bluuebunny/crossref_metadata_2025 huggingface dataset:
Transforming “Living Labs” into: ”Lighthouses”: a promising policy to achieve land-related sustainable development?
Authors: Bouma Johan
Published: May 2022

Abstract. The until that time rather abstract debate about sustainable development has been focused by introducing the United Nations Sustainable Development Goals (SDGs) in 2015 and the related European Green Deal in 2019. Restricting attention to agriculture, proposed targets and indicators are, however, not specific enough to allow a focus for developing innovative and sustainable management practices. Clarity is needed because farmers are suspicious of Governmental actions defining environmental rules and regulations. The European policy arena has recognized this problem and has presented the Mission concept that requires joint learning between farmers, scientists and citizens. For the soil Mission, “Living Labs” are proposed that should evolve into: “Lighthouses” when environmental thresholds for each of at least six land-related ecosystem services,are met. This presents “wicked” problems that can be “tamed” by measuring ecosystem services in a given :”Living Lab” that are associated with the land-related SDGs. Thresholds with a regional character are needed to seperate the “good” from the “not good enough”. Contributions by the soil to ecosystem services can be expressed by assessing soil health. By introducing the Mission concept, the policy arena challenges the research community to rise to the occasion by developing effective interaction models with farmers and citizens that can be the foundation for innovative and effective environmental rules and regulations. We argue and illustrate with a specific example, that establishing :”:Living Labs” can be an important, if not essential, contribution to realizing the lofty goals of the SDGs and the Green Deal.
Vertical profiles of cloud condensation nuclei number concentration and its empirical estimate from aerosol optical properties over the North China Plain
Authors: Zhang Rui , Wang Yuying , Li Zhanqing , Wang Zhibin , Dickerson Russell R. , Ren Xinrong , He Hao , Wang Fei , Gao Ying , Chen Xi , Xu Jialu , Cheng Yafang , Su Hang
Published: May 2022

Abstract. To better understand the characteristics of aerosol activation ability and optical properties, a comprehensive airborne campaign was implemented over the North China Plain (NCP) from May 8 to June 11, 2016. Vertical profiles of cloud condensation nuclei (CCN) number concentration (NCCN) and aerosol optical properties were measured simultaneously. Seventy-two-hour air mass back trajectories show that during the campaign the measurement region is mainly influenced by air masses in northwest and southeast. Air mass sources, temperature structure, anthropogenic emissions, and terrain distribution are factors influencing NCCN profiles. CCN spectra suggest that the ability of aerosol activation into CCN is stronger in southeast air masses than in northwest air masses and stronger in the free atmosphere than near the surface. Vertical distributions of aerosol scattering Ångström exponent (SAE) indicate that aerosols near the surface mainly originate from primary emissions consisting of more fine particles. The combined effect of aerosol lifting aloft and long-distance transport increase SAE and make it vary more in the free troposphere than near the surface. For parameterizing NCCN, the equation NCCN = 10β ∙ σγ is used to fit the relationship between NCCN and the aerosol scattering coefficient (σ) at 450 nm. The fitting parameters β and γ have linear relationships with the SAE. Empirical estimates of NCCN at 0.7% water vapor supersaturation (ss) from aerosol optical properties are thus retrieved for the two air masses: NCCN = 10-0.22∙SAE+2.39 ∙ σ0.30∙SAE+0.29 for northwest air masses and NCCN =10-0.07∙SAE+2.29 ∙ σ0.14∙SAE+0.28 for southeast air masses. The estimated NCCN at 0.7 % ss agrees with that measured, although the performance differs between low and high concentrations in the two air masses. The results highlight the important impact of aerosol sources on the empirical estimate of NCCN from aerosol optical properties.
Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)
Authors: van Wees Dave , van der Werf Guido R. , Randerson James T. , Rogers Brendan M. , Chen Yang , Veraverbeke Sander , Giglio Louis , Morton Douglas C.
Published: May 2022

Abstract. In fire emission models, the spatial resolution of both the modelling framework and the satellite data used to quantify burned area can have considerable impact on emission estimates. Consideration of this sensitivity is especially important in areas with heterogeneous land cover and fire regimes, and when constraining model output with field measurements. We developed a global fire emissions model with a spatial resolution of 500 m using MODerate resolution Imaging Spectroradiometer (MODIS) data. To accommodate this spatial resolution, our model is based on a simplified version of the Global Fire Emissions Database (GFED) modelling framework. Tree mortality as a result of fire, i.e. fire-related forest loss, was modelled based on the overlap between 30-m forest loss data and MODIS burned area and active fire detections. Using this new 500-m model, we calculated global average carbon emissions from fire of 2.1 ± 0.2 (±1σ interannual variability; IAV) Pg C yr–1 during 2002–2019. Fire-related forest loss accounted for 2.5 ± 0.9 % (uncertainty range = 1.9–3.2 %) of global burned area and 25 ± 6 % (uncertainty range = 18–32 %) of emissions, indicating that fuel consumption in forest fires is an order of magnitude higher than the global average. Emissions from the combustion of soil organic carbon in the boreal region and tropical peatlands accounted for 14 ± 4 % of global emissions. Our global fire emissions estimate was higher than the 1.5 Pg C yr–1 from GFED4 and similar to 2.1 Pg C yr–1 from GFED4s. Even though GFED4s included more burned area by accounting for small fires undetected by the MODIS burned area mapping algorithm, our emissions were similar to GFED4s due to higher average fuel consumption. The global difference in fuel consumption could mainly be explained by higher SOC emissions from the boreal region as constrained by additional measurements. The higher resolution of the 500-m model also contributed to the difference by improving the simulation of landscape heterogeneity and reducing the scale mismatch in comparing field measurements to model grid cell averages during model calibration. Furthermore, the fire-related forest loss algorithm introduced in our model led to more accurate and widespread estimation of high-fuel consumption burned area. Recent advances in burned area detection at resolutions of 30 m and finer show a substantial amount of burned area that remains undetected with 500-m sensors, suggesting that global carbon emissions from fire are likely higher than our 500-m estimates. The ability to model fire emissions at 500-m resolution provides a framework for further improvements with the development of new satellite-based estimates of fuels, burned area, and fire behaviour, for use in the next generation of GFED.
Quantify Oceanic Moisture Contribution to the Tibetan Plateau
Authors: Li Ying , Wang Chenghao , Haung Ru , Yan Denghua , Peng Hui , Xiao Shangbin
Published: May 2022

Abstract. Evaporation from global oceans is an important moisture source of glaciers and headwaters of major Asia rivers in the Tibetan Plateau (TP). Although recent accelerated global hydrological cycle, altered sea-land thermal contrast, and amplified warming rate over the TP are known to have profound effects on the regional water balance, the contribution of oceanic evaporation, in particular its spatial variability over the vast TP, remains unclear. The lack of such knowledge hinders an accurate quantification of regional water budgets and the reasonable interpretation of water isotope records from observations and paleo archives. Based on historical data and moisture tracking, this study systematically quantifies the absolute and relative contributions of oceanic moisture to the long-term TP precipitation. Results show that seasonal absolute and relative oceanic contributions are generally out of phase, revealing previously overlooked oceanic moisture contributions to the TP in winter as dominated by the westerlies and overestimated moisture contribution from the Indian Ocean in summer. Especially, the relative contribution of moisture from Indian Ocean is only ~30 % in the southern TP and further decreases to below 10 % in the northern TP. Absolute oceanic contributions explain the dipole pattern of long-term precipitation trends across the southern slope of the Himalayas and the central-northern TP. In comparison, the seasonality of relative oceanic contribution is associated with that of precipitation isotopes across the TP.
A 3D SEGMENTS BASED ALGORITHM FOR HETEROGENEOUS DATA REGISTRATION
Authors: Djahel R. , Monasse P. , Vallet B.
Published: May 2022

Abstract. Combining image and LiDAR draws increasing interest in surface reconstruction, city and building modeling for constructing 3D virtual reality models because of their complementary nature. However, to gain from this complementarity, these data sources must be precisely registered. In this paper, we propose a new primitive based registration algorithm that takes 3D segments as features. The objective of the proposed algorithm is to register heterogeneous data. The heterogeneity is both in data type (image and LiDAR) and acquisition platform (terrestrial and aerial). Our algorithm starts by extracting 3D segments from LiDAR and image data with state of the art algorithms. Then it clusters the 3D segments of each data according to their directions. The obtained clusters are associated to find possible rotations, then 3D segments from associated clusters are matched in order to find the translation and scale factor minimizing a distance criteria between the two sets of 3D segments. Two optimizers (simulated annealing and RANSAC) are tested to minimize this distance criterion, first on synthetic data, then on real data. The experiments carried out demonstrate the robustness and speed of RANSAC compared to simulated annealing.
CORROSION DETECTION FOR INDUSTRIAL OBJECTS: FROM MULTI-SENSOR SYSTEM TO 5D FEATURE SPACE
Authors: Haitz D. , Jutzi B. , Hübner P. , Ulrich M.
Published: May 2022

Abstract. Corrosion is a form of damage that often appears on the surface of metal-made objects used in industrial applications. Those damages can be critical depending on the purpose of the used object. Optical-based testing systems provide a form of non-contact data acquisition, where the acquired data can then be used to analyse the surface of an object. In the field of industrial image processing, this is called surface inspection. We provide a testing setup consisting of a rotary table which rotates the object by 360 degrees, as well as industrial RGB cameras and laser triangulation sensors for the acquisition of 2D and 3D data as our multi-sensor system. These sensors acquire data while the object to be tested takes a full rotation. Further on, data augmentation is applied to prepare new data or enhance already acquired data. In order to evaluate the impact of a laser triangulation sensor for corrosion detection, one challenge is to at first fuse the data of both domains. After the data fusion process, 5 different channels can be utilized to create a 5D feature space. Besides the red, green and blue channels of the image (1–3), additional range data from the laser triangulation sensor is incorporated (4). As a fifth channel, said sensor provides additional intensity data (5). With a multi-channel image classification, a 5D feature space will lead to slightly superior results opposed to a 3D feature space, composed of only the RGB channels of the image.
AN OVERVIEW OF ROAD HEALTH MONITORING SYSTEM FOR RIGID PAVEMENT BY TERRESTRIAL LASER SCANNER
Authors: Bhatt A. , Bharadwaj S. , Sharma V. B. , Dubey R. , Biswas S.
Published: May 2022

Abstract. Structural health monitoring (SHM) applications for roads should be created in order to save finances, protect public safety, and provide long-lasting road infrastructure. The terrestrial laser scanner (TLS) will be employed in this project for collecting data, used for monitoring purposes. LiDAR camera mounted on moving vehicle generating 3D point cloud is used for monitoring purpose. Poorly maintained roads result in lower productivity, higher fuel consumption, increased mechanical wear, hazardous operating conditions, driver discomfort, and higher rolling resistances. Road management agencies suffer with pavement repair methods and the finances to keep the existing road networks in good working order. The goal of this research work is to create a low-cost smart road health monitoring system that uses camera-based monitoring and smart phone sensors to identify the road section for maintenance. We have discovered that using accelerometers for pothole detection is ideal for this application. The road patches or pot holes for 2 km area of the RGIPT campus using accelerometer is being done. The smart phone will upload the position and any kind of undulated road surface to the cloud when the vehicle passes over it. Use of accelerometer may detect internal damage of the pavements before it appears on the top surface of the road. When other vehicles move towards an irregular road surface, the cloud will issue an undulated road surface reminder to make sure that the vehicle may safely and smoothly drive through the area. The system is simply dependent on a single phone setting and uses raw accelerometer measurements, which can record irregular driving or quick brakes. The data in this system are collected from the mobile phone and sensor for monitoring and forecasting of road surface. So, every pavement defect has different classification and treatment approach, as well as severity levels.
SFM-BASED 3D RECONSTRUCTION OF HERITAGE ASSETS USING UAV THERMAL IMAGES
Authors: Patrucco G. , Giulio Tonolo F. , Sammartano G. , Spanò A.
Published: May 2022

Abstract. In the last few years, notable progress has been made in the field of non-invasive diagnostic for the monitoring of heritage assets. In particular, multispectral imagery (more specifically thermal images will be addressed in this manuscript) allows investigations in the non-visible range of the electro-magnetic spectrum to be effectively carried out. Many researchers are currently exploring the possibilities related to the use of this kind of images in photogrammetric SfM-based processes to produce 2D and 3D value-added metric products, characterised by high level of detail and spatial resolution, including the information connected to the non-visible data. A data fusion-based strategy enables co-registering visible and thermal images in order to exploit the higher spatial resolution of the traditional true colour images. However, there are still many shortcomings to be addressed to properly and efficiently orient TIR (Thermal Infrared) images, connected (among other factors) to their low spatial resolution, or to the low contrast between adjacent materials characterised by similar emissivity. This paper proposes two different workflows to process thermal images using SfM algorithms, applied to three different case studies, each characterised by different characteristics and features (size, morphology, emissivity of the materials, etc.). The different pipelines are described and the obtained results are critically evaluated considering the metric accuracy, 3D geometric reconstruction and noise, completeness of the data and overall quality of the generated dense point cloud. Additionally, the effectiveness of the adopted strategies in connection with the peculiar features of the analysed case studies is also considered.
COMBINING LIDAR SLAM AND DEEP LEARNING-BASED PEOPLE DETECTION FOR AUTONOMOUS INDOOR MAPPING IN A CROWDED ENVIRONMENT
Authors: Tiozzo Fasiolo D. , Maset E. , Scalera L. , Macaulay S. O. , Gasparetto A. , Fusiello A.
Published: May 2022

Abstract. In this paper, we present a mapping system based on an autonomous mobile robot equipped with a LiDAR device and a camera, that can deal with the presence of people. Thanks to a deep learning approach, the position of humans is identified and a new surveying path is planned that brings the robot to scan occluded areas, so as to obtain a complete point cloud of the environment. Experimental results are performed with a wheeled mobile robot in different crowded scenarios, showing the applicability of the proposed approach to perform an autonomous survey avoiding occlusions and automatically removing from the map noisy and spurious objects caused by people presence.
REAL-TIME FOREGROUND SEGMENTATION FOR SURVEILLANCE APPLICATIONS IN NRCS LIDAR SEQUENCES
Authors: Kovács L. , Kégl M. , Benedek C.
Published: May 2022

Abstract. In this paper, we propose a point-level foreground-background separation technique for the segmentation of measurement sequences of a Non-repetitive Circular Scanning (NRCS) Lidar sensor, which is used as a 3D surveillance camera mounted in a fixed position. We show that by applying the NRCS Lidar technology, we can overcome various limitations of rotating multi-beam Lidar sensors, such as low vertical measurement resolution, which is disadvantageous in surveillance applications. As the main challenge, we need to efficiently balance between the spatial and the temporal resolution of the recorded range data. For this reason, we automatically generate and maintain a very high-resolution background model of the sensor’s Field of View, while for enabling real-time analysis of dynamic objects we use low integration time to extract the consecutive time frames. As a result, the laser reflections from foreground objects reflect sparse, but geometrically accurate samples of the silhouettes providing valuable input for higher-level shape description or event analysis steps. We demonstrate the efficiency of the new approach in different realistic NRCS Lidar measurements sequences, obtaining a 0.76 overall F1-score on the measured dataset.
AUTONOMOUS SENSING AND LOCALIZATION OF A MOBILE ROBOT FOR MULTI-STEP ADDITIVE MANUFACTURING IN CONSTRUCTION
Authors: Lachmayer L. , Recker T. , Dielemans G. , Dörfler K. , Raatz A.
Published: May 2022

Abstract. In contrast to stationary systems, mobile robots have an arbitrarily expandable workspace. As a result, the spatial dimensioning of the task to be mastered plays only a subordinate role and can be scaled as desired. For the construction industry in particular, which requires the handling and production of substantial components, mobile robots mean an unlimited expansion of the workspace based on their mobility levels and thus increased flexibility. The greatest challenge in mobile robotics lies with the discrepancy between the precision required for the task and the achievable positioning accuracy. External localization systems show significant potential for improvement in this respect but, in many cases, require a line of sight between the measurement system and the robot or a time-consuming calibration of markers. Therefore, this article presents an approach for an onboard localization system for use in a multi-step additive manufacturing processes for building construction. While a SLAM algorithm is used for the initial estimation of the robot’s base at the work site, in a refined estimation step, the positioning accuracy is enhanced using a 2D-laser-scanner. This 2D-scanner is used to create a 3D point cloud of the 3D-printed component each time after a print job of one segment has been carried out and before continuing a print job from a new location, to enable printing of layers on top of each other with sufficient accuracy over many repositioning manouvres. When the robot returns to a position for print continuation, the initial and the new point clouds are compared using an ICP-algorithm, and the resulting transformation is used to refine the robot’s pose estimation relative to the 3D-printed building component. While initial experiments demonstrate the approach’s potential, transferring it to large-scale 3D-printed components presents additional challenges, highlighted in this paper.
COMPARISON OF PHOTOGRAMMETRY TOOLS CONSIDERING REBAR PROGRESS RECOGNITION
Authors: Qureshi A. H. , Alaloul W. S. , Murtiyoso A. , Saad S. , Manzoor B.
Published: May 2022

Abstract. Construction progress monitoring is an important process throughout the project timeline towards its successful completion. Among imaging techniques, photogrammetry is considered as economical and effective method. However, few studies can be found on construction progress monitoring via photogrammetry; thus, not much guideline is available for this domain. This study evaluated the photogrammetry tools for the progress assessment of the rebar grid framework. Photogrammetry tools were evaluated and analysed following defined criteria, and Agisoft Metashape, and 3DF Zephyr were identified as better options. This study aims to provide a guideline to construction industry professionals and stakeholders towards the adoption of photogrammetric progress assessment for construction activities.
INTEGRATION OF DEPTH MAPS FROM ARCORE TO PROCESS POINT CLOUDS IN REAL TIME ON A SMARTPHONE
Authors: Haenel R. , Semler Q. , Semin E. , Grussenmeyer P. , Alby E.
Published: May 2022

Abstract. Real-world three-dimensional reconstruction is a project of long-standing interest in global computer vision. Many tools have emerged these past years to accurately perceive the surrounding world either through active sensors or through passive algorithmic methods. With the advent and popularization of augmented reality on smartphones, new visualization issues have emerged concerning the virtual experience. Especially a 3D model seems to be essential to provide more realistic AR effects including consistency of occlusion, shadow mapping or even collision between virtual objects and real environment. However, due to the huge computation of most of current approaches, most of these algorithms are working on a computer desktop or high-end smartphones. Indeed, the reconstruction scale is rapidly limited by the complexity of both computation and memory. Therefore, our study aims to find a relevant method to process real time reconstruction of close-range outdoor scenes such as cultural heritage or underground infrastructures in real time locally on a smartphone.
STORM DRAIN DETECTION AND LOCALISATION ON MOBILE LIDAR DATA USING A PRE-TRAINED RANDLA-NET SEMANTIC SEGMENTATION NETWORK
Authors: Mattheuwsen L. , Bassier M. , Vergauwen M.
Published: May 2022

Abstract. As the expansion of cities and urban areas results in the construction of more impermeable road surfaces, a well designed urban drainage system becomes of greater importance. However, the accurate and up-to-date mapping of storm drains necessary to create accurate drainage models is often lacking. In recent years, mapping of the road infrastructure is increasingly carried out by highly efficient mobile mapping systems but which lack automatic interpretation of the massive amount data. In this paper we present a fully automatic storm drain detection method to extract and locate storm drain inlets in mobile mapping lidar data. The point cloud is first segmented by a pre-trained RandLa-Net model, which although untrained to segment storm drains, is able the segment storm drain clusters in the hardscape class. The results from this class are further processed by enforcing different requirements to only extract and locate storm drain clusters. Our approach is evaluated on a large testing dataset with 171 storm drains and achieves 81.9%, 95.2% and 88.1% for recall, precision and F1-score respectively. The majority of the false positive and false negative detections are due to incorrect point cloud segmentation of the RandLa-Net. In terms of localisation, our approach achieves an RMSE of 5.5 cm on the centre location while the dimensions of the bounding box are on average 23% off compared to the ground truth.
PORTABLE MULTI-CAMERA SYSTEM: FROM FAST TUNNEL MAPPING TO SEMI-AUTOMATIC SPACE DECOMPOSITION AND CROSS-SECTION EXTRACTION
Authors: Perfetti L. , Elalailyi A. , Fassi F.
Published: May 2022

Abstract. The paper outlines the first steps of a research project focused on the digitalization of underground tunnels for the mining industry. The aim is to solve the problem of rapidly, semi-automatically, efficiently, and reliably digitizing complex and meandering tunnels. A handheld multi-camera photogrammetric tool is used for the survey phase, which allows for the rapid acquisition of the image dataset needed to produce the 3D data. Moreover, since often, automatic, and fast acquisitions are not supported by easy-to-use tools to access and use the data at an operational level, a second aim of the research is to define a method able to arrange and organise the gathered data so that it would be easily accessible. The proposed approach is to compute the 3D skeleton of the surveyed environment by employing tools developed for the analysis of vascular networks in medical imagery. From the computed skeletonization of the underground tunnels, a method is proposed to automatically extrapolate valuable information such as cross-sections, decomposed portions of the tunnel, and the referenced images from the photogrammetric survey. The long-term research goal is to create an effective workflow, both at the hardware and software level, that can reduce computation times, process large amounts of data, and reduce dependency on high levels of experience.
RELATIVE ACCURACY FOUND WITHIN IPHONE DATA COLLECTION
Authors: Tamimi R.
Published: May 2022

Abstract. The use of consumer gadgets such as smartphones can be incorporated in high accuracy LiDAR mapping with a proper assessment of sensors. Apple’s iPhone 13 Pro includes a LiDAR scanner that can create mapping projects with standard point cloud exports using the help of the built-in gyroscope and accelerometer, the iPhone can generate a 3D map by calculating its position and movement as the user navigates around a site. Its ability to create these maps brings up the question of the level of relative accuracy of the iPhone by analyzing and comparing the data to a high accuracy surveying total station and increasing the accuracy by utilizing sensor integration. This paper strives to achieve a better understanding of the relative accuracy found within iPhone data collection.
AUTO-ADAPTIVE MULTI-LEVEL SEAFLOOR RECOGNITION AND LAND SEA CLASSIFICATION (AMSRLC) IN REEF-ISLAND ZONES USING ICESAT-2 LASER ALTIMETRY
Authors: Xu Q. , Xie H. , Sun Y. , Liu X. , Guo Y. , Huang P. , Li B. , Tong X.
Published: May 2022

Abstract. The world’s first photon-counting laser altimetry satellite, the Ice, Cloud and land Elevation Satellite-2 (ICESat-2), which was launched in 2018, has proven to have a certain bathymetric capability, which provides a new means for the surveying of island and reef zones. However, how to accurately extract and separate land surface signal photons, sea surface signal photons, and seafloor signal photons in these areas has not yet been resolved. In this paper, we propose a validated auto-adaptive multi-level classification algorithm (AMSRLC), which can realize automatic recognition and classification of seafloor, sea surface, and land surface photons in island and reef zones. A overlapping histogram, a slope-adaptive search ellipse, and a water depth adaptive local signal-to-noise ratio are respectively used to extract flat sea surface signals, island and reef surface signals with slope changes, and seafloor signals that weaken with water depth. The overall classification indicators OA, AA, and Kappa reached 0.993, 0.973, and 0.987 respectively. The algorithm can effectively detect various signals with high detection accuracy.
DEEP LEARNING FOR 3D BUILDING RECONSTRUCTION: A REVIEW
Authors: Buyukdemircioglu M. , Kocaman S. , Kada M.
Published: May 2022

Abstract. 3D building reconstruction using Earth Observation (EO) data (aerial and satellite imagery, point clouds, etc.) is an important and active research topic in different fields, such as photogrammetry, remote sensing, computer vision and Geographic Information Systems (GIS). Nowadays 3D city models have become an essential part of 3D GIS environments and they can be used in many applications and analyses in urban areas. The conventional 3D building reconstruction methods depend heavily on the data quality and source; and manual efforts are still needed for generating the object models. Several tasks in photogrammetry and remote sensing have been revolutionized by using deep learning (DL) methods, such as image segmentation, classification, and 3D reconstruction. In this study, we provide a review on the state-of-the-art machine learning and in particular the DL methods for 3D building reconstruction for the purpose of city modelling using EO data. This is the first review with a focus on object model generation based on the DL methods and EO data. A brief overview of the recent building reconstruction studies with DL is also given. We have investigated the different DL architectures, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and the combinations of conventional approaches with DL in this paper and reported their advantages and disadvantages. An outlook on the future developments of 3D building modelling based on DL is also presented.
EXPLORING THE POTENTIALS OF UAV PHOTOGRAMMETRIC POINT CLOUDS IN FAÇADE DETECTION AND 3D RECONSTRUCTION OF BUILDINGS
Authors: Mwangangi K. K. , Mc’Okeyo P. O. , Oude Elberink S. J. , Nex F.
Published: May 2022

Abstract. The use of Airborne Laser Scanner (ALS) point clouds has dominated 3D buildings reconstruction research, thus giving photogrammetric point clouds less attention. Point cloud density, occlusion and vegetation cover are some of the concerns that promote the necessity to understand and question the completeness and correctness of UAV photogrammetric point clouds for 3D buildings reconstruction. This research explores the potentials of modelling 3D buildings from nadir and oblique UAV image data vis a vis airborne laser data. Optimal parameter settings for dense matching and reconstruction are analysed for both UAV image-based and lidar point clouds. This research employs an automatic data driven model approach to 3D building reconstruction. A proper segmentation into planar roof faces is crucial, followed by façade detection to capture the real extent of the buildings’ roof overhang. An analysis of the quality of point density and point noise, in relation to setting parameter indicates that with a minimum of 50 points/m2, most of the planar surfaces are reconstructed comfortably. But for smaller features than dormers on the roof, a denser point cloud than 80 points/m2 is needed. 3D buildings from UAVs point cloud can be improved by enhancing roof boundary by use of edge information from images. It can also be improved by merging the imagery building outlines, point clouds roof boundary and the walls outline to extract the real extent of the building.
3D DOCUMENTATION TOWARDS HERITAGE CONSERVATION: A RAPID MAPPING APPROACH APPLIED TO BAHRAIN FORTS
Authors: Chiabrando F. , Patrucco G. , Rinaudo F. , Sammartano G. , Scolamiero V. , Vileikis O. , Hasaltun Wosinski M.
Published: May 2022

Abstract. Rapid mapping techniques based on UAV photogrammetry and portable systems are increasingly helping cultural heritage sites’ digitization, in case of large contexts or complex accessibility. At the same time, a wider range of more consolidated image- and range-based approaches could be useful, but challenging to be applied depending on materials, accessibility, or light conditions. However, the fundamental issue of integrating and optimizing the large amount of metric data of different origins in 3D accurate models remains open. An integrated system is needed to support the work of expert conservator and restorers, as well as archaeological investigation, on onsite work and, mainly, on remote activity. This paper aims to present the metric survey project carried out in the Fort component of the World Heritage property Qal'at al-Bahrain – the Ancient Harbour and Capital of Dilmun. The Bahrain Authority for Culture and Antiquities (BACA) started the initiative towards the conservation of the historic forts of the Kingdom of Bahrain. This requires a tailored approach to the uniqueness and outstanding significance of the sites. The digital products are baseline data and support the future conservation decisions. The validation of the integrated approach adopted for the survey of the Qal’at al-Bahrain will be presented and discussed in this paper. The validation aims for the integrated pipelines are targeted focusing on the aspects connected to the repeatability and the applicability of the methodological approach in the other sites with the same context and features, and this may also lead to further future reflections related to the sustainability of the survey.
DEVELOPMENT OF MULTI-SENSOR MODULE MOUNTED MOBILE ROBOT FOR DISASTER FIELD INVESTIGATION
Authors: Jung Y. H. , Cho D. H. , Hong J. W. , Han S. H. , Cho S. B. , Shin D. Y. , Lim E. T. , Kim S. S.
Published: May 2022

Abstract. Disasters are not easy to predict because they occur suddenly, and the scale of disasters is increasing compared to the past. Since a new type of disaster field always appears, when a disaster occurs, responders who are put into the site recognize the same risk as secondary damage and are put into the field. In this regard, the robot performs missions such as search and rescue in the initial response process at the disaster field. It is a technology with high potential to reduce damage to people and property. Most of the robots are equipped with site accessibility and monitoring through cameras and remote control, but considering the specificity of the disaster field, it is not easy to fully monitor the site with a camera sensor as the possibility of poor visibility is very high. LiDAR uses a laser to recognize the distance to a nearby object in a relatively wide range and acquires three-dimensional information, so its accuracy and precision are higher than other sensors.In this study, one multi-sensor module was manufactured by combining LiDAR and IMU sensor with a computing board for real-time monitoring so that it could be used in the field of robots. In addition, we studied how to stably mount this multi-sensor module to a robot so that it can maintain optimal accuracy at disaster field, and it is intended to be utilized as a prior research for field operation of robots equipped with sensors in the future.
SEMANTIC SEGMENTATION USING A UNET ARCHITECTURE ON SENTINEL-2 DATA
Authors: Kotaridis I. , Lazaridou M.
Published: May 2022

Abstract. This paper presents the development of a methodological framework, based on deep learning, for the efficient mapping of main land cover classes (built-up, vegetation, barren land, water body) on different urban and suburban landscapes. In particular, the proposed framework integrates the superpixel segmentation (an essential procedure) with deep learning. A combination of spectral bands and indices is introduced to produce optimal results, ensuring adequate discrimination between built-up and barren land classes. A UNET architecture is implemented, which can learn the characteristics of main land cover classes from the input data that can be deployed from a Colab notebook without excessive computational needs. The resulted classifications depict promising accuracy values (above 90%).
DEEP LEARNING APPROACH FOR FLOOD DETECTION USING SAR IMAGE: A CASE STUDY IN XINXIANG
Authors: Zhao B. , Sui H. , Xu C. , Liu J.
Published: May 2022

Abstract. With the gradual warming of the global climate, frequent floods have caused huge losses to human life and property. Flood mapping by SAR image has been an important topic, and it is increasingly important to use deep learning method to extract flood information. In order to achieve automatic flood extent extraction, this paper proposes an attention mechanism-based water body extraction network with GF3 images, and successfully use it for flood detection in Xinxiang, Henan, China. In this paper, the proposed network incorporates the channel attention mechanism and position attention mechanism based on U-Net, to improve the efficiency and accuracy of water extraction, ignore the unimportant information by learning the weight and make the model focus toward the important information related to the water body. The OA of our method can reach 0.959 and Recall can reach 0.942 by verifying four sets of test data. Experiments show fast flood mapping in Xinxiang can be achieved by our method.
AN ADAPTIVE SUPERPIXELS FOR VEGETATION DETECTION ON HIGH RESOLUTION IMAGES BASED ON MLP
Authors: Tang X. , Huang X. , Xiong Z. , Wang X. , Zhan Z.
Published: May 2022

Abstract. Vegetation detection aims to find the area which should be attributed with the labels of vegetation on the captured images, such as forest, grass land etc., and nowadays it is a key research topic in the field of remote sensing information processing and application. Over the last few years, the deep learning method based on convolutional neural network (CNN) has become the mainstream method for vegetation detection. However, due to the peculiarities of the underlying encoding and decoding structures, it is common for some CNN methods to loss some boundary details of vegetation when employing high-resolution images with rich details and clear boundaries. In order to improve the boundary localization capability of vegetation, this paper proposes a hybrid solution, i.e., an MLP (MultiLayer Perceptron)-based high-resolution image adaptive superpixels vegetation detection method. Compared with the traditional watershed transform algorithm, this method adopts the two-step boundary marching criterion to generate superpixels with more adherent boundary and compact regularity which contains adaptive neighborhood information by design. Based on the generated superpixels with boundary detail information, this paper applies MLP for binary predictions, i.e., vegetation or non-vegetation. The experimental results show that our method has more precise vegetation boundary localization and higher accuracy compared with several state-of-the-art methods on the UAV image data set and ISPRS data set.
PHOTON-COUNTING LASER ALTIMETER DATA FILTERING BASED ON HIERARCHICAL ADAPTIVE FILTER FOR FOREST SCENARIO
Authors: Wang X. , Liang X.
Published: May 2022

Abstract. Photon-counting laser altimeter data has widely used in forest surveying and mapping with the successfully launch of ICESat-2. However, it is a challenge work that extract signal information from a large amount of noise photons, especially in complex scenario like forest. In this paper, we proposed the hierarchical adaptive filter method to extract signal photons and used this method to filtering ATL03 data in forest scenario. The hierarchical adaptive filter method follows a coarse-to-fine strategy to identify noise photons step by step. The most of noise are removed by filter based on feature of local distance, firstly. Then, the modified adaptive direction filter method is used to remove the noise photons near ground surface and vegetation. Finally, the filter based on continuity of topographic is used to remove the residual noise in the atmosphere. The airborne lidar data in experiment region was used to validate the effectiveness of method. The filter result overall recall is 97.51%, the overall precision is 98.58%, and the F-score is 98.04%. The result show that the hierarchical adaptive method we proposed can extract signal photons in background information effectively and preserve vegetation photons well.
Spatio-temporal analysis of slope-type debris flow activity in Horlachtal, Austria based on orthophotos and LiDAR data since 1947
Authors: Rom Jakob , Haas Florian , Heckmann Tobias , Altmann Moritz , Fleischer Fabian , Ressl Camillo , Betz-Nutz Sarah , Becht Michael
Published: May 2022

Abstract. In order to get a better understanding about the future development of alpine slope-type debris flows in the frame of climate change, complete and gapless records for the last century for this type of geomorphologic process are necessary. However, up to now such records are scarce. Here, the slope-type debris flow activity in the Horlachtal, Austria since 1947 is investigated with the help of historic and recent area-wide orthophotos. Using geomorphological mapping, both spatial and temporal variabilities in debris flow dynamics can be shown. The results indicate short-term variations rather than consistent increasing or decreasing trends of slope-type debris flow activity in Horlachtal. Specifically, three active periods between 1954 and 1973, 1990 and 2009 as well as between 2015 and 2018 can be registered. Analyses of the deposited debris flow volumes show that for parts of the study area the largest volumes appeared in the early 1990s which might have even influenced the dynamics in the following years. Studies on the spatial variabilities revealed differences of slope-type debris flow activity within the study area and point to local thunderstorms as triggers for debris flows. However, long-term precipitation data of high temporal resolution do not reveal increasing or decreasing tendencies in the number of such events.
Geomorphology and shallow sub-sea-floor structures underneath the Ekström Ice Shelf, Antarctica
Authors: Oetting Astrid , Smith Emma C. , Arndt Jan Erik , Dorschel Boris , Drews Reinhard , Ehlers Todd A. , Gaedicke Christoph , Hofstede Coen , Klages Johann P. , Kuhn Gerhard , Lambrecht Astrid , Läufer Andreas , Mayer Christoph , Tiedemann Ralf , Wilhelms Frank , Eisen Olaf
Published: May 2022

Abstract. The Ekström Ice Shelf is one of numerous small ice shelves that fringe the coastline of western Dronning Maud Land, East Antarctica. Reconstructions of past ice-sheet extent in this area are poorly constrained, due to a lack of geomorphological evidence. Here, we present a compilation of geophysical surveys in front of and beneath the Ekström Ice Shelf, to identify and interpret evidence of past ice-sheet flow, extent and retreat. The sea floor beneath the Ekström Ice Shelf is dominated by an incised trough, which extends from the modern-day grounding line onto the continental shelf. Our surveys show that mega-scale glacial lineations cover most of the mouth of this trough, terminating 11 km away from the continental shelf break, indicating the most recent minimal extent of grounded ice in this region. Beneath the front ∼30 km of the ice shelf measured from the ice shelf edge towards the inland direction, the sea floor is characterised by an acoustically transparent sedimentary unit, up to 45 m thick. This is likely composed of subglacial till, further corroborating the presence of past grounded ice cover. Further inland, the sea floor becomes rougher, interpreted as a transition from subglacial tills to a crystalline bedrock, corresponding to the outcrop of the volcanic Explora Wedge at the sea floor. Ice retreat in this region appears to have happened rapidly in the centre of the incised trough, evidenced by a lack of overprinting of the lineations at the trough mouth. At the margins of the trough uniformly spaced recessional moraines suggest ice retreated more gradually. We estimate the palaeo-ice thickness at the calving front around the Last Glacial Maximum to have been at least 305 to 320 m, based on the depth of iceberg ploughmarks within the trough and sea level reconstructions. Given the similarity of the numerous small ice shelves along the Dronning Maud Land coast, these findings are likely representative for other ice shelves in this region and provide essential boundary conditions for palaeo ice-sheet models in this severely understudied region.
Análise da alteração do ângulo nasolabial após indicação de extrações dentárias dos primeiros pré-molares: revisão de literatura
Authors: Silva Douglas Francisco da , Vale Michele Cristina Silva do , Sant’Ana Neto Antonio Lucio
Published: May 2022

A preocupação com a estética facial vem crescendo atualmente, baseada em um perfil de beleza social, e culturalmente estabelecido. A estética, e harmonização do sorriso é um dos objetos de estudo da ortodontia enquanto especialidade, que visa, garantir um pós-tratamento estético agradável e clinicamente positivo. Acreditava-se que apenas uma boa relação interdental promovia um perfil estético facial agradável, o que não se sustenta atualmente diante das atuais evidências apresentadas na literatura. Método: Este estudo trata-se de uma revisão da literatura, que utilizou como fonte de pesquisa as bases de dados, BVS, Pubmed e Scielo. Com os descritores: perfil facial, ortodontia, exodontia de pré-molares, e retração. Conclui-se que o tratamento ortodôntico pode alterar significativamente o ângulo nasolabial, assim como os tecidos moles da face durante o tratamento ortodôntico, não sendo possível ter uma previsibilidade de sua posição final, outro fator que se mostrou significativo foi a idade que demonstrou melhores resultados frente ao tratamento ortodôntico seguido de retração dental.
Dineral o ponderal de la emisión sobre la traslación del apóstol Santiago, de Fernando II de León. Propuestas de identificación e interpr-tación iconográfica
Authors: Mozo Monroy Manuel
Published: May 2022

Muchas son las opiniones vertidas sobre una meaja labrada en Santiago de Compostela por Fernando II, rey de Leon. Desde un principio se la denominó como como de la “Traslatio Sancti Iacobi” o “Traslación del Apóstol Santiago”, conservándose dicho ejemplar único en el Museo de las Peregrinaciones de Santiago de Compostela. Ahora tenemos la oportunidad de mostrar el primer y también único dineral o ponderal conocido de la misma, cuyo grado de rareza excede incluso al de la mencionada e impresionante meaja.

A través del presente estudio intentaremos ofrecer una primera interpretación didáctica, lógica y coherente de los símbolos labrados en ambas piezas, teniendo en cuenta aspectos numismáticos, históricos, documentales y artísticos, que siempre deber ser considerados en su conjunto, a la vez que procuraremos hacer un viaje virtual al pasado, con el fin de comprender como se percibía y entendían las imágenes recibidas a través de los ojos de una persona que vivió tales acontecimientos en la Edad Media.
El canje de la moneda mexicana por el peso provincial en Puerto Rico (1895-1896) y Bartolomé Maura y Montaner
Authors: Navarro Zayas Ángel Osvaldo
Published: May 2022

En esta investigación hemos reconstruido una parte de la historia monetaria de Puerto Rico a finales del siglo XIX. Nuestro objeto de estudio ha sido el problema de la moneda mexicana que circulaba en Puerto Rico y el peso provincial acuñado en la Real Fábrica de Moneda y Timbre para enfrentar dicho problema en la isla (1895-1896). El peso provincial de Puerto Rico, además del Bille-te de Canje que fue utilizado para llevar a cabo el canje de la moneda mexicana por el peso provincial, ambos fueron diseñados por el gran artista y principal grabador español de su época, Don Barto-lomé Maura y Montaner. Durante la investigación inicial pudimos localizar el diseño original del Billete de Canje, en donde queda evidenciado la cantidad de billetes de canje fabricados para facilitar dicho canje monetario (5,000,000). Nos atrevemos a afirmar que el Billete de Canje, ha sido el papel moneda con menor tiempo en circulación en algún territorio o país (circuló libremente 21 días).