, the modified Self-Supervised Sparse-to-Dense networks delivered satisfying results of spatial resolution downscaling. The quantitative analysing of the simulated VIIRS-liked DMSP NTL with original VIIRS NTL showed a good consistency at the pixel level of four selected sub datasets with R2 ranging from 0.64 to 0.76, and RMSE ranging from 3.96-9.55. Our method presents that the deep learning model can learn from relatively raw data instead of fine processed data based on expert knowledge to cross-sensor calibration and simulation NTL data.
MAPPING WITH SKYSAT IMAGES
Authors: Jacobsen K.
Published: May 2022

Abstract. The very high-resolution space imagery now competes with some functions that were previously solved with aerial images. Several very high-resolution optical satellites with a ground sampling distance (GSD) of 1 m and smaller are currently active. Not all of these satellites take images worldwide. Nevertheless, it is not a problem to obtain up-to-date satellite images with a very high resolution. Mapping projects not only need to consider access and quality, but also cost-effectiveness. Of course, the economic framework conditions are decisive for the decision as to whether space images or very high-resolution satellite images should be used. With a total 21 SkySat satellites, low-cost satellites with very high resolution have changed the economic conditions. To keep costs and weights down, the Skysat satellites were not designed to offer the best direct geo-referencing performance, but this problem can be solved by automatic orientation in relation to existing orthoimages.In North Rhine-Westphalia, the cadastral maps must be checked at regular intervals to ensure that the buildings are complete. A test project examined whether this is possible with SkySat images. The geometric conditions and the image quality with the effective ground resolutions are investigated. Experiences from earlier publications could not be used. First the specific problem had to be solved, the resolution of the SkySat images was improved by lowering the satellite orbit altitude from 500 km to 450 km and by a better super resolution with 0.50 m ground sampling distance for the SkySat Collect orthoimages and in addition Planet improved their generation of Collect images. The required standard deviation of the object details of 4 m was achieved clearly as the effective ground resolution of 0.5 m if the angle of incidence is below 20°.
IMPORTANCE OF PRECISE GRAVITY FIELD MODELING IN DIRECT GEOREFERENCING AND AERIAL PHOTOGRAMMETRY: A CASE STUDY FOR SWEDEN
Authors: Bagherbandi M. , Jouybari A. , Nilfouroushan F. , Ågren J.
Published: May 2022

Abstract. Direct georeferencing of airborne mobile mapping systems is developing with unprecedented speed using GNSS/INS integration. Removal of systematic errors is required for achieving a high accurate georeferenced product in mobile mapping platforms with integrated GNSS/INS sensors. It is crucial to consider the deflection of verticals (DOV) in direct georeferencing due to the recently improved INS sensor accuracy. This study determines the DOV using Sweden’s EGM2008 model and gravity data. The influence of the DOVs on horizontal and vertical coordinates and considering different flight heights is assessed. The results confirm that the calculated DOV components using the EGM2008 model are sufficiently accurate for aerial photogrammetry purposes except for the mountainous areas because the topographic signal is not modeled correctly.
AUTOMATION OF AS-BUILT BIM CREATION FROM POINT CLOUD: AN OVERVIEW OF RESEARCH WORKS FOCUSED ON INDOOR ENVIRONMENT
Authors: Gourguechon C. , Macher H. , Landes T.
Published: May 2022

Abstract. While BIM (Building Information Modelling) appears as a solution to reduce the cost and environmental impact of buildings, its implementation on existing buildings is still a major challenge. In the last five years, an important number of publications on the topic have been published. This paper proposes an up-to-date overview about the automation of as-built BIM creation from point clouds, focused on indoor environment. It is structured in two main parts. The first one deals with the segmentation and classification of point clouds by storey, rooms, walls, and slabs. The second one focuses on the modelling, strictly speaking, of the main elements of an indoor scene. The approaches are grouped into principal ideas. Through the presentation of methods using new types of scanners and associated sensors, it highlights the promising use of other information in addition to 3D geometry.
ACCURATE CALCULATION OF TREE STEM TRAITS IN FORESTS BY LOCAL CORRECTION OF POINT CLOUD REGISTRATION
Authors: Kawasaki H. , Masuda H.
Published: May 2022

Abstract. In recent years, it has become important in forestry and forest research to accurately calculate tree stem traits from point clouds captured using the terrestrial laser scanner. However, it is difficult to accurately align a large number of trees in a forest over a large area. Therefore, the reliability of traits calculated from point clouds has been problematic. In this paper, we propose a method to automatically correct misaligned point-clouds and calculate accurate tree stem traits. In our method, a different registration matrix is calculated for each tree to correct the misalignment. When the target tree is specified, point-clouds measured in the vicinity of the target tree and points of the neighbor trees are selected for multi-view registration, and a registration matrix suitable for the target tree is calculated. The experimental results show that the proposed method is effective in correcting misalignment and precisely calculating tree stem traits.
AUTOMATIC ALGORITHM FOR GEOREFERENCING HISTORICAL-TO-NOWADAYS AERIAL IMAGES ACQUIRED IN NATURAL ENVIRONMENTS
Authors: Craciun D. , Le Bris A.
Published: May 2022

Abstract. Automatic georeferencing for historical-to-nowadays aerial images represents the main ingredient for supplying territory evolution analysis and environmental monitoring. Existing georeferencing methods based on feature extraction and matching reported successful results for multi-epoch aerial images acquired in structured and man-made environments. While improving the state-of- the-art of the multi-epoch georeferencing problem, such frameworks present several limitations when applied to unstructured scenes, such as natural feature-less environments, characterized by homogenous or texture-less areas. This is mainly due to the lack of structured areas which often results in sparse and ambiguous feature matches, introducing inconsistencies during the pose estimation process. This paper addresses the automatic georeferencing problem for historical aerial images acquired in unstructured natural environments. The research work presented in this paper introduces a feature-less algorithm designed to perform historical-to- nowadays image matching for pose estimation in a fully automatic fashion. The proposed algorithm operates within two stages: (i) 2D patch extraction and matching and (ii) 3D patch-based local alignment. The final output is a set of 3D patch matches and the 3D rigid transformation relating each homologous patches. The obtained 3D point matches are designed to be injected into traditional multi-views pose optimisation engines. Experimental results on real datasets acquired over Fabas area situated in France demonstrate the effectiveness of the proposed method. Our findings illustrate that the proposed georeferencing technique provides accurate results in presence of large periods of time separating historical from nowadays aerial images (up to 48 years time span).
ROBUST AND EFFECTIVE AIRBORNE LIDAR POINT CLOUD CLASSIFICATION BASED ON HYBRID FEATURES
Authors: Liao L. F. , Tang S. J. , Liao J. H. , Wang W. X. , Li X. M. , Guo R. Z.
Published: May 2022

Abstract. State-of-the-art point cloud classification methods mostly process raw point clouds, using a single point as the basic unit and calculating point cloud features by searching local neighbors via the k-neighborhood method. Such methods tend to be computationally inefficient and have difficulty obtaining accurate feature descriptions due to inappropriate neighborhood selection. In this paper, we propose a robust and effective point cloud classification approach that integrates point cloud supervoxels and their locally convex connected patches into a random forest classifier. We apply a centroid cloud extracted from supervoxels into the proposed classifier, which effectively improves the point cloud feature calculation accuracy and reduces the computational cost. Considering the different types of point cloud feature descriptions, we divide features into three categories (point-based, eigen-based, and grid-based) and accordingly design three distinct feature calculation strategies to improve feature reliability. The proposed method achieves state-of-the-art performance, with average F1-scores of 89.16%, respectively. The successful classification of point clouds with great variation in elevation also demonstrates the reliability of the proposed method in challenging scenes to some extents.
GLOBALLY OPTIMAL POINT CLOUD REGISTRATION FOR ROBUST MOBILE MAPPING
Authors: Skuddis D. , Haala N.
Published: May 2022

Abstract. Point cloud registration algorithms have been studied for several decades. In the SLAM domain, dense local convergence based methods are typically used to register consecutive scans. Since these procedures are not globally optimal, it happens that they converge to a wrong local minimum. This can lead to gross errors during mapping and can make entire datasets unusable. We introduce a new branch and bound based point cloud registration method that is globally optimal. The method is able to reliably determine the global optimum within a given parameter search space. We show how this method can be used in a mapping system as a fallback function to correct gross errors. Using various public datasets, we demonstrate the capabilities of the method.
BUILDING DETECTION FROM AERIAL LIDAR POINT CLOUD USING DEEP LEARNING
Authors: Su S. , Nakano K. , Wakabayashi K.
Published: May 2022

Abstract. With the development and widespread application of aerial LiDAR, point cloud data can easily be acquired and used in many fields. The accurate detection of buildings from an aerial LiDAR point cloud has attracted much attention owing to its wide range of applications, such as updating building maps and constructing 3D city models. However, such applications remain challenging in the fields of photogrammetry, remote sensing, and computer vision. In this paper, we discuss the features that contribute to building detection accuracy from an aerial LiDAR point cloud using a deep-learning-based method (KPConv). We evaluated the influence of neighborhood size, intensity, RGB, and normal vectors on building detection. The study area was approximately 6 km2, consisting of 133 million points and 8,099 buildings. The density of the point cloud data was eight points/m2. We compared search radii of 4, 10, 25, and 50 m for finding neighboring points. The results suggest that an optimal neighborhood size improves the accuracy of building detection. For searching neighboring points, a radius of 25 m is optimal when the building area is less than 1000 m2, whereas a radius of 50 m is optimal when the building area is larger than 1000 m2.We also compared different features as inputs to KPConv for training and testing, such as i) 3D coordinates only, ii) 3D coordinates and intensity, iii) 3D coordinates and RGB, iv) 3D coordinates and normal vectors, and v) 3D coordinates, intensity, RGB, and normal vectors. The results suggest that neither intensity nor normal vectors contribute to the accuracy of building detection, while the features of RGB have a limited effect on the results.
COMPREHENSIVE QUANTITATIVE UNDERSTANDING OF THE LANDSCAPE USING TLS POINT CLOUD DATA
Authors: Tachikawa R. , Kunii Y.
Published: May 2022

Abstract. Landscape spaces such as gardens and parks are composed of various landscape components, creating diverse landscapes. In general, the quality of the landscape in these spaces is often judged subjectively by visitors. On the other hand, if landscapes can be evaluated objectively, they can be used to create better spaces in the management and creation of landscaped spaces. In recent years, point cloud data has been acquired in urban and natural spaces. In landscaped spaces, point cloud data is increasingly used for landscape simulation and current state planning. In this study, point cloud data acquired with a terrestrial laser scanner (TLS) in the target space were used to quantitatively characterize the entire landscape using fractal analysis and visual and ecological environmental quality models (VQM). We also segmented these data into components of the point cloud data and calculated the relationship between the data and the occupancy of the components. On the other hand, focusing on environmental visual information received passively from a wide range of environments, we conducted an analysis based on panoramic images created from point cloud data. As a result, both fractal analysis and VQM showed a high correlation with previous research methods in understanding the landscape using point cloud data. In addition, the analysis of the landscape was made more efficient than the conventional photographic analysis by segmenting the components in advance at the data processing stage, demonstrating the usefulness of landscape analysis from data acquired by laser scanners.
PROBABILISTIC SILHOUETTE-BASED CLOSE-RANGE PHOTOGRAMMETRY USING A NOVEL 3D OCCUPANCY-BASED RECONSTRUCTION
Authors: Asl Sabbaghian Hokmabadi I. , El-Sheimy N.
Published: May 2022

Abstract. Digital three-dimensional (3D) reconstruction of objects has many applications in computer vision, archaeology, and the entertainment industry. Digital 3D reconstruction can be used to preserve the appearance of valuable historical artifacts; it can be used to track the pose of an object in the images, and it can facilitate object modelling. 3D reconstruction of objects in the past has been achieved using many sensors such as cameras and laser-strip scanners. Monocular camera-based object 3D modelling can be categorized into sparse feature detector/descriptor-based and dense silhouette-based approaches. Feature-based methods identify distinctive features on the objects (captured from many images). In contrast, silhouette-based methods only require a distinguishable boundary between the object and the background. Silhouette-based methods have the advantage that in the controlled setups, a special background can be designed to be distinguishable from the object of interest; therefore, uniquely identifiable textures on the object’s surface are not required. Despite their advantages, silhouette-based probabilistic reconstruction remains a challenge. This article proposes a new probabilistic approach using 3D occupancy grids for the silhouette-based digital reconstruction of an object. The proposed method is designed to be usable with monocular cameras and achieves an accurate reconstruction using only sixteen images. Compared to similar silhouette-based volumetric approaches, the voxels are not discarded immediately during the reconstruction, and the occupancy grid mapping continuously changes the occupancy probability of the voxels with each new image included.