bluuebunny/crossref_metadata_2025 huggingface dataset:
Children between 9 and 13 years old as internet users
Authors: Oziębło Patrycja
Published: May 2022

The paper presents the new new media as a contemporary and dominant space in children's lives. Most children begin to use the Internet when they are a few years old, for example, while watching cartoons. The online world can be both an opportunity and a threat to young users. The aim of the research was to determine whether children's growing interest in the Internet has a limiting impact on traditional ways of communication with peers and whether the increasing use of social networking sites by children is determined by the fear of exclusion from their circle of peers. The study was conducted with the use of an online survey distributed among children from the third, fifth, and seventh years of two Polish primary schools. The results include several conclusions regarding children's contact with the new media and their peers. The study showed that the majority of primary school pupils are regular users of the new new media, and their interest in new technologies increases with age.
Badania wpływu pandemii COVID-19 na komunikację społeczną i media w wybranych polskich i zagranicznych czasopismach naukowych
Authors: Kuliński Hubert
Published: May 2022

Niniejszy artykuł stanowi metaanalizę badań nad wpływem pandemii COVID-19 na komunikację społeczną i media. Przeprowadzono analizę zawartości dziesięciu polskich i dziesięciu zagranicznych medioznawczych czasopism naukowych. Z próby N = 1214 abstraktów artykułów wyselekcjonowano n = 56 takich, które spełniały kryterium tematyki badawczej, a następnie przeprowadzono ich analizę w zakresie: obszarów badawczych, paradygmatów metodologicznych, stosowanych metod badawczych, teorii służących jako ramy badawcze i przedmiotu badań. Wykazano, że: 1) głównymi polami, w jakich prowadzono badania wpływu COVID-19 na komunikację i media, były zawartość, wpływ i audytoria mediów; 2) metodologia jakościowa była najczęstszym paradygmatem badawczym; 3) studium przypadku było najczęściej wykorzystywaną metodą badawczą. Artykuł może zainteresować osoby prowadzące metaanalizy w zakresie badań mediów i komunikacji.
Funkcjonowanie instytucji festiwalu filmowego w trakcie pandemii COVID-19 na przykładzie festiwalu Nowe Horyzonty
Authors: Najbor Joanna
Published: May 2022

Celem artykułu jest przedstawienie sposobów komunikacji festiwalu filmowego na początku pandemii COVID-19. Aby to zrobić, przeprowadzono analizę mediów społecznościowych festiwalu Nowe Horyzonty, który odbył się w listopadzie 2020 roku. W związku z wybuchem pandemii zarówno największe wydarzenia filmowe, jak i te lokalne musiały przenieść się do sieci. Nowa rzeczywistość, choć stanowiąca wyzwanie techniczne, organizacyjne i społeczne, okazała się też szansą na poszerzenie skali działań popularyzujących kulturę. W tekście przybliżono specyfikę tradycyjnego festiwalu filmowego, skupiając się na aspektach komunikacyjnych, perspektywie widzów oraz na strategiach promocyjnych wielodniowych wydarzeń filmowych. Na podstawie analizy materiału empirycznego zawierającego wypowiedzi i reakcje administratorów oraz użytkowników na profilach społecznościowych festiwalu wykazano, jakie elementy biorą udział w skutecznym kreowaniu społeczności (nie tylko) wirtualnej.
Transport Patterns of Global Aviation NOx and their Short-term O3 Radiative Forcing – A Machine Learning Approach
Authors: Maruhashi Jin , Grewe Volker , Frömming Christine , Jöckel Patrick , Dedoussi Irene C.
Published: May 2022

Abstract. Aviation produces a net climate warming contribution that comprises multiple forcing terms of mixed sign. Aircraft NOx emissions are associated with both warming and cooling terms, with the short-term increase in O3 induced by NOx emissions being the dominant warming effect. The uncertainty associated with the magnitude of this climate forcer is amongst the highest out of all contributors from aviation and is owed to the non-linearity of the NOx–O3 chemistry and the large dependency of the response on space and time, i.e., on the meteorological condition and background atmospheric composition. This study addresses how transport patterns of emitted NOx and their climate effects vary with respect to regions (North America, South America, Africa, Eurasia and Australasia) and seasons (January–March and July–September in 2014) by employing global-scale simulations. We quantify the climate effects from NOx emissions released at 250 hPa (~ 10 400 m) in terms of radiative forcing resulting from their induced short-term contributions to O3. The emitted NOx is transported with Lagrangian air parcels within the ECHAM5/MESSy Atmospheric Chemistry (EMAC) model. To identify the main global transport patterns and associated climate impacts of the 14 000 simulated air parcel trajectories, the unsupervised QuickBundles clustering approach is adapted and applied. Results reveal a strong seasonal dependence of the contribution of NOx emissions to O3. For most regions, a negative correlation is found between an air parcel’s downward transport and its mean contribution to O3. NOx emitted in the Northern regions (North America and Eurasia) experiences the longest residence times in upper midlatitudes (40–45 % of their lifetime), while those beginning in the South (South America, Africa and Australasia) remain mostly in the Tropics (45–50 % of their lifetime). Due to elevated O3 sensitivities, emissions in Australasia induce the highest overall radiative forcing, attaining values that are larger by factors of 2.7 and 1.2 relative to Eurasia during January and July, respectively. The location of the emissions does not necessarily correspond to the region that will be most affected – for instance, NOx over North America in July will induce the largest radiative forcing in Europe. Overall, this study highlights the spatially and temporally heterogeneous nature of the NOx–O3 chemistry from a global perspective, which needs to be accounted for in efforts to minimize aviation’s climate impact, given the sector’s resilient growth.
Spatiotemporal variations of the δ(O2 ∕ N2), CO2 and δ(APO) in the troposphere over the western North Pacific
Authors: Ishidoya Shigeyuki , Tsuboi Kazuhiro , Niwa Yosuke , Matsueda Hidekazu , Murayama Shohei , Ishijima Kentaro , Saito Kazuyuki
Published: May 2022

Abstract. We analyzed air samples collected on board a C-130 cargo
aircraft over the western North Pacific from May 2012 to March 2020
for atmospheric δ(O2 / N2) and CO2 amount fraction.
Observations were corrected for significant artificial fractionation of
O2 and N2 caused by thermal diffusion during the air sample
collection using the simultaneously measured δ(Ar / N2). The
observed seasonal cycles of the δ(O2 / N2) and atmospheric
potential oxygen (δ(APO)) varied nearly in opposite phase to the cycle of
the CO2 amount fraction at all latitudes and altitudes. Seasonal
amplitudes of δ(APO) decreased with latitude from 34 to
25∘ N, as well as with increasing altitude from the surface to 6 km by 50 %–70 %, while those of the CO2 amount fraction decreased by less
than 20 %. By comparing the observed values with the simulated δ(APO) and CO2 amount fraction values generated by an atmospheric
transport model, we found that the seasonal δ(APO) cycle in the
middle troposphere was modified significantly by a combination of the
northern and southern hemispheric seasonal cycles due to the
interhemispheric mixing of air. The simulated δ(APO) underestimated
the observed interannual variation in δ(APO) significantly, probably
due to the interannual variation in the annual mean air–sea O2 flux.
Interannual variation in δ(APO) driven by the net marine biological
activities, obtained by subtracting the assumed solubility-driven component
of δ(APO) from the total variation, indicated a clear influence on
annual net sea–air marine biological O2 flux during El Niño and
net air–sea flux during La Niña. By analyzing the observed secular
trends of δ(O2 / N2) and the CO2 amount fraction, global
average terrestrial biospheric and oceanic CO2 uptakes for the period
2012–2019 were estimated to be (1.8±0.9) and (2.8±0.6) Pg a−1 (C equivalents), respectively.
Nitrogen isotopes reveal a particulate-matter driven biogeochemical reactor in a temperate estuary
Authors: Dähnke Kirstin , Sanders Tina , Voynova Yoana , Wankel Scott D.
Published: May 2022

Abstract. Estuaries and rivers are important biogeochemical reactors that act to modify nutrient loads and composition in the intensively used coastal zone. In a case study during July 2013, we sampled an 80 km transect along the Elbe estuary under unusually low-oxygen conditions. To better elucidate specific mechanisms of estuarine nitrogen processing, we tracked the evolution of the stable isotopic composition of nitrate, nitrite, particulate matter, and ammonium through the water column. We used this exceptional summer situation to constrain the in-situ isotope effects of ammonium and nitrite oxidation and of remineralization at the reach scale. The isotope effects of nitrite oxidation and ammonium oxidation are consistent with pure culture assessments. We found that estuarine biogeochemistry is governed by settling, resuspension, and remineralization of particulate matter. We used the stable isotope data to quantify sources and sinks of nitrogen in the Elbe estuary. An isotope mass balance box-model was developed to reproduce internal N-cycling and associated isotope dynamics. The model underscores the role of the delivery and reactivity of particulate matter, but it also allowed us to pinpoint additional sinks of reactive nitrogen, such as the denitrification of water column nitrate in the intensively dredged and deep Hamburg harbour basin.
Twenty-one years of hydrological data acquisition in the Mediterranean Sea: quality, availability, and research
Authors: Ribotti Alberto , Sorgente Roberto , Pessini Federica , Cucco Andrea , Quattrocchi Giovanni , Borghini Mireno
Published: May 2022

Abstract. Since 2000 and for the following 20 years hydrological data of the Mediterranean Sea, with a particular focus on the western and central Mediterranean sub-basins, have been acquired to study the hydrodynamics at both coastal and open sea scales. Totally 1468 hydrological casts were realized in 29 oceanographic cruises planned due to scientific purposes linked with funding research projects but sometimes driven by sea conditions and type of vessel. After an accurate quality assurance and control, following standard procedures, all hydrological data were included in four online public open access repositories in SEANOE available from https://doi.org/10.17882/87567 (Ribotti et al., 2022). Hydrological and dissolved oxygen data are always present in all the datasets whereas pH, fluorescence, turbidity and CDOM are available just for some cruises. Samplings were carried out mainly along transects with some repetitions in the years. Then the results of two data analyses, staircase systems in the Tyrrhenian Sea and in the Algero-Provencal sub-basin and spreading of the Western Mediterranean Transient, are mentioned.
Impacts of different types of El Niño events on water quality over the Corn Belt, United States
Authors: Chen Pan , Li Wenhong , He Keqi
Published: May 2022

Abstract. The United States Corn Belt region, which primarily includes two large basins, namely, the Ohio-Tennessee River Basin (OTRB) and the Upper Mississippi River Basin (UMRB), is responsible for the Gulf of Mexico hypoxic zone. Climate patterns such as El Niño can affect the runoff and thus the water quality over the Corn Belt. In this study, the impacts of eastern Pacific (EP) and central Pacific (CP) El Niños on water quality over the Corn Belt region were analyzed using the Soil and Water Assessment Tool (SWAT) models. Our results indicated that at the outlets, annual total nitrogen (TN) and total phosphorus (TP) loads decreased by 13.1 % and 14.0 % at OTRB, 18.5 % and 19.8 % at UMRB, respectively, during the EP-El Niño years, whereas during the CP-El Niño years, they increased by 3.3 % and 4.6 % at OTRB, 5.7 % and 4.4 % at UMRB, respectively. On the sub-basin scales, more sub-basins showed negative (positive) anomalies of TN and TP during EP- (CP-) El Niño. Seasonal study confirmed that water quality anomalies showed opposite patterns during EP- and CP-El Niño years. At the outlet of OTRB, seasonal anomalies in nutrients matched the El Niño-Southern Oscillation (ENSO) phases, illustrating the importance of climate variables associated with the two types of El Niño on water quality in the region. At the UMRB, TN and TP were also influenced by agriculture activities within the region and their anomalies became greater in the growing seasons during both EP- and CP-El Niño years. Quantitative analysis of precipitation, temperature, and their effects on nutrients suggested that precipitation played a more important role than temperature did in altering water quality in the Corn Belt region during both types of El Niño years. We also found specific watersheds (located in Iowa, Illinois, Minnesota, Wisconsin, and Indiana) that faced the greatest increases in TN and TP loads, were affected by both the precipitation and agricultural activities during the CP-El Niño years. The information generated from this study may help proper decision-making for water environment protection over the Corn Belt.
DETECTING ANTHROPOGENIC VOLUME CHANGES IN CROSS SECTIONS OF A SANDY BEACH WITH PERMANENT LASER SCANNING
Authors: Kuschnerus M. , Lindenbergh R. , Lodder Q. , Brand E. , Vos S.
Published: May 2022

Abstract. Coastal areas world wide are highly dynamic areas, subject to continuous deformation processes. Both natural and anthropogenic processes constantly cause changes at various spatial scales. Sandy beaches in the Netherlands fall under a regulation, according to which moving sand is permitted, if the volume change remains below a certain threshold. The threshold holds for volume changes within a cross section of 1 m width of the beach. The enforcement of this rule is currently labor intensive, because monitoring generally happens only on a yearly basis, or incidental and non-quantitative. Improved observation capabilities with remote sensing are advancing the supporting technology for this kind of regulations. Permanent laser scanning is a potential tool for monitoring and quantifying volume changes of a section of the beach. We develop and implement methodology to extract time series of volume change with respect to a reference date of 01-01-2020 covering January 2020 until the end of April 2020. The method is applied on point cloud data from a permanent laser scanner on the coast of Noordwijk, The Netherlands. We analyse the time series for incidents, where the threshold in volume change is passed, and find all shortest intervals during which the threshold is passed. Then we analyse potential underlying cause in order to support not only enforcement, but also evaluation of the current regulation. This will ultimately help to work towards a better understanding of the influence of small scale human activities on coastal development.
THE STABILITY OF A PERMANENT TERRESTRIAL LASER SCANNING SYSTEM – A CASE STUDY WITH HOURLY SCANS
Authors: Voordendag A. B. , Goger B. , Klug C. , Prinz R. , Rutzinger M. , Kaser G.
Published: May 2022

Abstract. The stability of the permanently installed terrestrial laser scanner (TLS) in a high mountain environment at Hintereisferner glacier, Ötztal Alps, Austria, is tested. From previous studies it is already known that the uncertainty of the permanent setup results from scanning geometry, atmospheric conditions and instrumental limitations. This study focuses on the instrumental limitations related to the lack of perfect stability of the TLS. A case study is performed with hourly scans over the glacier and the data of the internal inclination sensors are read. A comparison of the scanning data with the inclination data shows that the TLS at Hintereisferner is affected by both high-frequency vibrations and coarser movements. The high-frequency vibrations cause radial stripes in the data, and cannot be corrected, as the internal inclinations sensors of the TLS measure at a frequency of 1 Hz, whereas pulses are emitted at effectively 23 kHz. The coarser movements are indicated by the measurement of roll and pitch with the internal inclination sensors and can be corrected by manually georeferencing the data.In order to complete the uncertainty assessment of a permanent long-range TLS system in a high mountain environment, future work will concentrate on the impact from the scanning geometry and from the atmospheric variables. The finalised uncertainty assessment is crucial to derive the smallest magnitude at which snow (re)distribution can be detected and, thus, significantly will improve the treatment of snow cover dynamics in future glacier mass balance research.
COMPARISON OF SINGLE-IMAGE URBAN HEIGHT RECONSTRUCTION FROM OPTICAL AND SAR DATA
Authors: Schmitt M. , Recla M.
Published: May 2022

Abstract. Deep learning-based depth estimation has become an important topic in recent years, not only in the field of computer vision. Also in the context of remote sensing, scientists started a few years ago to adapt or develop suitable approaches to realize a reconstruction of the Earth’s surface without requiring several images. There are many reasons for this: First, of course, the aspect of general economization, since especially high-resolution satellite images are often accompanied by high data acquisition costs. In addition, there is also the desire to be able to acquire high-quality geoinformation as quickly as possible in time-critical cases – for example, the provision of up-to-date maps for emergency forces in disaster scenarios. Finally, a reconstruction of topography based only on single images can also provide important approximate values for the classic multi-image methods. For example, various processing steps in a classical InSAR process chain require a rough knowledge of the Earth’s surface in order to achieve the most accurate and reliable results. In this paper, we review the developments documented in the remote sensing literature so far. Using an established neural network architecture, we produce example results for both very-high-resolution SAR and optical imagery. The comparison shows that SAR-based single-image-height reconstruction seems to bear an even greater potential than single-image height reconstruction from optical data.
HBIM LIBRARY DEVELOPMENT FOR A DORIC TEMPLE COLUMN
Authors: Kontoudaki A. , Georgopoulos A.
Published: May 2022

Abstract. The growing interest within the construction industry in the preservation, rehabilitation, and conservation of heritage-value buildings has led to the implementation of Historic Building Information Modelling (HBIM). Especially in Greece, where the number of historical buildings is considerably large, having efficient and standardized processes of Cultural Heritage management and conservation becomes a prerequisite. The rigorously accurate representation of old constructions requires specific geometric modelling processes in order to generate the necessary relevant libraries of parametric objects. This paper describes the development of a parametrized library for the main parts of a Doric Temple to be used for introducing such parts in an HBIM system. Moreover, to verify the correctness of the parametrized column an application is performed in which the dimensions of a real column are compared with the dimensions of the standard one. The study improves the potential of the BIM process for its application to buildings with distinctive architecture in the context of the preservation or conversion of heritage buildings. In first section the term HBIM is explained and its usefulness to cultural heritage (CH) is justified. Section 2 describes previous attempts to create HBIM libraries for historic structures. After that, the construction of parametric model, which was created with visual programming Dynamo, is presented. Finally, the efficiency of the adjustment of a standard column to a given point cloud is described and in the end the results of the application are compared and discussed.
SEMANTIC URBAN MESH SEGMENTATION BASED ON AERIAL OBLIQUE IMAGES AND POINT CLOUDS USING DEEP LEARNING
Authors: Wilk Ł. , Mielczarek D. , Ostrowski W. , Dominik W. , Krawczyk J.
Published: May 2022

Abstract. The use of deep machine learning methods for semantic classification of city mesh models is one of the current trends in geoscience development. Thanks to the thriving development of Convolutional Neural Networks (CNNs) it is now achievable to conduct fully automated process of building aforementioned 3D model by means of photogrammetric techniques and supplement it with additional semantic information obtained by Artificial Intelligence (AI) algorithms. In order to guarantee the comprehensiveness of said information it is essential to use an extensive range of 3D data including oblique aerial imagery and aerial laser scanning (ALS). Such comprehensive 3D mesh models may be later implemented in many Digital Twin class solutions additionally supported with modern GIS systems and its algorithms. To proof the validity of this thesis, the article showcases results of research conducted using deep learning based solutions tested on two datasets - real-world data in the form of oblique aerial images and ALS point clouds acquired in Bordeaux, France using novel Leica CityMapper-1 multisensoral system and large-scale dataset from SUM: A Benchmark Dataset of Semantic Urban Meshes. Both subalgorithms make use of CNNs as its core-feature. To perform accurate classification of oblique aerial scenes PSP-Net architecture accelerated by techniques of transfer learning has been used. Second algorithm destined for ALS point clouds utilizes CNN as well, but in this case implementation is based on proprietary architecture. The results of the experiments demonstrate that the utilizing these two mutually complementary solutions to extract new semantic information for city mesh models in proposed manner compared with the state-of-the-art methods achieves competitive classification performance.
DEEP CONVOLUTION NEURAL NETWORKS WITH RESNET ARCHITECTURE FOR SPECTRAL-SPATIAL CLASSIFICATION OF DRONE BORNE AND GROUND BASED HIGH RESOLUTION HYPERSPECTRAL IMAGERY
Authors: Galodha A. , Vashisht R. , Nidamanuri R. R. , Ramiya A. M.
Published: May 2022

Abstract. Drones have been of vital importance in the fields of surveillance, mapping, and infrastructure inspection. Drones have played a vital role in acquiring high-resolution images and with the present need for precision farming, drones have helped in crop classification and monitoring various crop patterns. With the recent advancement in computational power and development of robust algorithms to carry out deep feature learning and neural network, based learning such techniques have regained prominence in contemporary research areas such as classification of common 2-D and 3-D images, object detection, etc. In our research, we propose a deep convolutional neural network architecture (CNN) for the classification of aerial images captured by drones and high-resolution Terrestrial Hyperspectral (THS or HSI) which includes 6-layers and with weights optimized along with the input layer, the convolutional layer, the max-pooling layer, the fully connected layer, softmax probability classifier, and the output layer. We have acquired THS (using Cubert-GmbH data) and drone agricultural data of seasonal crops sowed during the months of March-June for the year 2017. Crop patterns include Cabbage, Eggplant, and Tomato with varying nitrogen concentrations in the region of Bangalore, Southern India. To study the influence and impact of CNN, the ResNets model has been applied. ResNets model and architecture are combined with a deep learning network followed by a recurrent neural learning network model (RCNN). The HSI input layer with corresponding ground truth data for the region is fed into the ResNets model with a spectral and spatial residual network for the 7*7*139 input Hyperspectral Imagery (HSI) volume. The network includes two spectral and two spatial residual blocks. An average pooling layer and a fully connected layer transform into a 5*5*24 spectral-spatial feature volume further to a single output feature vector. At present we use an RMSProp optimizer for error loss minimization which when applied to the drone data was able to achieve an overall accuracy of 97.16%. Similarly, for cabbage, eggplant and tomato acquired through the same method we achieved overall accuracy at 87.619%, 89.25%, and 80.566% respectively in comparison to ground truth labels. Drones and ground-based datasets equipped with good computational techniques have become promising tools for improving the quality and efficiency of precision agriculture today.
CALIBRATION OF PHOTOGRAMMETRIC SYSTEM FOR 3D MEASUREMENTS IN INACCESSIBLE MULTIMEDIA WORKING SPACE
Authors: Knyaz V. A. , Kniaz V. V. , Ippolitov E. V. , Novikov M. M.
Published: May 2022

Abstract. Hydrodynamic tunnel has proved to be an effective mean for studying aerodynamic processes using scaled models of real aircrafts. The specificity of study in a hydrodynamic tunnel requires to apply non-contact measuring techniques, such as photogrammetry, for retrieving information on geometrical characteristics of the flow. So reliable calibration of photogrammetric system is a key element for trustworthy analysis of flow behaviour. For 3D measurements in a hydrodynamic tunnel calibration technique must account for refraction effects at optical media interfaces. But often the design of a hydrodynamic tunnel does not allow to perform standard calibration procedure based on image acquisition of a special test field, placed in the working space of the measuring system. The presented study addresses this problem – developing the technique for accurate photogrammetric calibration, for the case of the working space being inaccessible for placing there a test field. The developed calibration technique estimates parameters of interior orientation of the photogrammetric system and parameters of multimedia optical environment by special preliminary procedure, that allows to obtain accurate 3D measurements during experiments in hydrodynamic tunnel. Experimental evaluation of the developed technique demonstrated high accuracy of photogrammetric 3D measurements.
3D MODELLING OF A HISTORIC WINDMILL: PPK-AIDED TERRESTRIAL PHOTOGRAMMETRY VS SMARTPHONE APP
Authors: Eker R. , Elvanoglu N. , Ucar Z. , Bilici E. , Aydın A.
Published: May 2022

Abstract. Cultural heritage (CH), what we inherited from the past generations, is a precious asset connecting the past to the present. It has many demonstrable benefits to nations around the world. For many countries, it has been a part of national identity as well as a key driver of the economy. However, CH is under constant threat of demolition due to wars, natural and human-induced hazards, and negligence. Therefore, documentation of CH has become very essential. Recent advancements in remote sensing technology have improved upon approaches for the surveying and structural modelling of the CH. This paper examines two close-range photogrammetry approaches in modelling a historic windmill. In the first approach, to generate a 3D model of the windmill, the images were obtained with a PPK-aided system and then processed through the Structure-from-Motion (SfM) method in Agisoft Metashape software. The second approach utilized a smartphone app both to capture the images and then generate the 3D model of the windmill with SfM. The 3D models of windmills, generated with two different methods, were compared in CloudCompare software using the cloud-to-mesh distance (C2M) tool. Two models were aligned with point pairs-picking for registration and the result showed that the models are quite similar and distance between the two models ranged from −5cm to +5cm.
HIGH PRECISION AUTOMATIC EXTRACTION OF CULTURAL RELIC DISEASES BASED ON IMPROVED SLIC AND AP CLUSTERING
Authors: Hu C. , Huang X. , Xia G. , Wang Y. , Liu X. , Ma X.
Published: May 2022

Abstract. Automatic and high-precision detection and quantitative expression of cultural relics diseases are important contents of cultural relics science and technology protection. Aiming at the problem of automatic extraction of boundary cultural relics diseases, this paper proposes an adaptive SLIC0 combined with AP clustering method to achieve high-precision detection of disease areas. Firstly, based on the SLIC0 algorithm, the selected area of the disease orthophoto frame is segmented, and the Canny edge detection is used as the true value. The number of superpixels is iterative until the accuracy meets the requirements, so as to achieve the best fitting of superpixel edges. Then the AP clustering method is used to merge the superpixels of the disease area to obtain the edge information. Finally, taking the surface shedding disease of painted cultural relics as an example, this method is applied to realize the high precision extraction and quantitative expression of the edge of the disease. The correctness, feasibility and advancement of the algorithm are proved by comparing with the existing manual methods. The method in this paper provides an efficient and high-precision means for the quantitative expression of cultural relics diseases, and can provide accurate data support for the scientific and technological restoration of cultural relics.
APPLICATION OF PHOTOGRAMMETRY FOR DIGITIZING INFORMATION ABOUT CULTURAL HERITAGE IN THE FORM OF DISPLAY HOLOGRAMS
Authors: Rabosh E. V. , Balbekin N. S. , Mezhenin A. V. , Petrov N. V.
Published: May 2022

Abstract. The aim of this article is to demonstrate the possibility of using photogrammetry as a method for documenting and digitizing data reconstructed from a display hologram. We discuss the importance of the task of digitizing information from holograms and the search for its solution, which will allow display holography to gain new distribution. Textured point clouds were obtained for the object and for its image reconstructed from the display reflective type hologram using monochromatic radiation. The requirements for the experimental setup for the digitization of the reconstructed from hologram object wavefront, as well as for the number of photographs for the hologram capturing are formulated and explained. The calculated three-dimensional models of the object and the reconstructed object wavefront were compared according to the Pearson correlation criterion. The achieved correlation value is 0.903. The results of comparison of two 3D models in the estimation of the standard deviation are given.
PHOTOGRAMMETRIC AND FLUORESCENCE SOLUTIONS FOR MONITORING OF HABITAT FORMING ORGANISMS
Authors: Rossi P. , Righi S. , Parente L. , Castagnetti C. , Cattini S. , Di Loro G. , Falvo E. , Grassi F. , Mancini F. , Rovati L. , Simonini R. , Capra A.
Published: May 2022

Abstract. The development and testing of innovative technologies and automated data analysis methodologies offer tools for the monitoring of complex marine ecosystems and the direct and indirect effects of climate change on natural heritage. Photogrammetric methods allow precise mapping of the underwater landscape as well as detailed three-dimensional (3D) reconstruction of marine structures, improving the study of complex marine ecosystems. Moreover, fluorescence analyses can provide critical information about the health status of marine organisms. Analysing the variations in their self-fluorescence, allow for early detect changes in their physiological state. These applications provide very useful data to evaluate the health state of biodiversity-rich 3D biogenic structures and make measurements of fine-scale changes, with greater precision than existing methodologies. This contribution shows a multidisciplinary approach to the design, development, and implementation of a technological solution based on the above-mentioned optical measuring systems. Such a system is characterized by a reflex camera, LED-based light sources, and filters to allow the analysis of the self-fluorescence signal. The proposed solution aspires to improve the standardization of monitoring plans through non-destructive fine-scale accurate data collection for image analysis and multi-temporal comparisons, providing challenging stepping-stones for habitat-forming anthozoan management and restoration activities. Initial results of tests carried out in controlled conditions are shown. The photogrammetric approach resulted in 3D reconstructions that allow the monitoring of deformations at millimetre scale. The fluorimetry methodology allowed to obtain high-resolution images with great repeatability, which enabled the identification of stressful status even in absence of geometric deformations. The proposed approaches and obtained results are discussed, together with potential issues related to their implementation in a real-world context adopting remotely operative vehicles.
INTEGRATION OF UAV-LIDAR AND UAV-PHOTOGRAMMETRY FOR INFRASTRUCTURE MONITORING AND BRIDGE ASSESSMENT
Authors: Gaspari F. , Ioli F. , Barbieri F. , Belcore E. , Pinto L.
Published: May 2022

Abstract. The health assessment of strategic infrastructures and bridges represents a critical variable for planning appropriate maintenance operations. The high costs and complexity of traditional periodical monitoring with elevating platforms have driven the search for more efficient and flexible methods. Indeed, recent years have seen the growing diffusion and adoption of non-invasive approaches consisting in the use of Unmanned Aerial Vehicles (UAVs) for applications that range from visual inspection with optical sensors to LiDAR technologies for rapid mapping of the territory. This study defines two different methodologies for bridge inspection. A first approach involving the integration of traditional topographic and GNSS techniques with TLS and photogrammetry with cameras mounted on UAV was compared with a UAV-LiDAR method based on the use of a DJI Matrice 300 equipped with a LiDAR DJI Zenmuse L1 sensor for a manual flight and an automatic one. While the first workflow resulted in a centimetric accurate but time-consuming model, the UAV-LiDAR resulting point cloud’s georeferencing accuracy resulted to be less accurate in the case of the manual flight under the bridge for GNSS signal obstruction. However, a photogrammetric model reconstruction phase made with Ground Control Points and photos taken by the L1-embedded camera improved the overall accuracy of the workflow, that could be employed for flexible low-cost mapping of bridges when medium level accuracy (5–10 cm) is accepted. In conclusion, a solution for integrating interactively final 3D products in a Bridge Management System environment is presented.
TYPHOON DAMAGE MAPPING USING NORMALIZED DIFFERENCED SPATIAL AUTOCORRELATION AND PLANETSCOPE IMAGE
Authors: Blanco A. C.
Published: May 2022

Abstract. Damages to built and natural environments are essentially changes that needs to be detected and quantified. This is particularly true for the change detection approach. While the use of vegetation indices is effective for such assessment in natural or vegetated areas, the use of built-up indices does not yield useful results. This is because there is usually no significant reduction in materials. However, typhoon-damaged buildings are usually characterized by a change in form, shape, color, and texture. In this study, we examined the use of local spatial autocorrelation (LSA) to evaluate the level of damage to buildings. In particular, the local Moran’s I was used, and an index called the normalized difference spatial autocorrelation (NDSA) was developed. Similar with other indices, the values are within the range of −1 to 1. NDSA using local Getis-Ord Gi and local Geary’s C were also generated. With the observation that LSA generally decreases due to damages (especially to manmade structures), positive NDSA identifies the damage areas. The magnitude of the values corresponded to the level or degree of damage sustained as interpreted from very high-resolution satellite image. It was noted that the manual tagging of damages had missed buildings which are clearly damaged or destroyed based on the visual comparison of pre- and post-typhoon satellite images. This illustrates the value of the NDSA not only to assess damage on its own but also for guiding manual tagging from image and prioritizing post-disaster needs assessment and recovery operation.
SMALL UAVS-BASED DISASTER DAMAGE INVESTIGATION: FOCUSED ON FLOODING DISASTER AND CHEMICAL ACCIDENT
Authors: Lim E. T. , Jung Y. H. , Shin D. Y. , Kim S. S.
Published: May 2022

Abstract. Disasters that occur in recent years are linked to various factors and occur in a chain, and the scale is also increasing. In order to solve the more complex and serious disaster problem, it is necessary to expand the affect scope of the disaster and to acquire and manage information by integrating and operating various sensors. With the introduction of UAVs for disaster work, various studies such as investigation of the affected area, rescue of survivors, and establishment of emergency communication networks are being conducted. This is useful for strengthening the field of disaster management because it is very effective in terms of time and human resources. In addition, it is possible to acquire high-resolution images, and by using the 3D point cloud data and ortho-mosaic imagery generation, it is possible to produce a disaster scene close to the real world. Therefore, if UAV information can be linked to disaster types and characteristics prior to supporting disaster damage investigation, it is possible to establish a response method according to the disaster damage situation and help decision-making. In order to examine the applicability of UAV technology for actual disaster sites, this study conducted data collection and analysis of damaged areas targeting sites damaged by heavy rain and chemical accidents. In the heavy rain damage site, flooding damage occurred in the ginseng cultivation patches due to river overflow caused by the torrential rain, and the damaged area and lots were investigated. The site of the chemical accident was damaged by hydrochloric acid gas leaking into the atmosphere, and the surrounding forest, crops, and residential facilities were identified and the affected area was calculated. As a result of investigating the damage from heavy rain and chemical accidents using UAV, it was possible to quickly collect data on a wide disaster site. In addition, it was easy to calculate the damaged area based on ortho-imagery, and the limitations of spatial analysis were reduced, so that it could be used more specifically and efficiently to the disaster site.
DEFORMATION EXTRACTION AND ANALYSIS OF COSEISMIC DEFORMATION FIELD BASED ON D-INSAR TECHNOLOGY
Authors: Shi A. , Jiang L. , Zhou L. , Tang H. , Wei B. , Li J.
Published: May 2022

Abstract. Extracting the seismic deformation field and analyzing the surface deformation after an earthquake occurs are of great significance for explaining earthquake mechanism and earthquake relief. Through the dual polarization mode of the two-pass method D-InSAR technology, this paper extracted the coseismic deformation fields based on two Sentinel-1A SAR images covering the Yulin earthquake area, and analyzed the surface deformation. The results show that the subsidence area obtained by VV and VH polarization modes are consistent. The uplift center areas are located in Tongxin Village in Rong County and Mapo Town in Luchuan County, and the subsidence center area is located in Fuxin Town of Beiliu City in the northeast. In the earthquake area, the maximum uplift derived by the VV mode SAR data is 12.1 cm, and the maximum subsidence is 5.8 cm. the maximum uplift obtained by the VH mode SAR data is 11.2 cm, and the maximum subsidence is 9 cm. The results obtained by the two polarization modes are consistent. D-InSAR technology can accurately obtain the coseismic deformation field, which provides data guarantee for the follow-up study of the inversion of seismic source parameters in the region.
APPLICATION OF SAR TIME-SERIES AND DEEP LEARNING FOR ESTIMATING LANDSLIDE OCCURRENCE TIME
Authors: Wang W. , Motagh M. , Plank S. , Orynbaikyzy A. , Roessner S.
Published: May 2022

Abstract. The time series of normalized difference vegetation index (NDVI) and interferometric coherence extracted from optical and Synthetic Aperture Radar (SAR) images, respectively, have strong responses to sudden landslide failures in vegetated regions, which is expressed by a sudden increase or decrease in the values of NDVI and coherence. Compared with optical sensors, SAR sensors are not affected by cloud and daylight conditions and can detect the occurrence time of failure in near real-time. The purpose of this paper is to automatically determine the time of failure occurrence using time series coherence values. We propose, based on some existing anomaly detection algorithms, a deep neural network-based anomaly detection strategy that combines supervised and unsupervised learning without a priori knowledge about failure time. Our experiment using July 21, 2020 Shaziba landslide in China shows that in comparison to widely used unsupervised methodology, the use of our algorithm leads to a more accurate detection of the timing of the landslide failure.
UNSUPERVISED HARMONIOUS IMAGE COMPOSITION FOR DISASTER VICTIM DETECTION
Authors: Zhang N. , Nex F. , Vosselman G. , Kerle N.
Published: May 2022

Abstract. Deep detection networks trained with a large amount of annotated data achieve high accuracy in detecting various objects, such as pedestrians, cars, lanes, etc. These models have been deployed and used in many scenarios. A disaster victim detector is very useful when searching for victims who are partially buried by debris caused by earthquake or building collapse. However, considering that larger quantities of real images with buried victims are difficult to obtain for training, a deep detector model cannot give full play to its advantages. In this paper we generate realistic images for training a victim detector. We first randomly cut out human body parts from an open source human data set and paste them into the ruins background images. Then, we propose an unsupervised generative adversarial network (GAN) to harmonize the body parts to fit the style (illumination, texture and color characteristics) of the background. These generated images are finally used to fine-tune a detection network YOLOv5. We evaluate both the AP (average precision) for IoU (Intersection over Union) 0.5 and for IoU ∈ [0.5:0.05:0.95], which are denoted as AP@0:5 and AP@[.5 : .95], respectively. The best experimental results show that the YOLOv5l pre-trained on the COCO data set performs poorly on detecting victims, and the AP@[.5 : .95] is only 19.5%. The model that uses our composite images as fine-tuning data can effectively detect victims, and increases the AP@[.5 : .95] to 33.6%. The AP@0:5 increases from 32.4% to 53.4%. Our unsupervised harmonization method further improves the results by 2.1% and 6.1%, respectively.
MAPPING NATURAL NON-FOREST VEGETATION REMOVAL IN THE BRAZILIAN AMAZON – A PILOT PROJECT
Authors: Almeida C. , Silva D. , Messias C. G. , Soler L. S. , Gusmão L. H. , Maurano L. E. P. , Souza T. , Soares I. , Barradas D. , Cunha I. P. , Bastos E. , Belluzo A. , Quadros C. , Pechini B. , Pinheiro F. , Silva L. J.
Published: May 2022

Abstract. The increasing pressure from agriculture and pasture activities over non-forest vegetation areas revealed the need of INPE to extend forest regular monitoring to non-forested areas. Non-forest vegetation consists of savannahs, shrub lands, grasslands or seasonal floodplains that occupy 279 thousand km2 (6,63 %) of Brazilian Amazon biome extension. Addressed ecosystem services of non-forest vegetation vary from climate, soil, carbon storage, biodiversity, water and fire regulation to cultural benefits and living of the population. The challenge in monitoring non-forest removal lies in climatic seasonality, high variability of phytophysiognomies and cloud coverage. In this work we developed a method to map non-forest vegetation from 2000 to 2021 in a pilot area enclosing five municipalities in Roraima and Amapá states. Considering the total area of 47 thousand km2, 58 % was originally forest and 37 % was non-forest vegetation. In 2020, soybean planted areas occupied 60 thousands hectares in these municipalities. We adapted PRODES protocol to non-forest features using visual interpretation of Landsat and SENTINEL-2 satellite images. Regional expertise supported the interpretation keys to distinguish non-forest removal from seasonal changes or other land use changes. A baseline map for 2000 is provided together with biannual and annual increments for 2002–2018 and 2019–2021, respectively. Results show removal of non-forested areas even overpassing deforestation in some municipalities. Accumulated non-forest removal was 3.133,06 km2 or 17,44 % of the non-forest area enclosed. This work consolidated the method to be applied in the Brazilian Amazon biome allowing a uniform historical mapping series of deforestation and non-forest removal.
THE XIONG’AN NEW AREA DEFORMATION MONITORING USING SENTINEL-1 SAR DATA BASED ON SBAS-INSAR TECHNIQUE
Authors: Wang X. , Zhang P. , Wu J. , Sun Z. , Zhang Q.
Published: May 2022

Abstract. In order to study the land subsidence trend since the construction of the Xiong’an New Area, 125 images of Sentinel-1 from January 2017 to September 2020 was obtained in this paper, and the average land subsidence rate and cumulative land subsidence of Xiong’an New Area is obtained by the SBAS-InSAR technology of multi-main image coherent target by GAMMA and the Self-developed software, and the causes of land subsidence from the geographical space in the Xiong’an New Area were analyzed by the divisional timing analysis method. The results showed that about 80% of Xiong’an New Area had slight land subsidence with the rate less than 1cm/yr and the cumulative subsidence less than 3cm, and the maximum subsidenc occurred in the northern of Xiongxian County with rate 7cm/yr and the cumulative subsidence was 30cm. Furthermore, the average subsidence rate and the cumulative maximum subsidence in the north and the northwest of Xiongxian County, the Baima Village and the Beifeng Village were studied. The research results showed that the land subsidence in Xiong’an New Area was related to the over exploitation of geothermal resources and groundwater, and the results could provide important reference values for keep abreast of the land subsidence status of the Xiong’an New Area, as well as the healthy development and long-term planning of the New Area.
SELF-TRAINING FOR SEMI-SUPERVISED DEEP CONTOUR DETECTION OF SURFACE WATER
Authors: Alsamman A. , Syed M. B.
Published: May 2022

Abstract. Contour detection is better for monitoring dynamic and long-term changes to surface water bodies. For that purpose, we present a semi-automated method for collecting and labeling water contours from Landsat-8 and Sentinel-2 images. Due to the need for human inspection, the method has thus far generated 14K labeled images from more than 1.5M images. Given the cost of data labeling, we propose a deep semi-supervised self-learning system performed in two training stages, known as teacher-student. The teacher is trained on the accurate human-labeled data, then used to pseudo label the remaining unlabeled data. The student is trained on both human-labeled and machine pseudo-labeled data. For both teacher and student, we use a uniquely designed multiscale UNet classifier that uses fewer parameters and is more accurate than other state-of-the-art classifiers. Random augmentations are used to “noise” the student model and improve its generalization, and normalization schemes are used to blend the human-labeled loss with the machine-labeled loss. Comparisons to existing water body detection classifiers and segmentation classifiers show the superiority of our proposed system in detecting water contours.