bluuebunny/crossref_metadata_2025 huggingface dataset:
Water vapour and ozone in the upper troposphere – lower stratosphere: Global climatologies from three Canadian limb-viewing instruments
Authors: Jeffery Paul S. , Walker Kaley A. , Sioris Chris E. , Boone Chris D. , Degenstein Doug , Manney Gloria L. , McElroy C. Thomas , Millán Luis , Plummer David A. , Ryan Niall J. , Sheese Patrick E. , Zou Jiansheng
Published: May 2022

Abstract. This study presents upper troposphere – lower stratosphere (UTLS) water vapour and ozone climatologies generated from 14 years (June 2004 to May 2018) of measurements made by three Canadian limb-viewing satellite instruments: the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS), the Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (MAESTRO), and the Optical Spectrograph and InfraRed Imaging System (OSIRIS; ozone only). This selection of instruments was chosen to explore the capability of these Canadian instruments in representing the UTLS, and to enable analysis of the impact of different measurement sampling patterns. The water vapour and ozone climatologies have been constructed using tropopause-relative potential temperature and equivalent latitude coordinates in an effort to best represent the distribution of these two gases in the UTLS, which is characterized by a high degree of dynamic and geophysical variability. Zonal-mean multiyear-mean climatologies are provided with 5° equivalent latitude and 10K potential temperature spacing, and have been constructed on a monthly, seasonal (3-month), and yearly basis. These climatologies are examined in-depth for two 3-month periods, December–January–February and June–July–August, and are compared to reference climatologies constructed from the Canadian Middle Atmosphere Model 39-year specified dynamics (CMAM39-SD) run, subsampled to the times and locations of the satellite measurements, in order to evaluate the consistency of water vapour and ozone between the datasets. Specifically, this method of using a subsampled model addresses the impact of each instrument’s measuring pattern, and allows for the quantification of the influence of different measurement patterns on multiyear climatologies. In turn, this permits a consistent evaluation of the measurements of these two gas species. For the water vapour climatologies produced, a less than 8 % overall difference was found between the climatologies generated from the two versions of ACE-FTS, while comparisons with the MAESTRO climatologies were found to yield 15–41 % overall differences, depending on the version of ACE-FTS and the season. When considering the ozone climatologies, those constructed from the two ACE-FTS 20 versions agreed to within 2 % overall, and the OSIRIS ozone climatologies agreed with these to within 10 %. The MAESTRO ozone climatologies were found to differ from ACE-FTS and OSIRIS by about 30–35 % for the former, and 25 % for the latter, albeit with regions of better agreement within the UTLS. Overall these findings indicate that this set of Canadian limb sounders provide consistent water vapour and ozone distributions in the UTLS.
Reversible and irreversible gas–particle partitioning of dicarbonyl compounds observed in the real atmosphere
Authors: Hu Jingcheng , Chen Zhongming , Qin Xuan , Dong Ping
Published: May 2022

Abstract. Glyoxal and methylglyoxal are vital carbonyl compounds in the
atmosphere and play substantial roles in radical cycling and ozone
formation. The partitioning process of glyoxal and methylglyoxal between the
gas and particle phases via reversible and irreversible pathways could
efficiently contribute to secondary organic aerosol (SOA) formation.
However, the relative importance of two partitioning pathways still remains
elusive, especially in the real atmosphere. In this study, we launched five
field observations in different seasons and simultaneously measured glyoxal
and methylglyoxal in the gas and particle phases. The field-measured
gas–particle partitioning coefficients were 5–7 magnitudes higher than the
theoretical ones, indicating the significant roles of reversible and
irreversible pathways in the partitioning process. The particulate
concentration of dicarbonyls and product distribution via the two pathways
were further investigated using a box model coupled with the corresponding
kinetic mechanisms. We recommended the irreversible reactive uptake
coefficient γ for glyoxal and methylglyoxal in different seasons in
the real atmosphere, and the average value of 8.0×10-3 for
glyoxal and 2.0×10-3 for methylglyoxal best represented the
loss of gaseous dicarbonyls by irreversible gas–particle partitioning
processes. Compared to the reversible pathways, the irreversible pathways
played a dominant role, with a proportion of more than 90 % in the
gas–particle partitioning process in the real atmosphere, and the proportion
was significantly influenced by relative humidity and inorganic components
in aerosols. However, the reversible pathways were also substantial,
especially in winter, with a proportion of more than 10 %. The
partitioning processes of dicarbonyls in reversible and irreversible
pathways jointly contributed to more than 25 % of SOA formation in the
real atmosphere. To our knowledge, this study is the first to systemically
examine both reversible and irreversible pathways in the ambient atmosphere,
strives to narrow the gap between model simulations and field-measured
gas–particle partitioning coefficients, and reveals the importance of
gas–particle processes for dicarbonyls in SOA formation.
Cavity ring-down spectroscopy of water vapor in the near-UV region
Authors: Yang Qing-Ying , Conway Eamon K. , Liang Hui , Gordon Iouli E. , Tan Yan , Hu Shui-Ming
Published: May 2022

Abstract. Water vapor absorption in the near-ultraviolet region is essential to describe the energy budget of Earth, but little spectroscopic information is available since it is a challenging spectral region for both experimental and theoretical studies. A continuous-wave cavity ring-down spectroscopic experiment was built to record absorption lines of water vapor around 415 nm. With a minimum detectable absorption coefficient of 4 × 10−10 cm−1, 40 rovibrational transitions of H162O were observed in this work, and 27 of them were assigned to the (224), (205), (710), (304), (093), (125), and (531) vibrational bands. A comparison of line positions and intensities determined in this work to the most recent HITRAN database is presented. Water vapor absorption cross-sections near 415 nm were calculated based on our measurements, which vary between 1 × 10−26 and 5 × 10−26 cm2/molecule. These data will also significantly impact the spectroscopy detection of trace gas species in the near-UV region.
Turbulent kinetic energy dissipation rate and associated fluxes in the western tropical Atlantic estimated from ocean glider observations
Authors: Sheehan Peter M. F. , Damerell Gillian M. , Leadbitter Philip J. , Heywood Karen J. , Hall Rob A.
Published: May 2022

Abstract. Ocean gliders enable us to collect the high-resolution microstructure observations necessary to calculate the dissipation rate of turbulent kinetic energy, ε, on timescales of weeks to months: far longer than is normally possible using traditional ship-based platforms. Slocum gliders have previously been used to this end; here, we report the first detailed estimates of ε calculated using the Batchelor spectrum method on observations collected by a FP07 fast thermistor mounted on a Seaglider. We use these same fast thermistor observations to calculate ε following the Thorpe scale method and find very good agreement between the two methods. The Thorpe scale method yields larger values of ε, but the average difference, which is less than an order of magnitude, is smaller than reported elsewhere. The spatio-temporal distribution of ε is comparable for both methods. Maximum values of ε (10−7 W kg−1) are observed in the surface mixed layer; values of approximately 10−9 W kg−1 are observed between approximately 200 and 500 m depth. These two layers are separated by a 100 m thick layer of low ε (10−10 W kg−1), which is co-located with a high-salinity layer of Subtropical Underwater and a peak in the strength of stratification. We calculate the turbulent heat and salt fluxes associated with the observed turbulence. Between 200 and 500 m, ε induces downward fluxes of both properties that, if typical of the annual average, would have a very small influence on the heat and salt content of the overlying salinity-maximum layer. We compare these turbulent fluxes with estimates of double-diffusive fluxes, having objectively identified those regions of the water column where double diffusion is likely to occur. We find that the double-diffusive fluxes of both heat and salt are larger than the corresponding mechanical fluxes.
ANALYSIS OF SUN GLARE ON ROUNDABOUTS WITH AERIAL LASER SCANNING DATA
Authors: González-Collazo S. M. , Balado J. , González E. , Soilán M.
Published: May 2022

Abstract. Road geometry and sun glares play an important role concerning road safety. In this research, the direct sunlight in a roundabout sited in Ávila (Spain) is analysed using Aerial Laser Scanning (ALS) point clouds. First, the roundabout is divided in 8 sections, obtaining the driver bearing vectors of the roundabout. Entrances and exits driver bearing vectors of the roundabout are also considered. Then, sun rays are generated for a specific location of the roundabout and in a specific day and time. The incidence of the sun rays with the driver’s vision angle is analysed based on human vision model. Finally, intersections of sun rays with obstacles are calculated utilizing ALS point clouds. ALS data is processed (removing outliers, reducing point density, and computing a Delaunay Triangulation) in order to obtain accurate intersection results with obstacles and optimise the computational time. The method was tested in a roundabout, considering different driver bearings, the slope of the road and the elevation of the terrain. The results show that sun glares are detected at any day and time of the year, therefore areas with risk of direct sun glare within the roundabout are identified. The sun ray’s incidence in the vision angle of the driver is higher during winter solstice, and intersections with obstacles occur mainly during sunrise and sunset. In roundabout vector 7, during winter solstice there is direct sun glare for 7 hours 30 minutes, at the equinoxes for 6 hours 15 minutes and during summer solstice there is no direct sun glare.
UAV PHOTOGRAMMETRY FOR METRIC EVALUATION OF CONCRETE BRIDGE CRACKS
Authors: Ioli F. , Pinto A. , Pinto L.
Published: May 2022

Abstract. Monitoring cracks opening on concrete bridges is a key aspect for structural health assessment. Digital image processing, combined with Unmanned Aerial Vehicles (UAVs) and photogrammetry, allows for non-contact 3D reconstruction of cracks, reducing costs and potential unsafe factors involved in manual inspections. This paper presents a flexible procedure based on UAV photogrammetry for accurate evaluation of cracks geometry, that can be implemented for periodic structural monitoring. Stereo-pair of images, acquired with UAVs close to the cracked surface, are used to build a scaled photogrammetric model through Structure-from-Motion. Cracks are detected on images by image binarization and digital image processing techniques. Thereafter, one single image is used to reconstruct crack 3D geometry, by back-projecting crack image coordinates on a 3D model of the object. This can be built from the current stereo-pair of images, or based on an existing photogrammetric model, in the case of a periodic monitoring set-up. Crack width is accurately estimated in 3D world. The procedure is tested and evaluated in a case study, obtaining millimetric accurate results, which is in line with the average ground sample distance of the images employed. Results highlight the potentials of UAVs and photogrammetry not only for bridge inspections and damages localization, but also for accurately evaluating cracks geometry and helping structural engineers to assess structure health conditions.
PROBABILISTIC VEGETATION TRANSITIONS IN DUNES BY COMBINING SPECTRAL AND LIDAR DATA
Authors: Kathmann H. S. , van Natijne A. L. , Lindenbergh R. C.
Published: May 2022

Abstract. Monitoring the status of the vegetation is required for nature conservation. This monitoring task is time consuming as kilometers of area have to be investigated and classified. To make this task more manageable, remote sensing is used. The acquisition of airplane remote sensing data is dependent on weather conditions and permission to fly in the busy airspace above the Netherlands. These conditions make it difficult to get a new, dedicated acquisition every year. Therefore, alternatives for this dependency on dedicated airplane surveys are needed. One alternative is the use of optical satellite imagery, as this type of data has improved rapidly in the last decade both in terms of resolution and revisit time. For this study, 0.5 m resolution satellite imagery from the Superview satellite is combined with geometric height data from the Dutch national airborne LiDAR elevation data set AHN. Goal is to classify vegetation into three different classes: sand, grass and trees, apply this classification to multiple epochs, and analyze class transition patterns. Three different classification methods were compared: nearest centroid, random forest and neural network. We show that outcomes of all three methods can be interpreted as class probabilities, but also that these probabilities have different properties for each method. The classification is implemented for 11 different epochs on the Meijendel en Berkheide dunal area on the Dutch coast. We show that mixed probabilities (i.e. between two classes) agree well with class transition processes, and conclude that a shallow neural network combined with pure training samples applied on four different bands (RGB + relative DSM height) produces satisfactory results for the analysis of vegetation transitions with accuracies close to 100%.
EVALUATION OF AN UNMANNED AERIAL VEHICLE (UAV) FOR MEASURING AND MONITORING NATURAL DISASTER RISK AREAS
Authors: Reiss M. L. L. , Mendes T. S. G. , Pereira F. F. , de Andrade M. R. M. , Mendes R. M. , Simões S. J. C. , de Lara R. , de Souza S. F.
Published: May 2022

Abstract. This work presents an evaluation of a small UAV-RPAS-PPK to be used in the generation of digital surface models (DSM), without the need for control points, having as main application the monitoring of disaster risk areas (landslide and flooding). The areas to be measured are difficult to access, which prevents or makes access to the land difficult. In this evaluation, a study area of approximately 13 km2 was flown over, an average pixel of 11.6 cm and a total of 417 photos. The equipment used to acquire the images was a SenseFly eBee X, equipped with GNSS PPK for Direct Georeferencing (DG) and a camera model S.O.D.A. In all, 42 ground checkpoints were measured using a dual-frequency GNSS receiver. For both the measurement of the checkpoints and for the Direct Georeferencing (DG) base of the Unmanned Aerial Vehicles (UAV), a relative processing was performed, using the Brazilian Network for Continuous Monitoring (RBMC) as a reference. With this evaluation, it was possible to achieve a result (RMSE) for phototriangulation better than 1.2 pixels for horizontal and 1.5 pixels for vertical, without the need to measure any control points on the ground.
A CNN ARCHITECTURE FOR DISCONTINUITY DETERMINATION OF ROCK MASSES WITH CLOSE RANGE IMAGES
Authors: Yalcin I. , Can R. , Kocaman S. , Gokceoglu C.
Published: May 2022

Abstract. Determination of discontinuities in rock mass requires scan-line surveys performed in in-situ that can reach up to dangerous and challenging dimensions. With the development of novel technological equipments and algorithms, the studies related to rock mass discontinuity determination remain up-to-date. Depending on the development of the Structure from Motion (SfM) method in the field of close-range photogrammetry, low-cost cameras can be used to produce 3D models of rock masses. However, the determination of rock mass discontinuity parameters must still be carried out manually on these models. Within the scope of this study, a Convolutional Neural Network (CNN) architecture is proposed to identify the discontinuities automatically as the first step for fully automated processing. The Kızılcahamam/Güvem Basalt Columns Geosite near Ankara, Turkey was determined as the study area. The orthophoto of this study area was produced using close-range photogrammetric methods and the training data was produced by manual mensuration. The dataset consists of labeled binary masks and images containing corresponding Red-Green-Blue (RGB) bands. Furthermore, the amount of data was increased by applying augmentation methods to the dataset. The U-Net architecture was used to detect rock discontinuities based on the produced orthophoto. The preliminary results presented here reveal that the discontinuity determination capability of the proposed method is high based on the visual assessments, while problems exist with image quality and discontinuity identification. In addition, considering the small size of the training dataset, the accuracy of the model would increase when a larger dataset could be employed.
CNN-BASED TEMPLATE MATCHING FOR DETECTING FEATURES FROM HISTORICAL MAPS
Authors: Xia X. , Heitzler M. , Hurni L.
Published: May 2022

Abstract. Efficiently detecting features from historical maps is a challenging task due to its inconsistent manual scribbling styles and the lack of large scale labelled training data. To tackle this issue, this paper proposes an automatic feature detection pipeline utilizing CNN-based template matching (TM), which can lead to efficient feature extraction with minimal input, i.e. one single template. Three CNN-based TM models equipped with different feature extractors are investigated and compared in this research, namely pre-trained VGG19 CNNs, autoencoders, and the combination of both. Experiments conducted on six tiles of the Swiss Old National Map demonstrate that the combined architecture achieves the best result in wetlands detection, resulting in a mean intersection over union (IoU) of 69% and an average F1 measure of 82%.
APPLICATION OF STEREO CAMERAS WITH WIDE-ANGLE LENSES FOR THE INDOOR MAPPING
Authors: Wierzbicki D. , Stogowski P.
Published: May 2022

Abstract. Recently, there has been an increase in interest in the use of wide-angle cameras in multi-image matching for the indoor 3D mapping and indoor localization. The demand for rapid 3D models of spaces in unknown environments is increasingly observed. That is particularly important when modelling unknown objects to conduct reconnaissance or building intervention after a disaster. In this case, developing a 3D model using a robot equipped with a system of synchronized stereo cameras with a short length longitudinal base is extremely desirable. In these studies, we present the approach to indoor location based on a 3D model developed from a dense point cloud with multi-image matching technique. As part of the research, an imaging system was developed, and an algorithm that converts images of selected objects to 3D model was implemented. The research presents the method of determining the object position based on the calculation of reference points’ disparity based on the Sum of Absolute Differences (SAD). Next, a dense point cloud was generated based on the method of mutual image matching using Structure from Motion (SfM) algorithms. The resulting dense cloud of points had a resolution of 0.05 m. Based on the developed algorithm, a method for generating a quick model of the environment based on multi-image matching and disparity maps was presented. The obtained test results confirmed the possibilities of using the developed methodology for the needs of rapid reconnaissance of the environment to determine the distance, location and size of objects of interest. The mapping accuracy is at a decimeter level, and the possibility of geolocation of objects can be performed with an accuracy of ± 0.15 m. Based on the obtained test results, the potential of using miniature, portable mobile image-based mapping systems has been demonstrated to identify and model inaccessible rooms. Further work will be focused on the improvement of the geometric image quality and to increase the accuracy of the calibration.
3D CHANGE DETECTION OF POINT CLOUDS BASED ON DENSITY ADAPTIVE LOCAL EUCLIDEAN DISTANCE
Authors: Chai J. X. , Zhang Y. S. , Yang Z. , Wu J.
Published: May 2022

Abstract. With the development of sensors and multi-view stereo matching technology, image-based dense matching point cloud data shares higher geometric accuracy and richer spectral information, and such data is therefore widely used in change detection-related research. Due to the inconsistent position and attitude of the image acquisition for generating two phases of point clouds, as well as the seasonal variation of vegetation, the 3D change detection is often subject to false detection. To improve the accuracy of 3D change detection of point clouds in large fields, a method of 3D change detection of point clouds based on density adaptive local Euclidean distance is proposed. The method consists of three steps: (1) Calculating the local Euclidean distances from each point in the second phase of point clouds to the k nearest neighboring points of the first phase of point clouds; (2) Improving the local geometric Euclidean distance based on the local density and performing 3D change detection according to a given threshold; (3) Clustering the change detection results using Euclidean clustering, and then eliminating the false detection area according to the given threshold. The experiments show that the changed region can be better extracted by the proposed method.
REINFORCEMENT LEARNING FOR AUTONOMOUS 3D DATA RETRIEVAL USING A MOBILE ROBOT
Authors: Kniaz V. V. , Mizginov V. , Bordodymov A. , Moshkantsev P. , Novikov D.
Published: May 2022

Abstract. 3D data retrieval is required in various fields such as an industrial monitoring, agriculture, and robotics. Recent advances in photogrammetry and computer vision allowed to perform 3D reconstruction using a set of images captured with uncalibrated camera. Such technique is commonly known as Structure-from-Motion. In this paper, we propose a reinforcement learning framework RL3D for online strong camera configuration planning onboard of a mobile robot. The mobile robot consists of a skid-steered wheeled platform, a single-board computer and an industrial camera. Our aim is developing a model that plans a set of robot location that provide a strong camera configuration. We developed an environment simulator to train our RL3D framework. The simulator was implemented using a 3D model of the indoor scene and includes a model of robot’s dynamics. We trained our framework using the simulator and evaluated it using a virtual and real environments. The results of the evaluation are encouraging and demonstrate that the controller model successfully learns simple camera configurations such as a circle around an object.
A SELF-SUPERVISED KEYPOINT DETECTION NETWORK FOR MULTIMODAL REMOTE SENSING IMAGES
Authors: Li L. , Han L. , Cao H. , Liu M.
Published: May 2022

Abstract. Currently, multimodal remote sensing images have complex geometric and radiometric distortions, which are beyond the reach of classical hand-crafted feature-based matching. Although keypoint matching methods have been developed in recent decades, most manual and deep learning-based techniques cannot effectively extract highly repeatable keypoints. To address that, we design a Siamese network with self-supervised training to generate similar keypoint feature maps between multimodal images, and detect highly repeatable keypoints by computing local spatial- and channel-domain peaks of the feature maps. We exploit the confidence level of keypoints to enable the detection network to evaluate potential keypoints with end-to-end trainability. Unlike most trainable detectors, it does not require the generation of pseudo-ground truth points. In the experiments, the proposed method is evaluated using various SAR and optical images covering different scenes. The results prove its superior keypoint detection performance compared with current state-of-art matching methods based on keypoints.
UTILISING SIMULATED TREE DATA TO TRAIN SUPERVISED CLASSIFIERS
Authors: Rönnholm P. , Wittke S. , Ingman M. , Putkiranta P. , Kauhanen H. , Kaartinen H. , Vaaja M. T.
Published: May 2022

Abstract. The aim of our research was to examine whether simulated forest data can be utilized for training supervised classifiers. We included two classifiers namely the random forest classifier and the novel convolutional neural network classifier that utilizes feature images. We simulated tree parameters and created a feature vector for each tree. The original feature vector was utilised with random forest classifier. However, these feature vectors were also converted into feature images suitable for input into a YOLO (You Only Look Once) convolutional neural network classifier. The selected features were red colour, green colour, near-infrared colour, tree height divided by canopy diameter, and NDVI. The random forest classifier and convolutional neural network classifier performed similarly both with simulated data and field-measured reference data. As a result, both methods were able to identify correctly 97.5 % of the field-measured reference trees. Simulated data allows much larger training data than what could be feasible from field measurements.
MULTI-CAMERA GEOMETRIC CALIBRATION: PRE-CALIBRATION AND ON-THE-JOB CALIBRATION OF THE MAIA MULTISPECTRAL SYSTEM
Authors: Garieri P. , Diotri F. , Forlani G. , Morra di Cella U. , Roncella R.
Published: May 2022

Abstract. Though with a less dramatic growth compared to photogrammetry, remote sensing from multispectral imagery taken by UAV (Unmanned Aerial Vehicles) platforms is applied to vegetation health monitoring, crop management, water quality assessments, geological inspections and much more, with a sizeable number of multispectral cameras now available on the market. As for satellite images, a key point in remote sensing is calibration, both geometric and radiometric, and the modelling of disturbances, to get well co-registered reflectance data. Leaving aside radiometric calibration, this paper focuses on how to best georeference the different bands one with respect to the other. This is normally achieved by the so-called band-to-band registration (BBR). Here, a straightforward approach is proposed, that exploits the multi-camera geometry and, unlike BBR, all the information contents of the bands, as inter-band matches are searched (and possibly found) for every pair of bands and not only between a reference and a slave band. Tests on images taken with the 9-band MAIA S2 camera are presented, discussing the pro and cons of pre-calibration and on-the-job calibration of the camera parameters of each sensor. The results found show that the proposed method is at least as good as the BBR ones.
VERY HIGH-RESOLUTION 3D SURVEYING AND MODELLING EXPERIENCES IN CIVIL ENGINEERING APPLICATIONS
Authors: Girelli V. A. , Tini M. A. , Bitelli G.
Published: May 2022

Abstract. In this paper some experiences in 3D modelling of objects with very high-resolution are described, carried out by the DICAM Geomatics group of the University of Bologna in multi-disciplinary contexts within the field of the Civil Engineering. In all the addressed case studies the main aim is the generation of a 3D model of the surface at a sub-millimetric scale, allowing a very accurate characterization of the surface geometry, useful for different purposes. 3D scanning and Structure from Motion photogrammetry have been used to generate the 3D models. In the paper the encountered problems and the adopted solutions in data surveying and processing are underlined, also discussing the added value of very high-resolution 3D modelling in multi-disciplinary activities.
HIGH-RESOLUTION DIGITAL SURVEY OF FLOORS: A NEW PROTOTYPE FOR EFFICIENT PHOTOGRAMMETRIC ACQUISITION
Authors: Adami A. , Fregonese L. , Helder J. , Rosignoli O. , Taffurelli L. , Treccani D.
Published: May 2022

Abstract. High-resolution surveying of historical floors is a very common practice in both research and everyday life. The type of floors typically concerned are made of mosaic, marble and stone. Because of their intrinsic characteristics, their survey typically requires very high-resolution results, to ensure excellent support for restoration, as well as in-depth knowledge of the artifact. In these cases, the focus must be kept on both geometric and radiometric content, to enable accurate metric representation and a rendering of colour and surfaces as close as possible to reality. In this research we propose a prototype of a photogrammetric acquisition system (under development) which tries to optimise the floor survey in terms of both geometric and colour documentation. In particular, the prototype makes use of the cross-polarisation technique with the aim of eliminating reflections from the images. The principle behind the prototype is the creation of a movable laboratory, a segregated space that allows excellent photographic acquisition even in difficult environmental conditions, which cannot always be controlled optimally. First tests showed its suitability and usefulness to reach the goal of a high resolution survey of historic floors.
APPLYING HERITAGE BUILDING INFORMATION MODELLING (HBIM) TO LOST HERITAGE IN CONFLICT ZONES: AL-HADBA’ MINARET IN MOSUL, IRAQ
Authors: Al-Muqdadi F. , Ahmed A.
Published: May 2022

Abstract. Arguably, the weaponry development during the last century aided in destroying cultural heritage now like never before, this is illustrated in the recent destruction of irreplaceable heritage sites in Syria and Iraq due to conflict. Heritage is often destroyed to demoralise local societies, erase memory, cause extensive economic loss and assert supremacy. Therefore, post-conflict recovery needs to aid in re-establishing damaged heritage as a beacon for cultural and social recovery. Using Heritage Building Information Modelling (HBIM) could provide tools to digitally recover damaged structures. Yet, there is little research on the implementation of HBIM in conflict zones. Thus, this study seeks to apply HBIM workflows to damaged heritage by virtually reconstructing Al-Hadba’s minaret, one of Iraq’s iconic landmarks, destroyed in 2017 by the “Islamic state”. The photogrammetry process used freely available web-based images that were imported into three photogrammetry software: Agisoft Metashape©, Autodesk Recap©, and AliceVision Meshroom to compare crowdsource processing capabilities. The resulting point cloud was scaled using onsite measurements of the minaret’s remaining base and imported into Autodesk Revit© to produce a HBIM model. While the process produced comprehensive digital documentation of Al-Hadba’, it revealed many challenges confronting digital reconstruction in conflict zones such as lacking documentation, conflict of historical information, crowdsource data constraints, and their effects on the resulting point cloud’s quality. The study highlights potential tools to support post-conflict recovery and attempts to present an accessible approach that could be used as a baseline in conflict zones with limited resources to aid in the cultural recovery of valuable heritage sites that represent an irreplaceable part of their community’s collective memory.
ADDRESSING CLASS IMBALANCE IN MULTI-CLASS IMAGE CLASSIFICATION BY MEANS OF AUXILIARY FEATURE SPACE RESTRICTIONS
Authors: Dorozynski M. , Rottensteiner F.
Published: May 2022

Abstract. Learning from imbalanced class distributions generally leads to a classifier that is not able to distinguish classes with few training examples from the other classes. In the context of cultural heritage, addressing this problem becomes important when existing digital online collections consisting of images depicting artifacts and assigned semantic annotations shall be completed automatically; images with known annotations can be used to train a classifier that predicts missing information, where training data is often highly imbalanced. In the present paper, combining a classification loss with an auxiliary clustering loss is proposed to improve the classification performance particularly for underrepresented classes, where additionally different sampling strategies are applied. The proposed auxiliary loss aims to cluster feature vectors with respect to the semantic annotations as well as to visual properties of the images to be classified and thus, is supposed to help the classifier in distinguishing individual classes. We conduct an ablation study on a dataset consisting of images depicting silk fabrics coming along with annotations for different silk-related classification tasks. Experimental results show improvements of up to 10.5% in average F1-score and up to 20.8% in the F1-score averaged over the underrepresented classes in some classification tasks.
ACCURACY ANALYSIS OF ESTIMATED CAMERA ORIENTATION PARAMETERS OF HISTORICAL IMAGES – CASE STUDY OF GEORG-SCHUMANN-BAU IN DRESDEN (GERMANY)
Authors: Maiwald F. , Sardemann H.
Published: May 2022

Abstract. Digitalization in archives provides access to an ever-increasing amount of historical photographs. As most of these are not taken by photogrammetric experts it is of interest if these images can be integrated into photogrammetric workflows in order to use them for advanced visualizations. While the pose estimation of historical images can be solved using feature matches retrieved by neural networks it is still unclear how accurate the determined poses are. This contribution tries to provide a reference for unordered historical photo collections with additional contemporary images using the example of the Georg-Schumann-Bau in Dresden, Germany. Therefor, a calibrated camera is used to take images of the building with the idea to enhance the estimated camera parameters of historical images in a combined model. This reference can then be used for comparison with a model created using exclusively historical images. Only half of the estimated poses are below 10 meters compared to the reference which falls behind expectations. Nonetheless, it is possible to use the estimated poses for evaluating aggregations of historical images.
ANALYSIS OF DATA JOINING FROM DIFFERENT INSTRUMENTS FOR OBJECT MODELLING
Authors: Pavelka K. , Raeva P. , Pavelka jr. K. , Kýhos M. , Veselý Z.
Published: May 2022

Abstract. The aim of this contribution is an analysis of related technologies for data capturing for creating a digital twin of selected construction. As a case project a baroque church was selected. Four technologies were used for object modelling: TLS (terrestrial laser scanning), digital close-range photogrammetry SfM (structure from motion) as a terrestrial and aerial (drone) technology, and PLS (personal mobile laser scanner). It was possible to combine data from drone and terrestrial photogrammetry, the model was compared to TLS and proved to be sufficiently accurate. The data from the PLS were more complex (inside and outside) but did not contain higher parts of the building. However, it suitably complemented the entire interior. The complex model can be made as joined model from all devices and it is suitable for export to virtual reality (VR).
RESEARCH ON THE APPLICATION METHOD OF HISTORICAL BUILDING PROTECTION UNDER THE INTEGRATION OF MULTIPLE TECHNOLOGIES
Authors: Yang X. , Qiu B. , Tian Z.
Published: May 2022

Abstract. With the development of society, the protection of historical buildings has been paid more and more attention. In recent years, China has increased the surveying and mapping of historical buildings buildings. This paper takes the historical building protection project in Zhejiang Province, China as an example. This paper introduces the method of combining UAV aerial photography, 3D laser scanning, close close-range photogrammetry and traditional surveying and mapping techniques. Mapping and information collection of historical buildings. And on this basis, the content and methods of quality inspection of historical architectural achievements are introduced. Guarantee the reliability of historical building archives and data quality quality. This will play a supporting role in the future repair and maintenance of historical buildings buildings.
INTEGRATING GIS AND PHOTOGRAMMETRIC RECORDING FOR EXTENDED SEABED ARCHAEOLOGICAL RESEARCH, MARSA BAGOUSH, EGYPT
Authors: Salama M. , Khalil E.
Published: May 2022

Abstract. Photogrammetry and GIS technologies are developing rapidly and becoming more affordable. They have also emerged as significant archaeological tools as a result of an increasing level of automated workflow in data acquisition and processing. Moreover, these techniques offer considerable reduction in the cost of underwater archaeological research. That is in addition to significant enhancement in research plans, management, and results, especially in extended seabed archaeological projects. Therefore, these technologies are utilized as essential aspects of the underwater archaeological research project in the site of Marsa Bagoush, Egypt. The site is located 250 km west of Alexandria, and it takes the form of a bay, almost 650,000 m2. Marsa Bagoush (ancient Zygris) was mentioned by Claudius Ptolemaeus in the 2nd century AD as one of the main anchorages along the northwest coast of Egypt between Alexandria and Marsa Matrouh (ancient Paraetonium).This paper presents the results of the on-going digital documentation of the site which is a major part of an underwater archaeological investigation project. The paper will discuss the promising application of photogrammetry in underwater archaeology. It will also look at the integration of GIS Database techniques, which represent accurate methods of documenting topographical and archaeological elements and obtaining three-dimensional digital models of the site and its contents.
SATELLITE-BASED LAND/SEA CONTINUUM: AN APPLICATION TO MONITOR THE SAINT LOUIS COAST (SENEGAL, WEST AFRICA)
Authors: Taveneau A. , Almar R. , Bergsma E. W. J. , Sy B. , Ndour A.
Published: May 2022

Abstract. The historical city of Saint Louis in Senegal is situated on the Langue de Barbarie sand spit and is particularly prone to erosion: buildings have collapsed and population relocated due to shoreline retreat. At Saint Louis understanding the beach-morphodynamics is essential, and relies on the monitoring of nearshore topography and bathymetry. Remote sensing techniques relying on very high resolution (sub metric) satellites such as the Pleiades constellation and Planet now offer new perspectives in coastal monitoring and engineering. Digital Elevation Models (DEMs) of the emerged part of the beach, topography, and the submerged nearshore bathymetry can be obtained using respectively (tri-)stereogrammetry, and depth inversion through wave kinematics or/and colour based methods. These methods offered promising results (Almeida et al., 2019, Taveneau et al., 2021, Almar et al., 2019a) in previous studies when applied to Pleiades satellite. This work showcases a DEM derived from a 3-images (dT ≃ 9.5s) Pleiades acquisition in March 2020 and covering the key topography/bathymetry morphological continuum. Root mean squared errors of about 5 m for bathymetry and 0.9 m for topography are obtained. To limit erosion at Saint Louis urban beaches, the construction of a protection structure has begun in late 2020. Here, satellite DEM is used to monitor the efficiency of the structure and its downstream impact along the sand spit.
SEAWEED PRESENCE DETECTION USING MACHINE LEARNING AND REMOTE SENSING
Authors: Tonion F. , Pirotti F.
Published: May 2022

Abstract. The human pressure over coastal areas is becoming increasingly relevant, due to the combinations of resource depletion, climate change effects and ocean eutrophication. Coastal ecosystems are so exposed to a huge number of stress factors that endanger their ecosystem services, like carbon uptake and biodiversity maintenance, that can be crucial in facing the effects of climate changes. With a particular focus on seaweeds, these ecosystems are becoming rapidly relevant both for carbon sinks and as a source of high value products, for example thanks to cosmetic and food industries that produce high added values products.In this contest the capability of conducting efficient monitoring is crucial to monitor environmental dynamics and resources trends. Traditionally seaweed monitoring was carried out with on field surveys that could be based on botanic analysis combined with genetic study, depending on the aims. Recently Remote Sensing techniques, combined with Artificial Intelligence ones, gave a new perspective to seaweed monitoring, introducing tools that are always more efficient.In this contest the present work aims to test the potentiality of remote sensing and artificial intelligence techniques for seaweed monitoring along the Irish west coast, building the basis for a fully automated tool for monitoring. The results showed that, with a supervised classification approach, it is possible to train Random Forest (RF) to perform very precise classification over the entire West Coast of Ireland. In particular, with all the RF configurations tested the Overall Accuracy (OA) was greater than 98.61, with the best performance obtained with the configuration Ntree = 600 and mtry = 2 that produced an OA = 98.87.
APPLICATIONS OF PHOTOGRAMMETRY AND REMOTE SENSING TECHNOLOGIES IN CHANG’E-4 MISSION
Authors: Di K. , Liu Z. , Wan W. , Gou S. , Yu T. , Wang J. , Li L. , Liu C. , Liu B. , Peng M. , Wang Y. , Yue Z. , He X. , Liu S.
Published: May 2022

Abstract. This paper presents a brief overview of photogrammetry and remote sensing technologies and applications in the Chang’e-4 mission. The developed technologies, such as image matching, block adjustment, 3D mapping, spectral analysis and mineral retrieval, have been applied in topographic mapping, rover localization, and rover path planning, and directly supported daily surface operations of the Yutu-2 rover. The technologies and products have also been supportive in various scientific investigations, such as topographic evolution of Von Kármán crater, multi-scale crater morphologies, mineral composition of lunar deep materials, regolith maturity and space weathering effects, etc.
RAMAN SPECTRAL ANALYSIS FROM SHERLOC ONBOARD MARS 2020
Authors: Tripathi P. , Garg R. D.
Published: May 2022

Abstract. The Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals (SHERLOC) is one of the instruments mounted on the Perseverance rover’s robotic arm. SHERLOC focuses on searching for mineral species and organics that may contain spectral signatures of hydrous environments and past microbial life. It measures the Raman scattering in a narrow wavelength range (246–357 nm) with a significantly stronger fluorescence around 274–355 nm. Here the 40 measurements of Raman spectra from three detectors of SHERLOC, captured on Sol 4, were analyzed along with Matscam-Z images. Principal component analysis was applied to check the mineralogical diversity. The results include fluorescence signatures with maxima at 276 nm and Raman peaks similar to amorphous silicates.
FIRE SEVERITY ASSESSMENT OF AN ALPINE FOREST FIRE WITH SENTINEL-2 IMAGERY
Authors: Leblon B. , Ogunjobi Oluwamuyiwa F. , Lingua E. , LaRocque A.
Published: May 2022

Abstract. Fire is a common phenomenon in many forests and is considered an important ecological tool. Fire severity mapping presents an effective way to assess post-fire management intervention and is helpful in environmental and climate change research. The objective of this study was to determine the severity of a forest fire event that occurred from 24th to 27th October 2019 at Taibon Agordino using Sentinel-2A satellite images and creating a severity map suitable as a decision-making tool for post-fire management intervention. The Sentinel-2A satellite data was classified into the following five classes: Unburned, Low Severity, Moderate Severity, High Severity, and Shadow with the non-parametric Random Forest (RF) classifier, and the resulting classified image was validated using validation sites. The RF classifier was applied first to the ten original band reflectance of Sentinel-2. In a second step, additional variables were added to the classification, namely the digital elevation model (DEM), the slope, and five vegetation indices (i.e., Differenced Normalized Burn Ratio (dNBR), Relative Differenced Normalized Burn Ratio (RdNBR), Differenced Bare Soil Index (dBSI), Global Environmental Monitoring Index (GEMI) and Burn Area Index (BAI)) The inclusion of vegetation indices and DEM-related variables increased the classification accuracy from 99.26% to 99.61% and the overall accuracy from 70.51% to 83.33%. In the classification with the ten original band reflectance, the variable of importance plot ranked the Red-Edge-3, Red, and SWIR 1 band reflectance as the top three most important input features, while for the classification with 17 variables, RdNBR, DEM and dNBR were the top three most important input features.
IMPROVING RELIABILITY IN GLOBAL FLOOD MAPPING BY GENERATING A SEASONAL REFERENCE WATER MASK USING SENTINEL-1/2 TIME-SERIES DATA
Authors: Martinis S. , Groth S. , Wieland M. , Rättich M. , Knopp L.
Published: May 2022

Abstract. Variable intra-annual climatic and hydrologic conditions result in many regions of the world in a strong seasonality of the water extent throughout the year. This behaviour, however, is usually not reflected in satellite-based flood emergency mapping. This may lead to non-reliable representations of the flood extent and to misleading information within disaster management activities. In order to be able to separate flooding from normally present seasonal water coverage, up-to-date, high-resolution information on the seasonal water cover is crucial. In this work, we present an automatic methodology to generate a global and consistent permanent and seasonal reference water product based on high resolution Earth Observation data, specifically designed for the use within flood rapid mapping activities. The water masks are primarily based on the time-series analysis of optical Sentinel-2 imagery, which are complemented by Sentinel-1 Synthetic Aperture Radar-based information in data scarce regions. The methodology has been developed based on data of five globally distributed study areas (Australia, Germany, India, Mozambique, and Sudan). Within this work results for Australia and India are demonstrated and are systematically compared with external reference water products. Results show, that by using the proposed product it is possible to give a more reliable picture on flood-affected areas in the frame of disaster response.
MONITORING OF ARTIFICIAL RESERVOIR INDUCED LANDSLIDES FROM SPACE: A CASE STUDY OF FROM INDIA
Authors: Mishra V. , Singh C. , Jain K.
Published: May 2022

Abstract. Monitoring Reservoir-induced landslides is critical for determining and mitigating the geo-environmental risks associated with the artificial reservoir. This paper employs Persistent Scatterer Interferometry (PSI) technique for the mapping and monitoring of Reservoir Induced Landslides in the rim area of Baglihar Dam Reservoir on Chenab river (Jammu and Kashmir, India) along National Highway NH-244. It has been evident that the slope movements are necessarily impacted by the reservoir drawdown effect (RDE) as well as the rainfall. We have utilised 46 Sentinel-1A C-band radar satellite imagery of the study area. The monitoring results have yielded the Line of Sight slope movement along the reservoir rim varies from −97 mm/year to +80 mm/year. The temporal correlation between the displacements and the local precipitation (derived from TRMM using Google Earth Engine) was qualitatively analysed. Two unstable slopes have been identified and monitored.