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There has been controversy regarding the air pollutants emitted from sources closely related to the activities of daily life in China, such as cooking, setting off fireworks, sacrificial incense and joss paper burning, and barbecue (which have been named the five missing sources – FMSs), and the extent to which they impact the outdoor air quality. To date, due to the lack of both an activity dataset and emission factors, there have been no estimations of the emission of air pollutants from FMSs. In this work, we have attempted to combine questionnaire data, various statistical data, and data on points of interest to obtain a relatively complete set of activity data. The emission factors (EFs) of carbonaceous aerosols were tested in our lab. Emission inventories of carbonaceous aerosols with a high spatial–temporal resolution for FMSs were then established, and the spatial variation trend and driving forces were discussed. From 2000 to 2018, organic carbon (OC) emissions were in the range of 4268–4919 t. OC emissions from FMSs were between 1.5 ‰ and 2.2 ‰ of the total emissions in China. Black carbon (BC), elemental carbon (EC), and brown carbon (BrC) absorption cross-section (ACS BrC) emissions from FMSs were in the ranges of 22.6–43.9 t, 213–324 t, and 14.7–35.6 Gm 2 , respectively. Their emissions tended to be concentrated in certain periods and areas. The OC emission intensities in central urban areas were 3.85–50.5 times those in rural areas (due to the high density of human activity), while the ACS BrC emissions in rural regions accounted for 63.0–79.5 % of the total emissions (resulting from uncontrolled fireworks). A mass of fireworks led to much higher ACS BrC and EC emissions on Chinese New Year's Eve, with respective values that were 1444 and 262 times their corresponding yearly averages. Significant (p<0.01) correlations between the population's income and pollutant emissions were also found: these correlations were positive (r=0.94) and negative (r=-0.94) for urban and rural regions, respectively, indicating the necessity to regulate residents' lifestyles and increase residents' income in respective urban and rural regions. This study provides firsthand data to identify emissions, variation trends, and impacting factors from the FMSs. This is helpful for modeling works on air quality, the climate effect, and human health risks during specific periods or in specific regions as well as for modifying emission control policies. The data compiled in this work can found at 10.6084/m9.figshare.19999991.v2 (Cheng et al., 2022). [ABSTRACT FROM AUTHOR]

There has been controversy about the air pollutants emitted from sources closely related to people's daily life (such as cooking, fireworks burning, sacrificial incenses and joss paper burning, and barbecue, named as five missing sources, FMS) impacting the outdoor air quality to what extent. Till now, there is no emission estimation of air pollutants from FMS, as the missing of both activity dataset and emission factors. We attempted to combine the questionnaire data, various statistical data, and data of points of interest to obtain a relatively complete set of activity data. The emission factors (EFs) of carbonaceous aerosols were tested in our lab. Then, the emission inventories of carbonaceous aerosol with the high spatial-temporal resolution for FMS were established firstly, and the spatial-variation trend and driving forces were discussed. From 2000 to 2018, organic carbon (OC) increased from 4268 t to 4919 t. The OC emission from FMS was 1.48-2.18 - of its total emission in China. The emissions of black carbon, element carbon (EC), and brown carbon absorption cross-section (ACSBrC) emissions from FMS were in the ranges of 22.6-43.9 t, 213-324 t, and 14.7-35.6 Gm2, respectively. Their emissions tended to concentrate in special periods and areas. The OC emission intensities in central urban areas were 3.85-50.5 times that of rural areas due to the high density of human activities. While the ACSBrC emissions in rural regions accounted for 63.0-79.5% of the total emission result from uncontrolled fireworks burning. A mass of fireworks burning led to extremely higher ACSBrC and EC emissions on Chinese New Year's eve, as 1444 and 262 times their corresponding yearly average values. Interestingly, significant (p<0.01) correlations between human incomes and pollutant emissions were found, but they were positive (r = 0.94) and negative (r = -0.94) for urban and rural regions, indicating the necessity of regulating human lifestyle and increasing income for urban and rural peoples, respectively. This study provided the first-hand data for identifying the emissions, variation trends and impacting factors of FMS, which is helpful for modeling works on air quality, climate effect, and human health risks at specific periods or regions and for modifying their emission control policies. The data in this work could be found at https://doi.org/10.6084/m9.figshare.19999991.v1 (Cheng et al., 2022). [ABSTRACT FROM AUTHOR]

In 3D building models, a large number of texture maps with different sizes increase the number of model data loading and drawing batches, which greatly reduces the drawing efficiency of the model. Therefore, this paper proposes a texture set mapping method based on vertex importance. Firstly, based on the 2D space boxing algorithm, the texture maps are merged and a series of Mipmap texture maps are generated, and then the vertex curvature, texture variability and location information of each vertex are calculated, normalized, and weighted to get the importance of each vertex, and then finally, different Mipmap-level textures are remapped according to the importance of the vertices. The experiment proves that the algorithm in this paper can reduce the amount of texture data on the one hand, and avoid the rendering pressure brought by the still large amount of data after merging on the other hand, so as to improve the rendering efficiency of the model. [ABSTRACT FROM AUTHOR]

The technological methods for digital documentation and the experimental best practice standards used to document and describe the many artistic objects determine the challenges and the numerous uses of today's cultural heritage.Because of this, it's crucial to preserve heritage by documenting it consciously and using conservation techniques that don't diminish or affect its worth. The study examines the issues we faced in disseminating all of the technical content we have, rather than focusing on the general importance of digital data for the technical staff. To share our heritage, we must make the digital legacy we have built over the last twenty years accessible. Starting from practical examples of dissemination projects in the context of conservation items, the analysis discusses an investigation into the findings and disciplinary culture that have resulted from research conducted by academics in the field of digital heritage on papers, conferences and EU projects produced and conducted between 2018 and 2022. The paper reviews the digital cultural heritage framework outlined by research up to this point.Our findings demonstrate that, despite the fact that the DCH area is constantly changing, we still primarily consider the technical side of digital without considering it as a genuine possibility for openness and sharing. [ABSTRACT FROM AUTHOR]

Humankind is known to be curious and inquisitive. Even if the code were lost, future individuals would be eager to find out what kind of message a printed paper offers. This was already the case when hieroglyphs were deciphered. Printed paper does not need additional equipment to be recognized as a data carrier. Therefore, paper as an analogue data store medium provides an interesting redundancy to deliver important information to future generations. The presentation deals with topics of age resistance of paper and inks as well as recommendations of how to empower printed or written paper to be a long-term data store. Different ageing conditions, different inks, different printing processes, and different fibre sources reveal the opportunities and threats. Data obtained during a 2-year-long project together with BASE (Bundesamt für die Sicherheit der nuklearen Entsorgung – Federal Authority for the Safety of Nuclear Waste Management) on the long-term stability of paper (Langzeitbeständigkeit von Papier – Labest Papier) emphasize the feasibility (Arbeitsstandberichte AP1–AP3 (literature, typical damage, recommendations), unpublished data). The ageing mechanisms of paper are known, as are countermeasures. This leads to standards and recommendations for the use of paper for documents. ISO 9706 (1994) is the main proponent of the direction that longevity can be ensured solely via the composition of the paper and the initial situation at the beginning of ageing. DIN 6738 (2007), on the other hand, takes as its basis the approach of assessing the physical ageing of the paper based on artificial ageing and the strength losses observed in the process. Other standards vary the approaches somewhat or mix them in part, for which the lecture presents a comparison table. The usability of a document depends on two essential factors. Firstly, the residual strength after an ageing process must be high enough for the document to be usable. Secondly, the information written on it must still be legible or at least recognizable (ISO 11798, 2023). However, although certain fibre resources that are recommended by different standards dealing with the long-term storage of documents as well as some fibre sources are denied, we must consider that all fibre sources undergo an ageing process. Different combinations of fibre and ink will lead to different results after natural or artificial ageing. This does not necessarily mean that only the best combination is able to fulfil the requirements related to transport information to an address in the far future. Albeit less strong or with higher optical changes, even by current standards, a rejected fibre source may nevertheless be able to keep the printed information over a long period of time. Paper has been a proven reliable information carrier for approx. 1000 years as the oldest known paper document archived in Palermo is from 1109 CE. It is reasonable to expect a reliability for many more years because the "Ebers" papyrus (worldwide longest, complete, and best-preserved papyrus scroll) stored at the University of Leipzig is already 3500 years old. Compared to modern information storage media such as digital media or microfilm, paper also has the advantage of a long migration time and that no reading or decoding device is necessary. Taking this into account, paper documents are predestined to play a major role in record, knowledge, and memory (RK&M) strategies, as described by Pescatore and Palm (2023). Finally, it remains to say that there can never be an intactness in the sense of being completely untouched. Paper, however, is a material consisting of natural polymers that prove their longevity in the world's oldest tree, a 9500-year-old spruce in Sweden. [ABSTRACT FROM AUTHOR]

This study aims on an exploration of recent applications of drones in the Malaysian context. More specifically, to investigate the drone's application status and application areas. To search the academic literature and identify the included documents, we have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We conducted an overview study of the total number of 54 documents (out of 202) published since 2018, which were extracted from the Scopus database. The results of the bibliometric analysis of the included documents showed that drones in Malaysia are applied in different disciplines, including agriculture, mapping, forestry, river monitoring, transportation, construction, coastal management, flood management, and other applications. Among these, agriculture and mapping are the main application areas. MDPI published the most journal articles, and IGRSM conferences published the highest number of conference papers. Furthermore, scholars from the Universiti Teknologi Malaysia have the highest contribution among others. [ABSTRACT FROM AUTHOR]

This paper explores digital education for pupils in a humanities teaching-learning lab. So far, digital humanities educational approaches have been primarily aimed at students. Teaching-learning labs, in which students design, implement and evaluate teaching offers for pupils, have so far almost exclusively existed in STEM subjects.Based on a classification in European frameworks and a literature analysis on teaching-learning labs of digital history education, the current state of the art on the topic is elucidated. Using concrete practical examples, the first results of involving students in digital history projects will be presented and suitable digital tools for teaching humanities content will be introduced. [ABSTRACT FROM AUTHOR]

The world is experiencing an unprecedented rate of urbanisation, with 70% of its population expected to live in urban cities by 2050 (United Nations, 2018). This is driving demand for smart city development projects, that address city related issues such as traffic congestion, inadequate housing, excessive crime rates, strains on utilities like energy and water, amongst others. The volume and analysis of data associated with these activities, call for a mechanism for managing data in smart cities.This paper is aimed at developing a data governance framework for managing data generated by smart cities, and improving the way they operate as well as the quality of life of their citizens. The paper will highlight the characteristics of smart cities, the importance for cities to become smart, how the smartness of cities can be measured, and which cities around the world are considered smart. It will also discuss why data is important for the success of smart cities, the types and sources of data typically present in smart city settings and the data management challenges that plague smart cities globally.A sequential mixed methods research approach incorporating qualitative and quantitative data collection and analysis methods was employed, following a multi-step approach starting with analysis of existing frameworks and interviews with subject matter experts. This was followed by a survey of industry professionals, for insights on key framework components, and the use of case studies and questionnaires for framework evaluation.Throughout the research process, the focus of smart cities on people rather than technology was prominent. As such, the most important theme of the framework was identified as "People", particularly in their roles as data stewards and data owners. The remaining five themes of the recommended data governance framework are, Process, Technology, Data, Protection and Valuable Insights. [ABSTRACT FROM AUTHOR]

Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration time spans of the order of decades and, thus, cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. "Restoration" encompasses applications to pre-restoration and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. The aim of this scoping review is to identify, describe, and categorize current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of ecohydrological and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the entire Web of Science database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorized according to this review's research interests in seven distinct categories aggregating the papers' research themes and model outputs. Restoration site context was added by identifying 229 unique study site locations from the full database, which were catalogued and analysed against raster data for the Köppen–Geiger climate classification scheme. A majority of northern peatland sites were in temperate oceanic zones or humid continental zones that experienced snow. Over one in five models from the full database of papers were unnamed and likely intended for single use. Key themes emerging from topics covered by papers in the database included the following: modelling restoration development from a bog growth perspective, the prioritization of modelling greenhouse gas (GHG) emissions dynamics as a part of policymaking, the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems, and the increased prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Models are presented according to their application to peatlands or broader ecosystem and organized from most to least complex. This review provides valuable context for the application of ecohydrological models in determining strategies for peatland restoration and evaluating post-intervention development over time. [ABSTRACT FROM AUTHOR]

Understanding carbon sources and sinks across the Earth's surface is fundamental in climate science and policy; thus, these topics have been extensively studied but have yet to be fully resolved and are associated with massive debate regarding the sign and magnitude of the carbon budget from global to regional scales. Developing new models and estimates based on state-of-the-art algorithms and data constraints can provide valuable knowledge and contribute to a final ensemble model in which various optimal carbon budget estimates are integrated, such as the annual global carbon budget paper. Here, we develop a new atmospheric inversion system based on the 4D local ensemble transform Kalman filter (4D-LETKF) coupled with the GEOS-Chem global transport model to infer surface-to-atmosphere net carbon fluxes from Orbiting Carbon Observatory-2 (OCO-2) V10r XCO 2 retrievals. The 4D-LETKF algorithm is adapted to an OCO-2-based global carbon inversion system for the first time in this work. On average, the mean annual terrestrial and oceanic fluxes between 2015 and 2020 are estimated as - 2.02 and - 2.34 GtC yr -1 , respectively, compensating for 21 % and 24 %, respectively, of global fossil carbon dioxide (CO 2) emissions (9.80 GtC yr -1). Our inversion results agree with the CO 2 atmospheric growth rates reported by the National Oceanic and Atmospheric Administration (NOAA) and reduce the modeled CO 2 concentration biases relative to the prior fluxes against surface and aircraft measurements. Our inversion-based carbon fluxes are broadly consistent with those provided by other global atmospheric inversion models, although discrepancies still occur in the land–ocean flux partitioning schemes and seasonal flux amplitudes over boreal and tropical regions, possibly due to the sparse observational constraints of the OCO-2 satellite and the divergent prior fluxes used in different inversion models. Four sensitivity experiments are performed herein to vary the prior fluxes and uncertainties in our inversion system, suggesting that regions that lack OCO-2 coverage are sensitive to the priors, especially over the tropics and high latitudes. In the further development of our inversion system, we will optimize the data-assimilation configuration to fully utilize current observations and increase the spatial and seasonal representativeness of the prior fluxes over regions that lack observations. [ABSTRACT FROM AUTHOR]

Which published papers have transformed our understanding of the chemical processes in the troposphere and shaped the field of atmospheric chemistry? By way of expert solicitation and interactive peer review, this paper explores the influence of the ideas in peer-reviewed articles based on input from our community of atmospheric scientists. We explore how these papers have shaped the development of the field of atmospheric chemistry and identify the major landmarks in the field of atmospheric chemistry through the lens of those papers' impact on science, legislation and environmental events. We also explore the ways in which one can identify the papers that have most impacted the field and discuss the advantages and disadvantages of the various approaches. Our work highlights the difficulty of creating a simple list, and we explore the reasons for this difficulty. The paper also provides a history of the development of our understanding of tropospheric chemistry and points some ways for the future. [ABSTRACT FROM AUTHOR]

Floods kill numerous people every year in both developed and developing countries. The transfer of research findings from the academic community to practitioners, policy-makers and citizens may reduce the impact of floods on mortality. This systematic review analyzes 44 scientific articles extracted from WOS and SCOPUS databases written in English, published between 2010 and 2020, and focuses on flood fatalities. The first main finding of this review is the classification of drivers of flood mortality into two groups: the first group relates to the environment, and the second group relates to the fatalities. The second main finding is the identification of strategies to practically cope with the identified drivers of flood fatalities. The main shortcomings of the review concern (a) the unavailability of papers based on flood fatality occurrence in developing countries and (b) the absence of data focusing on people who have survived floods. This review amplifies useful findings, best practices and lessons learned that can be useful for administrators, risk managers, and teachers of primary and secondary schools to mitigate the impact of future floods on human life. [ABSTRACT FROM AUTHOR]

In December 2020 the ICA joined the Open Geospatial Consortium (OGC) as an associate member. As a community composed of businesses, government agencies, universities, and research organizations that work with geospatial data, the OCG is a resource to which software developers who work with geospatial data turn for guidance and advice. As such, it provides the ICA with an opportunity to reach a group of people who might not have had prior experience or background in cartography. [ABSTRACT FROM AUTHOR]

To mark the 20th anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences, we highlight 11 key publications covering major subject areas of NHESS that stood out within the past 20 years. The papers cover all the topics contemplated in the European Geosciences Union (EGU) Division on Natural Hazards including dissemination, education, outreach and teaching. The selected articles thus represent excellent scientific contributions in the major areas of natural hazards and risks and helped NHESS to become an exceptionally strong journal representing interdisciplinary areas of natural hazards and risks. At its 20th anniversary, we are proud that NHESS is not only used by scientists to disseminate research results and novel ideas but also by practitioners and decision-makers to present effective solutions and strategies for sustainable disaster risk reduction. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional neural networks (CNNs) algorithms have demonstrated outstanding performance in a wide range of remote sensing applications, including image classification, image detection, and image segmentation. Urban development, as defined by urban expansion, mapping impervious surfaces, and built-up areas, is one of these fascinating issues. The goal of this research is to explore at and summarize the deep learning approaches used in urbanization. In addition, several of these methods are highlighted in order to provide a comprehensive overview and comprehension of them, as well as their pros and downsides. [ABSTRACT FROM AUTHOR]

Keyframes extraction is required and effective for the 3D reconstruction of objects from a thermal video sequence to increase geometric accuracy, reduce the volume of aerial triangulation calculations, and generate the dense point cloud. The primary goal and focus of this paper are to assess the effect of keyframes extraction from the thermal infrared video sequence on the geometric accuracy of the dense point cloud generated. The method of keyframes extraction of thermal infrared video presented in this paper consists of three basic steps. (A) The ability to identify and remove blur frames from non-blur frames in a sequence of recorded frames. (B) The ability to apply the standard baseline condition between sequence frames to establish the overlap condition and prevent the creation of degeneracy conditions. (C) Evaluating degeneracy conditions and keyframes extraction using Geometric Robust Information Criteria (GRIC). The performance evaluation criteria for keyframes extraction in the generation of the thermal infrared dense point cloud in this paper are to assess the increase in density of the generated three-dimensional point cloud and reduce reprojection error. Based on the results and assessments presented in this paper, using keyframes increases the density of the thermal infrared dense point cloud by about 0.03% to 0.10% of points per square meter. It reduces the reprojection error by about 0.005% of pixels (2 times). [ABSTRACT FROM AUTHOR]

Nowadays color in scientific visualizations is standard and extensively used to group, highlight or delineate different parts of data in visualizations. The rainbow color map (also known as jet color map) is famous for its appealing use of the full visual spectrum with impressive changes in chroma and luminance. Besides attracting attention, science has for decades criticized the rainbow color map for its non-linear and erratic change of hue and luminance along the data variation. The missed uniformity causes a misrepresentation of data values and flaws in science communication. The rainbow color map is scientifically incorrect and hardly decodable for a considerable number of people due to color vision deficiency (CVD) or other vision impairments. Here we aim to raise awareness of how widely used the rainbow color map still is in hydrology. To this end, we perform a paper survey scanning for color issues in around 1000 scientific publications in three different journals including papers published between 2005 and 2020. In this survey, depending on the journal, 16 %–24 % of the publications have a rainbow color map and around the same ratio of papers (18 %–29 %) uses red–green elements often in a way that color is the only possibility to decode the visualized groups of data. Given these shares, there is a 99.6 % chance to pick at least one visual problematic publication in 10 randomly chosen papers from our survey. To overcome the use of the rainbow color maps in science, we propose some tools and techniques focusing on improvement of typical visualization types in hydrological science. We give guidance on how to avoid, improve and trust color in a proper and scientific way. Finally, we outline an approach how the rainbow color map flaws should be communicated across different status groups in science. [ABSTRACT FROM AUTHOR]

Rainbow colour maps are known to be problematic yet remain widely used in scientific communication. This study extends work by Stoelzle and Stein (2021) to investigate the extent of their use in geoscience publications. It is found that over half (55 %) of all papers surveyed from six geoscience journals from the years 2005, 2010, 2015 and 2020 (n=2638) contained at least one visualisation that uses rainbow or red–green colour schemes and are therefore potentially misleading and colour-inaccessible. Recent changes to the submission guidelines for all European Geosciences Union (EGU) journals would seem to place greater responsibility in the future with editors and reviewers to identify and correct colour issues as part of the review process. [ABSTRACT FROM AUTHOR]

Structural connectivity is an important factor in preserving coral diversity. It maintains the stability and adaptability of coral reef ecosystems by facilitating ecological flow, species migration, and gene exchange between coral communities. However, there has always been a lack of consistent solutions for accurate structural connectivity describing and quantifying, which has hindered the understanding of the complex ecological processes in coral reefs. Based on this, this paper proposes a framework that uses advanced remote sensing and deep learning technologies to assess coral structural connectivity. Specifically, accurate coral patches are firstly identified through image segmentation techniques. And the structural connectivity is quantified by assessing the connectivity patterns between and within these coral patches. Furthermore, Tetiaroa Island in the South Pacific is used as a case study to validate the effectiveness and accuracy of the framework in assessing coral structural connectivity. The experimental results demonstrate that the framework proposed in this paper provides a powerful tool for understanding the internal ecological processes and external spatial patterns of coral reef ecosystems, thereby promoting scientific understanding and effective management of coral reef conservation. [ABSTRACT FROM AUTHOR]

Decades of decline, disinvestment, and racism have left Detroit with an abundance of abandoned buildings, ruins, vacant lots, and illicit trash dumps. Though these structures and materials might have forfeited their previous purposes, they can act as catalysts, substances, and co-creators of artworks. The paper is thus interested in examining the intricate interplay between art, space, and materiality in Detroit further. Drawing from the practices of local artists Olayami Dabls and Scott Hocking, the paper adopts a new materialist framework to investigate the dynamic agency of matter in the artistic process. By considering materials as active participants in the production of art and space, the paper seeks to add to the emerging interest in the emancipation and meaning making of material in art as well as cultural geography's engagements with new materialism. [ABSTRACT FROM AUTHOR]

The preface to the Proceedings of the 8th Smart Data Smart Cities (SDSC) conference provides an overview of the concept of a Smart City, which involves the use of digital layers for city governance, citizen services, and sustainable development. Smart Cities utilize intelligent technology to enhance urban life by integrating data from various sources such as sensors, demographics, mapping, and building information models. The conference focuses on the use of spatial information and communication technologies in Smart Cities, covering topics such as 3D modeling, citizen engagement, transportation, urban decision-making, and smart urban mobility. The volume consists of 31 research papers selected through blind review, addressing different aspects of Smart Cities. [Extracted from the article]

Due to the increase in the frequency and intensity of natural disasters such as heavy rainfall events, the evaluation of the vulnerability of the built environment is becoming increasingly important. Evaluation techniques for each separate building often require detailed geometric models of the supporting structures and time-consuming simulations. One possibility to overcome this problem is to categorize the buildings in a first step and use representative building models for each category. This paper presents a semi-automated approach for categorizing buildings and creating parametric models for the respective building categories. Using these models, the buildings of a category can be collectively examined for their vulnerability to various impacts. First, this paper introduces open geodata that can be used for this process. For the categorization of the buildings, the collected data is further processed to extract additional information such as building age classes or floor plan geometries of the buildings. This results in a data set, with the help of which the buildings can be categorized. However, information about the load-bearing structure is often missing in the different data sources. By including information on typical construction methods that are associated with the previously determined characteristics (age, floor plan geometry, usage), representative models can be created for individual building categories. In this study, the approach was tested in a selected reference area in Berlin. The results indicate that the presented approach is a promising first step towards deriving geometrical models from open geodata that can be used to evaluate the vulnerability of buildings. [ABSTRACT FROM AUTHOR]

The technology for 3D reconstruction of tree models based on point clouds has been extensively researched, necessitating effective datasets for the study of branch and leaf separation, skeleton point extraction, and tree parameter extraction methods. However, existing datasets for 3D tree models face several challenges, including insufficient data volume for deep learning network training, low accuracy of model ground truth impeding effective method precision evaluation, and a lack of dataset richness to satisfy the needs of multi-type method assessments. In response to these challenges, This paper introduces, for the first time, a fully automated method for generating structured three-dimensional synthetic tree models, and constructs a large-scale 3D synthetic tree dataset enriched with comprehensive structural information. This method facilitates automated computation across several processes, including the mass generation of simulated trees, separation of branches and leaves, noise generation, extraction of skeleton points, and volume calculation. To validate the usability of this dataset across various applications, this paper employs state-of-the-art (SoTA) algorithms to verify the accuracy of methods in 3D tree model reconstruction and carbon stock calculation, thereby thoroughly demonstrating the dataset's effectiveness. [ABSTRACT FROM AUTHOR]

With the wide use of UAVs in urban scenes making image acquisition easier, processing large-scale 3D reconstruction problems have become a basic need of the Structure from Motion (SfM) community. Because of high memory consumption and computation complexity, bundle adjustment (BA) is a significant bottleneck in large-scale 3D reconstruction problems. Distributed strategy is a current research direction. Recent research cut the view graph to optimize sub-scenes in small size and average common cameras and/or 3D points to align them into one in the end. Yet, those methods tried to simply average cameras and/or 3D points, which lack mathematical rigor and tend to get a sub-optimal result. This paper first cuts the view graph and expands it. Then we use the strategy of motion averaging instead of simply averaging points to obtain a robust result. Finally, refined results are achieved by optimizing reprojection errors in the overlapping area. We conduct experiments on UAV data, check the validity of graph cut and expansion, and analyze whether graph size influences the precision computation time. Visualization results show that our method preserves scenes well; precision and visualization of un-expanded scenes prove the necessity of graph expansion; statistical results also indicate the negative infect of large sub-scene size. [ABSTRACT FROM AUTHOR]

Floods kill several people every year in both developed and developing countries. The transfer of research findings from the academic community to practitioners, policy-makers and citizens may reduce the impact of floods on mortality. This systematic review analyzes 44 scientific articles extracted from WOS and SCOPUS databases written in English, published between 2010 and 2020, and focuses on flood fatalities. The first main finding of this review is the classification of drivers of flood mortality into two groups: the first group relates to the environment and the second group relates to the victims. The second main finding is the identification of strategies to practically cope with the identified drivers of flood fatalities. The main lacks of the review concern: a) the unavailability of papers based on flood fatality occurrence in developing countries and b) the absence of data focusing on people who have survived floods. This review amplifies useful findings, best practices, and lessons learned that can be useful for administrators, risk managers, and teachers of primary and secondary schools to mitigate the impact of future floods on human life. [ABSTRACT FROM AUTHOR]

This paper discusses the representation of the scale dimension using tGAP/SSC-DHE, emphasizing its role in creating vario-scale representations. It outlines the process of integrating the scale dimension as the third dimension in a 2D map before transitioning to 3D or 4D maps. DHE is introduced as an effective data structure for this approach, maintaining crucial connections across different levels of detail and offering enhanced topology. Its strength lies in the ability to modify specific parts of the model while retaining a clear structure, thanks to well-defined operators such as Euler operators. This method provides a versatile framework for representing complex spatial data, allowing for detailed analysis while preserving overall coherence and it updates the shape and structure of the model at the same time through local modifications. Through practical implementation, it demonstrates the potential for improved visualization and understanding of multi-dimensional spatial relationships. Also, the DHE data structure supports slicing by either a horizontal plane to produce a map at a specific scale (depending on the height of the horizontal plane) and tilted planes to produce perspective views with mixed scales. [ABSTRACT FROM AUTHOR]

The potential of Building Information Models (BIM) to establish rights and responsibilities for multi-level building complexes in cadastral registrations has been explored in many previous researches. However, the implementation of BIM-based cadastral registrations in practice remains limited due to the complex interplay between technical potentials and legal implications as well as uncertainty about the additional complexity of a BIM model and the extra work that will be required to generate the BIM Legal model. In collaboration with Netherlands Kadaster, we have investigated the data requirements for a BIM Legal model that will support the 3D cadastral registration of apartment complexes and that aligns with BIM creation processes in practice. The BIM Legal model aims to visualise the spatial aspects of apartment complexes and to align the cadastral registration to the 3D data that is generated in the design and construction phase of new buildings. The BIM Legal file will be submitted when an apartment complex is being registered and is based on IFC, the open standard established by BuildingSMART International and used in the BIM domain. To make the implementation of BIM Legal feasible legally, technically, and economically, we apply a 3-phase approach. This paper presents the BIM Legal model as defined in Phase 1, which enables to generate a BIM Legal model compliant with current legislation frameworks with no/minimal manual interaction from BIM models as commonly generated in design processes. The paper describes the 3-phase approach, the context of Phase 1, the data requirement analyses, the defined BIM Legal data model, as well the questions that emerged during the specification process that need to be answered to further improve the implementation of Phase 1 and to further develop BIM Legal for Phases 2 and 3. [ABSTRACT FROM AUTHOR]

Over the last decades, the concept of map visual complexity has been extensively investigated in cartographic research. Existing approaches provide specific measures and metrics which can be utilized towards the objective examination of map effectiveness, e.g. Monmonier (1974), MacEachrean (1982), Fairbairn (2006), and Rautenbach et al. (2015). Moreover, recent studies analyze behavioral data, e.g. Cybulski (2020), Tzelepis et al. (2020), using cartographic products distributed in the internet (Schnur et al., 2018). However, map visual complexity could be affected by several factors. Among them, map scale seems to play a significant role, since it is connected to the accuracy and the level of information delivered by a cartographic product (Dumont et al., 2020). In this work, the concept of map visual complexity is connected to visual heterogeneity. A new method for quantifying visual heterogeneity of paper maps is proposed. The method is based on diffuse reflectance spectroscopy and on a random segmentation and sampling in maps with different scale levels. It is shown that by using certain segmentation size, an indicator based on Shannon entropy of the samples can be efficient in characterizing the visual heterogeneity and the scale of the maps. A low-cost optical spectrometer is utilized in order to record the spectra of 40 random segments of the maps. Reflectance spectroscopy aims at measuring the relative level of reflected light as it is compared with a reference (white surface) to record a percentage value at each wavelength and for each segment of the map. A bifurcated optical fiber has been used to deliver the reference light to the map surface (Halogen light source), while the reflected light is guided to the spectrometer (Figure 1). Segment size is defined by moving the fiber closer to the map surface and it is expected to significantly affect the estimation of the indicator. In the present experiments, the distance of the fiber from the map surface is such so that the diameter of the randomly selected segments is approximately 3 mm. The spectrometer spectral resolution is approximately 1.5 nm and, consequently, more than 240 spectral channels are recorded for each sample, since only the visible region of the spectrum is required (380 nm - 750 nm). The proposed heterogeneity indicator is the Shannon entropy, as is calculated by the probabilities to record similar reflection spectral values (relative reflected light) in the space defined by the 40 samples of each map. Since the spectral values are continuous variables, they have to be properly quantized (discretization process), before the probabilities and the entropy are calculated. In addition, Principal Component Analysis (PCA) can be also utilized in order to reduce the spectral data dimensions (channels) and to enable faster calculations. As a means to evaluate the proposed indicator, 60 different paper maps are used. Maps' production is based on the use of the standard layer of OpenStreetMap (OSM) as it is provided by the Web Map Service (WMS) of terrestris GmbH & Co. KG. OSM cartographic backgrounds are utilized in this work because they present adequately high information (content) density and diversity across different scale -- or zoom -- levels compared to other online map services, e.g. Google Maps, Here WeGo, etc. (Skopeliti and Stamou, 2019). More specifically, for the needs of this research study, twenty regions in Greece and three different map scale levels (1:k, 1:4k, & 1:40k) are examined. The produced maps are printed using the same paper quality. Box plots are applied to show the indicator with respect to the scale. It is shown that the scale of the map strongly affects the information content and the visual heterogeneity of the map. [ABSTRACT FROM AUTHOR]

In the Swiss National Forest Inventory (NFI), information about the current condition and the changes of the Swiss forest is regularly collected (Brändli et al. (2020)). Data collection consists of on-the-ground surveys, remote-sensing analyses and an interview survey with the district foresters. The interview survey takes place every nine years and includes an update of the current situation of the forest road network, consisting of roads that can be used for wood extraction and transport. Information on the location of roads and their characteristics, such as the suitability for different vehicle classes, is important to assess the accessibility of the forest and to estimate costs for wood harvesting and transport. In all forest road surveys conducted up to now the forest road network was updated by drawing lines and writing predefined codes on paper maps by hand. In the third and fourth survey, information on road properties was later combined with existing digital road geometries, producing a spatial digital road dataset which could be analysed in a GIS (Fraefel and Fischer (2019)). However, the process to transfer the mapped properties from the paper maps to the digital road geometries (scanning, georeferencing, attribute entry) was very time-consuming. In addition, the growing number of road properties in the survey made it difficult to clearly display all the existing information on a fixed-scale paper map. For the current (fifth) NFI forest road survey, we have therefore developed a procedure that allows the interviewer to collect information about road network changes directly in a GIS, together with the forester, at the forester's office. This requires a user interface that plays together with a touch interface to make data entry similar to pen-and-paper mapping, easy to use even for non-GIS-specialists. At the same time, the system should be sufficiently mobile to be transported by car and offer a large screen for the clear representation of the previously collected information and easy map navigation within the survey area. Various tests and comparisons included different ways of manipulating existing features and drawing new ones; different touch interfaces; the display of information in the map; and the set-up of the user interface with, for example, custom buttons. The resulting tool has a number of benefits, compared to a survey on paper maps: no scanning and georeferencing is required (cost reduction); faster attribute assignment (from digital lines to existing road geometries); value domains and consistency constraints for data entry; the possibility to alert the interviewer to specific locations by turning layers on and off; and faster data availability through online transmission, if required. A drawback of the method is the need for additional hardware (screens and pen tablets) and some training with regard to the hardware and GIS software. [ABSTRACT FROM AUTHOR]

The preparation of laser scanning missions is important for efficiency and data quality. Furthermore, it is a prerequisite for automated data acquisition, which has numerous applications in the built environment, including autonomous inspections and monitoring of construction progress and quality criteria. The scene and potential scanning locations can be discretized to facilitate the analysis of visibility and quality aspects. The remaining mathematical problem to generate an economic scan strategy is the Viewpoint Planning Problem (VPP), which asks for a minimum number of scanning locations within the given scene to cover the scene under pre-defined requirements. Solutions for this problem are most commonly found using heuristics. While these efficient methods scale well, they cannot generally return globally optimal solutions. This paper investigates the VPP based on a problem description that considers quality-constrained visibility in 3D scenes and suitable overlaps between individual viewpoints for targetless registration of acquired point clouds. The methodology includes the introduction of a preprocessing method designed to simplify the input data without losing information about the problem. The paper details various solution methods for the VPP, encompassing conventional heuristics and a mixed-integer linear programming formulation, which is solved using Benders decomposition. Experiments are carried out on two case study datasets, varying in specifications and sizes, to evaluate these methods. The results show the actual quality of the obtained solutions and their deviation from optimality (in terms of the estimated optimality gap) for instances where exact solutions can not be achieved. [ABSTRACT FROM AUTHOR]

The ISPRS Technical Commission I Midterm Symposium on "Intelligent Sensing and Remote Sensing Application" was held in Changsha, China, during May 13–17, 2024, aiming to provide a platform to share the latest researches, advanced technologies and application experience, to discuss the future development and to seek international cooperation in various forms. The Symposium has received 229 full paper and abstract s, among them 45 double-blind peer-reviewed full papers were published in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information, and 165 papers accepted through abstract review were published in the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. These papers are mostly dedicated to topics of the 8 TC I Working Groups, 3 Inter-commission Working Groups, including Satellite Missions and Constellations for Remote Sensing, Mobile Mapping Technology, Multispectral, Hyperspectral and Thermal Sensors, LiDAR, Laser Altimetry and Sensor Integration, Microwave and InSAR Technology for Earth Observation, Orientation, Calibration and Validation of Sensors, Data Quality and Benchmark of Sensors, Multi-sensor Modelling and Cross-modality Fusion, Robotics for Mapping and Machine Intelligence, Autonomous Sensing Systems and their Applications, Digital Construction: Reality Capture, Automated Inspection and Integration to BIM, Point Cloud Generation and Processing, Artificial Intelligence Technology Related to Sensor Systems, Multi-sensor Remote Sensing Applications.. These papers presented the latest trends of sensor systems. The full papers and abstracts were reviewed by the members of the Symposium Scientific Committee comprised of Working Group officers and invited experts. We would like to take this opportunity to express our great gratitude to the Scientific Committee, Local Organizing Committee, Sponsors, Exhibitors and all those who have contributed to this successful Symposium. We also want to express our thanks to the authors for their excellent papers and presentations. Tang Xinming, Antonio Maria Garcia Tommaselli, Zhang Tao, Xie JunfengISPRS Technical Commission I on Sensor SystemsMay 2024, Changsha, China [ABSTRACT FROM AUTHOR]

The paper deals with the wind speed of strong and extreme wind events in Poland and their descriptions of their effects. Two recent estimation developed by the Institute of Meteorology and Water Management in Warsaw [4] and by Lorenc [3] are presented. Their strong and weak points are briefly described. The 37 annual maximum gusts of wind speeds measured at all meteorological stations between 1971 and 2007 are analysed with extremal probability paper. Based on the measured and estimated wind speeds (taken from existing literature), the authors suggest new estimations for strong and extreme wind that may occur in Poland. In the near future, Poland is going to construct some important structures, such as a central air terminal and some nuclear power plants, so knowledge about strong and extreme winds in our country is very important for engineers who will design these types of structures. In this case, the information given in Section 6 of the ISO 1382 standard for identifying hazards, estimating, evaluating, and treating risk may be applied. [ABSTRACT FROM AUTHOR]

With the increase of remote sensing image acquisition methods and the number of remote sensing image data, the traditional manual annotation and recognition methods can no longer meet the needs of the present production life. This study explores the use of deep learning techniques to improve the efficiency and accuracy of target detection in remote sensing satellite images, especially for small targets. Traditional target detection methods often face challenges in recognition accuracy and processing speed due to the specificity and complexity of satellite images, such as large size, variable lighting conditions and complex background. Therefore, this paper adopts the YOLOv5 model and introduces the CBAM (Convolutional Block Attention Module) attention mechanism, which significantly improves the detection of small and dense targets. Experimental validation, using the improved YOLOv5 model on the VisDrone2021 dataset, demonstrates that the model improves the mean average percision (mAP) by 1.9% while maintaining realtime performance. This paper provides new ideas for remote sensing image processing, especially for applications in the fields of urban planning and automatic driving, etc. Despite the progress made in this study, the detection of small targets in remote sensing images, the limited classification accuracy and the detection of dynamic targets still need further research. [ABSTRACT FROM AUTHOR]

As urbanization accelerates, traffic congestion and frequent accidents have become prominent issues, prompting the development of intelligent transportation systems. This paper focuses on the research of vehicle and pedestrian detection algorithms to improve detection accuracy in complex traffic environments. Considering the limitations of traditional object detection algorithms in complex situations, this study adopts the deep learning-based YOLOv8 algorithm and introduces the Coordinate Attention (CA) module to enhance the model's feature extraction and localization capabilities. Experimental results show that the improved YOLOv8 network achieves a 1.1% increase in detection accuracy while maintaining its original speed. Furthermore, this paper constructs a vehicle and pedestrian dataset suitable for Chinese traffic scenes, providing an effective solution for autonomous driving assistance systems. Overall, this study holds significant reference value for vehicle and pedestrian detection in the field of intelligent transportation. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges of implementing the CityGML standard within database environments for managing urban digital twins (UDT), with a particular focus on addressing big geodata issues. CityGML stands as a critical standard for representing and exchanging 3D urban models and thematic data, essential for effective urban planning and infrastructure management. However, integrating CityGML into databases poses challenges due to the dual requirements of geospatial and semi-structured data. While the former imposes a certain rigor in its formalism, the second benefits from more flexibility. Through a comprehensive review and benchmarking of existing database implementations, including both new distributions and those often use, 3DCityDB, CJDB, Measur3D, and Cerbere, this paper proposes a novel approach towards a multi-database CityGML environment tailored to the specific needs of UDT. The proposed solution leverages the strengths of both relational and NoSQL databases, offering a flexible and scalable architecture while ensuring data consistency, geospatial capabilities and compliance with the CityGML schema. The research hypothesis suggests integrating Cerbere, a middleware for CityGML schema compliance and transaction validation, with 3DCityDB and Measur3D (MongoDB). This approach aims to demonstrate the feasibility of managing advanced 3D data operations based on the CityGML model (3DCityDB) and scalability for big data like IoT (Measur3D) within a multi-database environment. The paper contributes to the advancement of UDT by providing a comprehensive solution for managing diverse data types, facilitating more effective urban planning, infrastructure management, and sustainable development initiatives in the context of smart cities. [ABSTRACT FROM AUTHOR]

One of the major challenges that existing spatial data is facing is the fragmentation of its representation of indoor and outdoor space. As studies in the use of omnidirectional images in representing space and providing Location-based Services (LBS) has been increasing, the representation of the different scales of space, both in indoors and outdoors, has yet to be addressed. This study aims to develop a data model for generating a multi-scale image-based representation of space using omnidirectional images based spatial relationships. This paper identifies the different scales of space that are represented in spatial data and extends previous approaches of using omnidirectional images in providing indoor LBS towards representing the other scales of space, particularly in outdoor space. Using a sample data, we present an experimental implementation to demonstrate the potential of the proposed data model. Results show that apart from the realistic visualization that image data provides, basic spatial functions can be performed on the image data constructed based on the proposed data model. [ABSTRACT FROM AUTHOR]

Figure-ground maps play a key role in many disciplines where urban planning or analysis is involved. In this context, the automatic generation of such maps with respect to certain requirements and constraints is an important task. This paper presents a first step towards a deep automatic generation of figure-ground maps where the built density of the generated scenes is controlled and taken into account. This is preformed building upon a Geographic Data Translation model which has been applied to generate less available geospatial features, e.g. building footprints, from more widely available geospatial data, e.g. street network data, using conditional Generative Adversarial Networks. A novel processing approach is introduced to incorporate the population density and the built density accordingly. Furthermore, the impact of both the level of detail of the street network, i.e. its sparsity or density, and the spatial resolution of the training data on the generated figure-ground maps has been investigated. The generated maps and the qualitative results reveal an obvious impact of these parameters on the layout of built and unbuilt areas. Our approach paves the way for the expansion of existing districts by figure-ground maps of future neighbourhoods considering factors such as density and further parameters which will be subject of future work. [ABSTRACT FROM AUTHOR]

Numerous existing buildings in Germany lack corresponding BIM models. Advanced analysis and visualization purposes, including indoor navigation, energy demand simulations, and building materials recycling on these buildings presents significant challenges, as only Level of Detail 2 (LoD2) models provided by local housing authorities, along with architectural drawings and textual descriptions of the internal structures, are available. This paper proposes a semi-manual workflow, LoD2BIM, to facilitate the efficient reconstruction and conversion of existing building models, particularly transforming Germany's official 3D Building Models in LoD2 into comprehensive, information-rich IFC models using open-source software. Through the integration of BaseX and CesiumJS, the visualization, querying and extracting of one LoD2 model from CityGML file become easy and feasible. Central to the LoD2BIM workflow is Blender, it acts as the primary platform for model editing, reconstruction and conversion. Users can expediently rebuild and refine LoD2 models utilizing Blender's visual interface and the tool floorplan to blender3d, based on floorplans or detailed written descriptions. New Blender add-ons are developed to streamline the model import and conversion process. The BlenderBIM add-on is also modified to facilitate the automatic batch assignment of mesh as IFC classes. This workflow not only showcases the reconstruction and subsequent conversion of CityGML models into IFC models but also illustrates the potential to act as an alternative to commercial software in Building Information Modeling (BIM) editing. [ABSTRACT FROM AUTHOR]

Effective visualization of spatial data, especially in the realm of 3D cadastral visualization, relies on the utilization of optimal interaction techniques and user interfaces for navigating complex datasets and understanding property delineations. This paper synthesizes findings from diverse studies investigating the efficacy of interaction modalities and user interfaces in 3D visualization across various domains. Drawing parallels to the broader field of 3D visualization, particularly in interaction tasks and user interface paradigms, this paper examines the potential for advancing 3D cadastral visualization systems. The study identifies fundamental interaction tasks crucial for effective 3D cadastral visualization, including object manipulation, widget manipulation, and data selection and annotation. It evaluates a range of user interfaces, from traditional input methods to emerging technologies such as gesture-based interfaces and virtual reality (VR) headsets, highlighting their respective strengths and limitations.Embracing insights from comparative analyses of immersive and non-immersive scenarios, this paper reveals significant insights into the effectiveness of immersive environments, such as virtual reality and augmented reality, in enhancing user experience and task performance for 3D cadastral visualization. Additionally, it aims to address key challenges associated with visualizing 3D cadastral data in immersive environments by proposing a comprehensive framework for evaluating the effectiveness and utility of immersive visualization for 3D cadastral purposes. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for filling holes in Level of Detail 2 (LoD2) building mesh models, addressing the challenges posed by geometric inaccuracies and topological errors. Unlike traditional methods that often alter the original geometric structure or impose stringent input requirements, our approach preserves the integrity of the original model while effectively managing a range of topological errors. The algorithm operates in three distinct phases: (1) pre-processing, which addresses topological errors and identifies pseudo-holes; (2) detecting and extracting complete border rings of holes; and (3) remeshing, aimed at reconstructing the complete geometric surface. Our method demonstrates superior performance compared to related work in filling holes in building mesh models, achieving both uniform local geometry around the holes and structural completeness. Comparative experiments with established methods demonstrate our algorithm's effectiveness in delivering more complete and geometrically consistent hole-filling results, albeit with a slight trade-off in efficiency. The paper also identifies challenges in handling certain complex scenarios and outlines future directions for research, including the pursuit of a comprehensive repair goal for LoD2 models to achieve watertight 2-manifold models with correctly oriented normals. Our source code is available at https://github.com/tudelft3d/Automatic-Repair-of-LoD2-Building-Models.git. [ABSTRACT FROM AUTHOR]

Autonomous vehicles must navigate independently in an outdoor environment using features or objects. However, some objects may be more or less suitable for localization due to their attributes. Therefore, this work investigates the suitability of landmarks for camera- and object-based outdoor localization methods. First, object attributes are methodically derived from the requirements of object-based localization. The physical representation on the camera image plane, probability of occurrence, and persistence were identified as influencing the object localization suitability. The influence of the object's camera image plane representation regarding object recognition algorithms is not considered or discussed, but advice on the minimum object pixel size is provided. The first milestone was the creation of an equation for object localization suitability calculation by normalizing and multiplying the identified attributes. Simultaneously, potential objects from the outdoor environment were identified, resulting in a structured object catalog. The results of the equation and catalog are a ranked according to the object localization suitability in a comparison table. Our comparison demonstrates that objects such as buildings or trees are more suitable than street lane markings for self-localization. However, most current datasets do not include the proposed instantiated objects. The paper addresses this issue, assists in the object selection for outdoor localization methods and provides input for the creation of future-oriented datasets and autonomous driving maps. [ABSTRACT FROM AUTHOR]

This paper introduces Property Valuation Application Domain Extension (ADE) within CityGML 3.0, aiming to integrate relevant indoor and outdoor 3D variables (cost estimation, view quality, etc.) for accurate property valuation. Current models lack the necessary features for this specific application. Leveraging IFC data for indoor elements, this ADE extends CityGML, addressing the existing gap. This paper identifies and categorizes data requirements, leading to the conceptualization and development of the model. By enriching CityGML 3.0 with IFC data, the approach introduces new features like the "Property Unit" to ensure adaptability across diverse valuation scenarios. Despite encountering data integrability challenges, we here commit to refining the model and overcoming these obstacles. A preliminary implementation using CityJSON demonstrates successful integration and paves the way for future implementation. These include developing an API platform and establishing an official repository to facilitate practical usability and scalability. This research significantly contributes to advancing property valuation processes by providing accurate valuations for stakeholders and promoting the use of 3D urban data in domain-specific extensions. [ABSTRACT FROM AUTHOR]

Rapid mapping demands efficient methods for a fast extraction of information from satellite data while minimizing data requirements. This paper explores the potential of deep learning for the generation of high-resolution urban elevation data from Synthetic Aperture Radar (SAR) imagery. In order to mitigate occlusion effects caused by the side-looking nature of SAR remote sensing, two SAR images from opposing aspects are leveraged and processed in an end-to-end deep neural network. The presented approach is the first of its kind to implicitly handle the transition from the SAR-specific slant range geometry to a ground-based mapping geometry within the model architecture. Comparative experiments demonstrate the superiority of the dual-aspect fusion over single-image methods in terms of reconstruction quality and geolocation accuracy. Notably, the model exhibits robust performance across diverse acquisition modes and geometries, showcasing its generalizability and suitability for height mapping applications. The study's findings underscore the potential of deep learning-driven SAR techniques in generating high-quality urban surface models efficiently and economically. [ABSTRACT FROM AUTHOR]

Monitoring the deformation of long-span bridges is essential for assessing structural health and safety. The existing single-camera deformation measurement method is unable to meet the high-precision measurement requirement for the deformation of large-span bridges. This paper proposes a real-time deformation measurement method for the long-span bridge using multi-inertial camera system. The method is based on visual measurement which the target deformation is measured by multiple cameras. Meanwhile, inertial measurement is used to estimate the relative pose of the camera to compensate for camera motion errors. It is applied to the health monitoring system of a long-span bridge. The experimental results show that the method proposed in this paper is highly consistent with the hydrostatic leveling measurement, with a RMSE of 4.83mm. The method can accurately and real-time measure the deformation of large-span bridges with a promising engineering value. [ABSTRACT FROM AUTHOR]

The total amount of sugarcane planting in China ranks third in the world, and Guangxi is the largest sugarcane planting base in China. In order to help the Guangxi government and industry better understand the annual planting distribution and scale of sugarcane in the whole region, it is convenient to optimize the planting structure and improve the efficiency of resource utilization, so as to ensure the stable development of Guangxi sugar industry. Based on the satellite-based hyperspectral image data of ' Zhuhai No.1 ', this paper carries out sugarcane recognition and area extraction, which fills the application gap of satellite-based hyperspectral image data in extracting sugarcane area in China. A globally optimized extended random walk model was used to identify and extract sugarcane by combining the spatial-spectral information of hyperspectral imagery. Firstly, the Initial probability estimation is performed using the LOR classifier, and then use the data after MNF dimensionality reduction to construct a weighted graph, and finally the probability optimization and recognition are carried out based on the extended random walk model. The results show that the spatial-spectral joint method based on extended random walk can effectively identify sugarcane, and the sugarcane recognition accuracy of 2021 year image can reach 91.4%, the misrecognition rate is 2.8%; the sugarcane recognition accuracy of 2020 year image can reach 90.5 %, the misrecognition rate is 3.1%. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) can image the ground with a wide area and high resolution at all times and in all weather and has become an important means of remote sensing. Differential interferometry SAR technology can obtain high-precision surface deformation information by processing more than two SAR images before and after deformation. In recent years, it has attracted widespread attention and research. However, due to the increasing number of ground electronic devices, ground radio frequency interference (RFI) has become one of the main problems in differential interferometry SAR processing, seriously affecting the performance of differential interferometry SAR imaging and differential interferometry surface deformation monitoring applications. In this paper, a clutter cancellation interference enhancement detection algorithm is proposed. By clutter suppression in the primary and secondary images, the interference-to-signal ratio is increased, which effectively improves the interference detection capabilities. The effectiveness of the algorithm in this paper is verified by the on-orbit measured data of the Lutan-1 satellites. [ABSTRACT FROM AUTHOR]

This paper presents a systematic review of published case studies on Heritage Building Information Modelling (HBIM) since 2018, and identifies research gaps in the subject matter. Building upon the foundational work of Ewart and Zuecco (2019), this research aims to reveal the latest trends in HBIM implementation, identify recent developments of HBIM technologies, changes in the purpose of HBIM programs and stakeholder roles and responsibilities, and uncover knowledge gaps that provide avenues for future research. Utilizing the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) approach, two major academic databases, Scopus and Web of Science (WOS), were searched, resulting in a rich and diverse dataset for analysis. The paper reports findings on the status of reality capture techniques used to acquire data for HBIM development, focusing on terrestrial laser scanning (TLS) technology. The review highlights the benefits and limitations of TLS for data acquisition in HBIM, as well as the integration of TLS with other reality capture technologies, such as Structure from Motion (SfM) and photogrammetry. The paper further outlines the typical workflow for processing TLS scan data and explores the integration of multiple point clouds for comprehensive heritage site modeling. In addition to the state of the art, this systematic review also uncovers several research gaps in the field of HBIM that offer opportunities for future research and innovation, including the lack of guidelines for data acquisition in HBIM programs, the predominantly manual development process of HBIM from TLS point cloud data, and the under-utilized capacity of TLS for long-term monitoring and change detection. This comprehensive review provides valuable insights into the current landscape of HBIM, offering guidance for future research and development in the heritage sector and highlighting areas in need of further investigation to advance the field. [ABSTRACT FROM AUTHOR]

Complex geometric distortions and nonlinear radiation differences between optical and synthetic aperture radar (SAR) images present challenges for the matching of sufficient and evenly distributed corresponding points. To address this problem, this paper proposes a deep convolutional network based on an attention mechanism for matching optical and SAR images. In order to obtain robust feature points, we employ phase consistency instead of image intensity and gradient information for feature detection. A deep convolutional network (DCN) is designed to extract high-level semantic features between optical and SAR images, providing robustness to geometric distortion and nonlinear radiation changes. Notably, incorporating multiple inverted residual structures in the DCN facilitates efficient extraction of local and global features, promoting feature reuse, and reducing the loss of key features. Furthermore, a dense feature fusion module based on coordinate attention is designed, focusing on the spatial positional information of effective features, integrating key features into deep descriptors to enhance the robustness of deep descriptors to nonlinear radiometric differences. A coarse-to-fine strategy is then employed to enhance accuracy by eliminating mismatches. Experimental results demonstrate that the proposed network performs better than the manually designed descriptors-based methods and the stateof- the-art deep learning networks in both matching effectiveness and accuracy. Specifically, the number of matches achieved is approximately 2 times greater than that of other methods, with a 10% improvement in F-measure. [ABSTRACT FROM AUTHOR]

The carbon budget concept states that the global mean temperature (GMT) increase is roughly linearly dependent on cumulative emissions of CO 2. The proportionality is measured as the transient climate response to cumulative emissions of carbon dioxide (TCRE). In this paper, the deviations of the carbon budget from the strict linear relationship implied by the TCRE are examined through the lens of a temperature response to an emission pulse (i.e., pulse response) and its relationship with a nonlinear TCRE. Hereby, two sources of deviation are distinguished: emission scenario and climate state dependence. The former stems from the scenario choice, i.e., the specific emission pathway for a given level of cumulative emissions and the latter from the change in TCRE with changing climatic conditions. Previous literature argues for scenario independence using a stylized set of emission scenarios, and offers a way to fit a nonlinear carbon budget equation. This paper shows how the pulse response, viewed as a Green's function, gives a unifying perspective on both scenario and state dependence. Moreover, it provides an optimization program that tests the scenario independence under the full range of emission pathways for a given set of constraints. In a setup chosen in this paper, the deviations stemming from emission pathway choices are less than 10 % of the overall temperature increase and gradually diminish. Moreover, using the pulse response as a Green's function, the scenario-dependent effects of a reduced-complexity climate model were replicated to a high degree, confirming that the behavior of scenario-dependent deviations can be explained and predicted by the shape of the pulse response. Additionally, it is shown that the pulse response changes with climatic conditions, through which the carbon budget state dependency is explained. Using a pulse response as an approximation for a state-dependent TCRE, an alternative method to derive a nonlinear carbon budget equation is provided. Finally, it is shown how different calibrations of a model can lead to different degrees of carbon budget nonlinearities. The analysis is done using FaIRv2.0.0, a simple climate emulator model that includes climate feedback modifying the carbon cycle, along with a one-box model used for comparison purposes. The Green's function approach can be used to diagnose both models' carbon budget scenario dependency, paving the way for future investigations and applications with other and more complex models. [ABSTRACT FROM AUTHOR]