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AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data. [ABSTRACT FROM AUTHOR]

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]

The number of studies investigating the effects of ocean acidification on marine organisms and communities increases every year. Results are not easily comparable since the carbonate chemistry and ancillary data are not always reported in similar units and scales and are not calculated using similar sets of constants. To facilitate data comparison, a data compilation hosted by the PANGAEA Data Publisher was initiated in 2008 and is updated on a regular basis (10.1594/PANGAEA.962556; Ocean Acidification International Coordination Centre (OA-ICC), 2023). By November 2023, a total of 1501 datasets (over 25 million data points) from 1554 papers had been archived. To easily filter and access relevant biological response data from this compilation, a user-friendly portal (https://oa-icc.ipsl.fr , last access: 9 November 2023) was launched in 2018. Here, we present an update of this data compilation since its second description by Yang et al. (2016) and provide an overview of the OA-ICC portal for ocean acidification biological response data, launched in 2018. Most of the study sites from which data have been archived are in the North Atlantic Ocean, North Pacific Ocean, South Pacific Ocean, and Mediterranean Sea, while polar oceans are still relatively poorly represented. Mollusca and Cnidaria are still the best-represented taxonomic groups. The biological processes most reported in the datasets are growth and morphology. Other variables that can potentially be affected by ocean acidification and are often reported include calcification/dissolution, primary production/photosynthesis, and biomass/abundance. The majority of the compiled datasets have considered ocean acidification as a single stressor, but their relative contribution has decreased from 68 % before 2015 to 57 % today, showing a clear tendency towards more data archived from multifactorial studies. [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]

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]

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]

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]

ISPRS Student Consortium (ISPRS SC) has played a significant role in bringing students and young researchers closer to the field of remote sensing and photogrammetry in the evolving landscape of geospatial sciences. Innovative strategies and events have been employed by the ISPRS SC to address contemporary challenges and harness emerging opportunities in the field of photogrammetry and remote sensing. Through an analysis of the global contributions of ISPRS SC, ranging from research initiatives to the online webinar series, this paper underscores the consortium's commitment to fostering collaboration, innovation, and knowledge exchange among young scholars and professionals worldwide. The paper highlights key thematic areas addressed by ISPRS SC such as advancements in remote sensing technologies, geospatial data analysis, and applications in diverse domains including environmental monitoring, agriculture, urban planning, and disaster management. Moreover, in this paper, we emphasize the role of the consortium in nurturing future leaders in geospatial sciences through ISPRS SC mentorship programs, networking opportunities, and capacity-building initiatives. By highlighting the initiatives led by the ISPRS Student Consortium, this paper underscores the key role of youth-driven innovation for positive change and shaping the future of geospatial sciences on a global scale. [ABSTRACT FROM AUTHOR]

This paper reduces uncertainty in the quantification of offshore wind speed and turbulence intensity. A range of industry standard relationships, including those from International Electrotechnical Commission (IEC) and International Organization for Standardization (ISO), are compared with an extensive set of met mast data collected offshore northwestern Europe over recent decades. Analysis initially focused on over 1000 independent storm peak events identified within the 10 min mean wind speed data. Time series and coherent vertical profiles were subjected to detailed scrutiny and analysis, considering wind speeds at various averaging periods, turbulence intensity and gust factors. Most peak events were associated with neutral atmospheric conditions, so they were well represented by the ISO Frøya profile, with a shear close to the IEC power law exponent of α= 0.11. A new pragmatic approximation of atmospheric stability in terms of relative shear is outlined, bringing together key elements of the IEC and ISO standards. New empirical relationships to quantify offshore wind ambient turbulence intensity are described. A simple generalised form of gust factor relationship is adopted, with a coefficient that varies with averaging period. Distinct recommendations are made for estimating peak 10 min mean winds from peaks in 1 or 3 h mean winds. Finally, a simplified workflow for the estimation of extreme offshore winds is used to show how IEC and ISO relationships can be effectively combined. [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 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]

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]

Earth observation (EO) applications offer significant potential for addressing challenges and driving sustainable development in the Philippines. Despite the positive effect EO services can bring, its utilization encounter barriers that includes the lack of awareness of its benefits. The Philippine Space Agency (PhilSA) initiated the Space Information Infrastructure Capacity Building and Training Program (SIICaP) that aims to promote awareness of EO applications through free and inclusive online webinar platforms. This paper highlights the various webinars conducted by SIICaP showing their relevance in raising awareness of EO data, services, and programs. The analysis of evaluation results from the webinar participants further illustrates the program's reach and success. By showcasing SIICaP, this paper contributes to efforts in developing capacity building programs that aim to have the same objectives of SIICaP. [ABSTRACT FROM AUTHOR]

Analyzing night lights images allows identifying and monitoring affected areas during conflicts, as an overall decrease in brightness indicates abandoned or destroyed buildings, curfews, or power shortages. While detecting decrease in lights intensity is an established technique to identify critical areas during conflicts, the causes of appearing bright lights have not been extensively studied. An increase in brightness may be related to extreme events, such as fires resulting from bombing. This paper shows several examples supporting this new use of these images, validated using high-resolution optical earth observation data and ancillary information sources in the frame of the conflict in Ukraine which has been ongoing since February 2022. [ABSTRACT FROM AUTHOR]

The Land Use/Cover Area frame Survey (LUCAS) of the European Union (EU) presents a rich resource for detailed understanding of land cover and use, making it invaluable for Earth observation (EO) applications. This paper discusses the recent enhancements and improvements in the LUCAS Copernicus module, particularly the data collection process of 2022, its protocol simplifications, and geometry definitions compared to the 2018 survey and data. With approximately 150 000 polygons collected in 2022, an increase from 60 000 in 2018, the LUCAS Copernicus 2022 data provide a unique and comprehensive in situ dataset for EO applications. The protocol simplification also facilitates a faster and more efficient data collection process. In 2022, there were 137 966 polygons generated out of the original 149 408 LUCAS Copernicus points, which means that 92.3 % of the points were actually surveyed. The data have 82 land cover classes for the Copernicus module that map to 88 classes up to the LUCAS level-3 legend. For land use the data have 40 classes, along with 18 classes of land use types. The dataset is available for download (product IDentification – PID: http://data.europa.eu/89h/e3fe3cd0-44db-470e-8769-172a8b9e8874 ;). The paper elaborates further on the implications of these enhancements and the need for continuous harmonization to ensure semantic consistency and temporal usability of data across different periods. Moreover, it calls for additional studies exploring the potential of the collected data, especially in the context of remote sensing and computer vision. It ends with a discussion of future data usage and dissemination strategies. [ABSTRACT FROM AUTHOR]

This paper addresses the actual problem of multiclass classification in large training sets. Classical Support Vector Machines (SVM) is a popular, convenient and well-interpreted classification method, but it has a high computational complexity of a training stage in a nonlinear case and a low data parallelism. The aim of this paper is to improve the scalability of nonlinear multiclass SVM.In the basis of this paper is Kernel-based Mean Decision Rule method with smart sampling (SS-KMDR) we previously proposed for fast solving large-scale binary SVM problems. In this paper we, at first, extend SS-KMDR for the multiclass classification problem. At second, we propose the modified algorithm of smart sample construction that allows to improve its characteristics and also extend it to possess the possibility to solve large-scale multiclass SVM problems. Experimental investigation of proposed methods was made on three large handwritten digit images data sets of different size and one large intrusion detection data set. Experiments show that both proposed multiclass methods allow to reach the quality near state-of-the-art SVC quality, but they essentially outperform it in the training time. The proposed Dual-Layer Smart Sampling SVM (DLSS-SVM method) allows additionally reduce training and test times in contrast to the basic smart sampling technique. [ABSTRACT FROM AUTHOR]

High-Definition Maps (HD Maps) are lane-level 3-dimensional road network models that ensure the safe operation of autonomous vehicles and drive the continuous advancement of autonomous driving technology to higher levels. However, HD Maps currently face challenges regarding limited coverage and description of indoor environments (e.g. underground parking), as well as the lack of uniformity between indoor and outdoor map formats. Therefore, this paper proposes a Layered HD Map model for both indoor and outdoor environments. The map model consists of 6 layers, allowing effective representation of indoor and outdoor environments, as well as transitional areas. The paper also establishes an Unmanned Ground Vehicle (UGV) to collect and update map data using LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) and autonomous exploration technology, aiming to enhance the efficiency and automation of HD Map acquisition. In order to verify the validity of the proposed map model, this paper conducted a navigation experiment in a double decker parking lot containing entrances and exits. The results of the experiments demonstrate that the Layered HD Map generated through autonomous LiDAR SLAM can effectively describe the indoor and outdoor environments, and enable successful navigation of UGVs. [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]

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]

Array Interferometric Synthetic Aperture Radar (ArrayInSAR) point cloud is a point cloud obtained by three-dimensional imaging of two-dimensional Synthetic Aperture Radar (SAR) images using array interferometric synthetic aperture radar technology, which can eliminate the phenomenon of layover in two-dimensional images and provide new data support for intelligent mapping. However, due to the influence of system thermal noise, baseline error and environment interference, there are noise points in the ArrayInSAR point cloud with uneven distribution and large elevation error, which bring a lot of gross errors to the 3D terrain results. To solve the problem that discrete points in ArrayInSAR point cloud data seriously affect the quality of three-dimensional data, this paper proposes an ArrayInSAR point cloud outlier noise removal method based on hybrid filtering. According to the distribution pattern of noise points, the outlier noise is divided into discrete outlier noise points and clusters of outlier noise. Firstly, the adaptive segmentation of point cloud dispersion coefficient within the k-neighborhood is used to remove discrete outlier noise points, and then the adaptive threshold segmentation algorithm based on the number of in-class points after K-means clustering is used to remove the cluster outlier noise of ArrayInSAR point cloud data. In order to verify the effectiveness, the proposed method is compared with other classical outlier noise removal methods. The experimental results show that the method proposed in this paper can effectively remove outlier noise in ArrayInSAR point cloud data and improve the quality of ArrayInSAR point clouds. [ABSTRACT FROM AUTHOR]

This paper delves into the multifaceted experiences and insights garnered from organizing a short-term training programme aimed at professionals, students, academics, and climate enthusiasts. The programme, named CLImbing for CLImate GEOspatial School (CLIGEOS-2024), was a collaborative effort by the International Society of Photogrammetry and Remote Sensing (ISPRS) and ISPRS Student Consortium (ISPRS-SC) alongside Center for space science and geomatics studies (CSSGS) Pashchimanchal campus, Institute of Engineering, Tribhuvan University, Nepal, Universita' Degli Studi Di Udine, Italy and LJ University, India aimed at addressing environmental challenges, particularly in hill and mountain regions. Despite facing logistical and communication hurdles due to the geographical dispersion of organizing institutions, the event successfully brought together participants with backgrounds in GIS, remote sensing, agriculture, and forestry. The programme was structured to promote participatory learning and project-based learning methodologies, incorporating classroom teaching, hands-on activities, fieldwork, data collection, short projects, quizzes, and evaluation mechanisms. Through a meticulously planned sequence, participants engaged in theoretical lectures, practical sessions on drone technology, industry expert presentations, and a mountain trek to observe climate change phenomena first-hand. Despite challenges related to infrastructure, including limited internet connectivity and access to data, the programme served as a catalyst for raising awareness about climate change and promoting sustainable practices. Key takeaways highlighted the importance of spatial data analysis, interdisciplinary collaboration, and hands-on learning in advancing solutions for sustainable development. The paper concludes with lessons learned from the programme, providing insights for future initiatives aimed at capacity building and knowledge dissemination. [ABSTRACT FROM AUTHOR]

The paper investigates multidisciplinary education and specialization at the master's level, analyzes assessment methods, and proposes a technique for evaluating geospatial technology education. It emphasises is on pedagogical approaches, and highlights the importance of capacity building to meet the growing demand for skilled professionals in spatial information management. The research provides guidelines for improving course content, structure, and teaching methodologies, aiming to enhance the quality and relevance of geospatial education for both students and stakeholders. This paper is based on online data gathered from 115 universities across the globe, who are imparting geospatial education, campus-based and e-learning, also a survey was conducted of two thousand students. It supported in analysis and suggestion to develop a specific robust technique for assessment of the geospatial technology education and provides the corrective measures to the users of the technique. It proposed a framework aimed at enhancing the course content, organization, and teaching methods substantially. Further an attempt was made to adopt AHP model for investigating the statistical approach. It was aimed to know the actual values and significance of each parameter used for analysis of the study. In order to prepare a common guideline for improving the course structure, course content and teaching methodology, ANFIS architecture (Artificial Neural Fuzzy-Inference System) was adopted. Spatial information represented in the form of thematic maps in relation with the respective attribute data in Geographical Information System (GIS) environment is now used by all spectrum of the society, be it government organizations, industry, and academia. The result of the study has designed the criteria and common guidelines, which may be implemented, would able to help and curtail the challenges of the geospatial education as global vision for Local Action. [ABSTRACT FROM AUTHOR]

This paper presents a collaborative effort to develop localized solar photovoltaic (PV) power output (PPV) forecasting models for the Philippines using geospatial data. It underlines the importance of solar energy in the country and discusses the opportunities and challenges associated with PPV forecasting. Project SINAG, a two-year research project, aimed to develop solar PV output forecasting models through a collaborative approach with academic institutions, solar energy industries, and government agencies. Actual PPV data from 43 solar PV installations were analyzed alongside meteorological data from the PAGASA weather bureau, ERA5, AHI-8, and FY- 4A. These datasets were filtered based on a one-year period to ensure quality. The study employed SARIMAX, LSTM, and XGBoost models individually and in hybrid models to develop the forecasting models. Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In a case study in Baguio City, the SARIMAX model exhibited strong seasonal dependence, providing more accurate forecasts in dry seasons than in wet seasons. Additionally, the forecasting accuracy of each model (SARIMAX, LSTM, and XGBoost) varied based on the month and location of the installation, emphasizing the need for local and season-based PPV forecasting models. Despite implementation challenges, such as collaboration arrangements, bureaucratic barriers, and budget constraints, the project produced thirteen research publications and provided data for three student theses. This paper also demonstrated diverse engagements and contributions that emphasize the significance of collaborative research in conducting nationwide-scale data-driven projects. [ABSTRACT FROM AUTHOR]

Terrain services have increasingly wide applications in urban planning, 3D gaming animation, and other fields. Map websites typically provide terrain services to users in the form of terrain shaded relief tiles. Map tile technology offers a new solution for large-scale concurrent user access. Although terrain shaded relief tiles can effectively represent changes in terrain relief, users cannot adjust the visualized raster images for visualization schemes, making data analysis and applications more challenging. The emergence of Terrain-RGB technology has effectively addressed this limitation. However, currently, internet maps mostly provide multi-level map services to users in the form of web map tile service (WMTS). Terrain-RGB images produced by simple encoding tools may exhibit grid lines when rendered on tiles with a small grid spacing, affecting the usability of Terrain-RGB products. Therefore, this paper proposes a technical approach for producing global multi-level Terrain-RGB products using digital elevation model (DEM), focusing on addressing the issues of gridlines and splicing traces from different scenes DEM data, offering a new solution for map websites to release analyzable terrain services. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization presents challenges and requirements for multi-class object detection in urban scenes. Accurately identifying buildings, vehicles, and trees in urban scenes can optimize urban planning, traffic management, monitoring environmental conditions, and promote the development of smart cities. Traditional target detection methods perform poorly in complex urban environments, while deep learning technology achieves accurate target recognition and positioning by automatically extracting high-level semantic features. In this study, we chose to use the YOLOv5s algorithm for multi-class target detection in urban scenes. YOLOv5s is a lightweight deep learning model with small storage space and efficient detection speed. In this paper, the Potsdam area data published by ISPRS is used to make the label data of buildings, vehicles and trees. The YOLOv5s algorithm is used to iteratively train the model. The results show that the mAP value detected by the YOLOv5s model can reach 82.83%. The experimental results show that the algorithm shows higher accuracy than SSD and Faster R-CNN in tree detection. Although it has a slight decline in building and vehicle detection, considering the factors such as detection accuracy, speed, and model size, the YOLOv5s algorithm has a better recognition and detection effect for the detection of multi-class targets in urban scenes. [ABSTRACT FROM AUTHOR]

Offshore wind farm (OWF)'s impact on marine ecological environment has attracted widespread attention since its rapid development. The paper uses high-resolution satellite image data to extract the distribution of offshore wind farms (OWFs) in China based on deep learning method. Chlorophyll-a (CHL-a) concentration and sea surface temperature (SST) were used as indicators to analyze the impact of OWFs' development. We can then draw the following conclusions: 1) From 2016 to 2022, the number of OWFs in China continues to increase, the growth rate reachs 28% in 2022. 2) The construction of OWFs presents a development trend from near sea to far sea and from shallow sea to deep sea. From 2016 to 2022, the new OWFs are mainly concentrated in sea areas with 5-50 meters water depth, accounting for 84.7 % of the total increase in OWFs. 3) The distribution density of OWFs is negatively correlated with CHL-a concentration and SST, the more OWFs, the greater the impact on CHL-a and SST. In one word, our work analyzed the development trend of OWFs in China and evaluated the impact of OWFs' expansion on marine ecological environment and climate. [ABSTRACT FROM AUTHOR]

Precise georeferencing of remote sensing data is usually implemented in a post-processing fashion and is a crucial step for Earth observation applications such as change detection, natural hazard management, and ground target tracking. It is particularly important for small satellites intending to perform temperature monitoring and wildfire detection on a global scale wherein the precise location of fire is to be communicated. The cost-efficient navigating systems on board such satellites are often not capable of providing accurate geolocation information directly due to space and power limitations. Therefore, it is very important to have a globally applicable georeferencing refinement framework that is robust against illuminational and time-relevant scene changes. In this paper, we propose a georeferencing framework for thermal infrared images that consists of ensemble matching of deep learning-based land cover predictions to archival, well-georeferenced land cover maps. We verify the proposed framework on the georeferencing of single-band Landsat thermal imagery. Experimental results show the efficiency and practicality of the method with 72% of the test images geolocated within 1-pixel accuracy with no trajectory information available. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is common: training deep learning models on small, homogeneous data sets, typically data from only a single hydrological basin. In this position paper, we show that LSTM rainfall–runoff models are best when trained with data from a large number of basins. [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]

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]

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]

Ultra-wideband (UWB) positioning technology stands out from many indoor positioning technologies with its advantages of high precision. However, non-line-of-sight (NLOS) propagate leads to heavy range error and reduces position accuracy, this paper proposes a NLOS identification method based on channel impulse response (CIR), which includes three stages. Firstly, CIR based feature selection is carried out, which includes correlation analysis of calculated features. Secondly, fuzzy comprehensive evaluation model is introduced to NLOS identification. Finally, time of arrival (TOA) based location estimation is realized after NLOS ranging error mitigation. Simulation results show that the average identification rate of NLOS can exceed 86%, and the average positioning error can be reduced by about 0.5m. [ABSTRACT FROM AUTHOR]

In recent years, the evolution of digital twin technology has paved the way for the construction of intelligent holographic intersections. This can be facilitated by utilizing precise point clouds from roadside lidar. With its capability of real-time monitoring, lidar plays a crucial role in enhancing intersection perception, enabling precise detection and tracking of road objects, as well as providing accurate speed estimates. Despite the introduction of few roadside lidar datasets aimed at enhancing supervised learning algorithms, their applicability to intelligent intersection monitoring remains limited. To address this, this paper presents an Intelligent Intersections (Int2sec) dataset, which exhibits several salient features: 1) it encompasses a broad array of urban intersection scenarios accompanied by a substantial quantity of object annotations; 2) the deployment of dual lidar stations facilitates a thorough scanning of scenes, thereby ensuring expansive scene coverage and mitigating the mutual occlusion phenomenon amongst objects; and 3) the dataset not only catalogues the coordinates, dimensions, and orientations of objects but also encompasses additional attributes such as tracking IDs and real-time motion statuses. Furthermore, the paper evaluates the efficacy of various prominent benchmarking networks, providing a critical analysis and prospective for future research. [ABSTRACT FROM AUTHOR]

Aiming at the problems of islands, redundancy and sharing difficulties of spatial and temporal business models and data in geospatial and spatial business systems, this paper proposes an integrated and coupled expression method of models and data guided by metadata sets, based on the research on core metadata of information resources and component-based encapsulation. First of all, this method classifies the model and data features, and constructs the metadata description and expression framework dominated by feature meta-components; then, establishes metadata sets through the expansion of the metadata description and expression framework; then, constructs the correlation between the model and the data through the use of the established metadata sets by means of the component-based encapsulation method, so as to complete the integrated coupling of the model and the data; and finally, realises the integrated coupling expression of the model and the data in the digital pipeline business system. The results show that this method integrates the expression of digital pipeline element models and data, solves the problems of silos, redundancy and conflicts of heterogeneous models and data from multiple sources, ensures the integrity of the attributes of the models and data elements, and promotes the structured expression and application of the models and data. [ABSTRACT FROM AUTHOR]

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]

The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased CO2 concentrations and to partition contributions from changes in cloud amount, altitude, and optical depth. However, because this method relies on cloud property histograms derived from passive satellite sensors or produced by passive satellite simulators in models, changes in obscuration of lower-level clouds by upper-level clouds can cause apparent low-cloud feedbacks and adjustments, even in the absence of changes in lower-level cloud properties. Here, we provide a methodology for properly diagnosing the impact of changing obscuration on cloud feedbacks and adjustments and quantify these effects across climate models. Averaged globally and across global climate models, properly accounting for obscuration leads to weaker positive feedbacks from lower-level clouds and stronger positive feedbacks from upper-level clouds while simultaneously removing a mostly artificial anti-correlation between them. Given that the methodology for diagnosing cloud feedbacks and adjustments using cloud radiative kernels has evolved over several papers, and obscuration effects have only occasionally been considered in recent papers, this paper serves to establish recommended best practices and to provide a corresponding code base for community use. [ABSTRACT FROM AUTHOR]

The transferability of hydrological models over contrasting climate conditions, also identified as model robustness, has been the subject of much research in recent decades. The occasional lack of robustness identified in such models is not only an operational challenge – since it affects the confidence that can be placed in projections of climate change impact – it also hints at possible deficiencies in the structures of these models. This paper presents a large-scale application of the robustness assessment test (RAT) for three hydrological models with different levels of complexity: GR6J, HYPE and MIKE SHE. The dataset comprises 352 catchments located in Denmark, France and Sweden. Our aim is to evaluate how robustness varies over the dataset and between models and whether the lack of robustness can be linked to some hydrological and/or climate characteristics of the catchments (thus providing a clue as to where to focus model improvement efforts). We show that, although the tested models are very different, they encounter similar robustness issues over the dataset. However, models do not necessarily lack robustness in the same catchments and are not sensitive to the same hydrological characteristics. This work highlights the applicability of the RAT regardless of model type and its ability to provide a detailed diagnostic evaluation of model robustness issues. [ABSTRACT FROM AUTHOR]

The accurate calibration of parameters in atmospheric and Earth system models is crucial for improving their performance but remains a challenge due to their inherent complexity, which is reflected in input–output relationships often characterised by multiple interactions between the parameters, thus hindering the use of simple sensitivity analysis methods. This paper introduces the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a new tool designed with the aim of providing a simple and flexible framework to estimate the sensitivity and importance of parameters in complex numerical weather prediction models. This tool leverages the strengths of multiple regression-based and probabilistic machine learning methods, including LASSO (see the list of abbreviations in Appendix B), support vector machine, classification and regression trees, random forest, extreme gradient boosting, Gaussian process regression, and Bayesian ridge regression. These regression algorithms are used to construct computationally inexpensive surrogate models to effectively predict the impact of input parameter variations on model output, thereby significantly reducing the computational burden of running high-fidelity models for sensitivity analysis. Moreover, the multi-method approach allows for a comparative analysis of the results. Through a detailed case study with the Weather Research and Forecasting (WRF) model coupled with the Noah-MP land surface model, ML-AMPSIT is demonstrated to efficiently predict the effects of varying the values of Noah-MP model parameters with a relatively small number of model runs by simulating a sea breeze circulation over an idealised flat domain. This paper points out how ML-AMPSIT can be an efficient tool for performing sensitivity and importance analysis for complex models, guiding the user through the different steps and allowing for a simplification and automatisation of the process. [ABSTRACT FROM AUTHOR]

This paper introduces a multifaceted methodology to identify and compile single natural hazards and multi-hazard interrelationships within the context of data-scarce urban settings, exemplified by the Kathmandu Valley, Nepal. This approach integrates (i) five blended types of evidence to support a more nuanced and holistic understanding of a hazardscape where data are scarce and (ii) a 2 h practitioner stakeholder workshop with seven participants to provide greater context to the hazards, consider their impacts through the co-production of multi-hazard interrelationship scenarios, and show how this methodology could support more people-centred disaster risk reduction (DRR) strategies. We use blended evidence types, including academic literature, grey literature, media, databases, and social media, to systematically search for exemplars of single hazard types and multi-hazard interrelationships that have influenced or could potentially influence the Kathmandu Valley. We collated 58 sources of evidence for single hazard types and 21 sources of evidence for multi-hazard interrelationships. Using these sources, our study identified 21 single hazard types across 6 hazard groups (geophysical, hydrological, shallow Earth processes, atmospheric, biophysical, and space/celestial hazards) and 83 multi-hazard interrelationships (12 have direct case study evidence of previous influence in the Kathmandu Valley) that might influence the Kathmandu Valley. These exemplars are collated into a Kathmandu Valley Single Hazards and Multi-Hazard Interrelationships Database (Thompson et al., 2024) accompanying this paper. We supplement these exemplars with multi-hazard interrelationship scenarios and multi-hazard impacts developed by practitioner stakeholders engaged in DRR research and practice in the Kathmandu Valley. The results illustrate the complexity of the hazardscape, with many single hazard types and multi-hazard interrelationships potentially influencing the Kathmandu Valley. The research emphasises the importance of inclusive DRR strategies that recognise disaggregated impacts experienced by different social groups. This knowledge can inform the development of dynamic risk scenarios in planning and civil protection, thus strengthening multi-hazard approaches to DRR in "Global South" urban areas such as the Kathmandu Valley. [ABSTRACT FROM AUTHOR]

The large-scale landslide susceptibility assessment (LSA) is an important tool for reducing landslide risk through the application of resulting maps in spatial and urban planning. The existing literature more often deals with LSA modelling techniques, and the scientific research very rarely focuses on acquiring relevant thematic and landslide data, necessary to achieve reliable results. Therefore, the paper focuses on the crucial step of classifying continuous landslide conditioning factors for susceptibility modelling by presenting an innovative comprehensive analysis that resulted in 54 landslide susceptibility models to test 11 classification criteria (scenarios which vary from stretched values, partially stretched classes, heuristic approach, classification based on studentized contrast and landslide presence, and commonly used classification criteria, such as natural neighbour, quantiles and geometrical intervals) in combination with 5 statistical methods. The large-scale landslide susceptibility models were derived for small and shallow landslides in the pilot area (21 km2) located in the City of Zagreb (Croatia), which occur mainly in soils and soft rocks. Some of the novelties in LSA are the following: scenarios using stretched landslide conditioning factor values or classification with more than 10 classes prove more reliable; certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others; all the tested machine learning methods give the best landslide susceptibility model performance using continuous stretched landslide conditioning factors derived from high-resolution input data. The research highlights the importance of qualitative assessments, alongside commonly used quantitative metrics, to verify spatial accuracy and to test the applicability of derived landslide susceptibility maps for spatial planning purposes. [ABSTRACT FROM AUTHOR]

Ocean alkalinity enhancement (OAE) and direct ocean removal (DOR) are emerging as promising technologies for enacting negative emissions. The long equilibration timescales, potential for premature subduction of surface water parcels, and extensive horizontal transport and dilution of added alkalinity make direct experimental measurement of induced CO2 uptake challenging. Therefore, the challenge of measurement, reporting, and verification (MRV) will rely to a great extent on general circulation models, parameterized and constrained by experimental measurements. A number of recent studies have assessed the efficiency of OAE using different model setups and different metrics. Some models use prescribed atmospheric CO2 levels, while others use fully coupled Earth system models. The former ignores atmospheric feedback effects, while the latter explicitly models them. In this paper it is shown that, even for very small OAE deployments, which do not substantially change atmospheric pCO2 , the change in oceanic CO2 inventories differs significantly between these methods due to atmospheric feedback causing some ocean CO2 off-gassing. An analogous off-gassing occurs during direct air capture (DAC). Due to these feedback effects, care must be taken to compute the correct metrics when assessing OAE efficiency with respect to determining negative emissions credits, as opposed to determining the effect on global temperatures. This paper examines the commonly used metrics of OAE efficiency, their exact physical meanings, the assumptions inherent in their use, and the relationship between them. It is shown that the efficiency metric η(t) , used in prescribed p CO 2atm simulations, equals the equivalent schedule of a gradual DAC removal and storage in a fully coupled system. [ABSTRACT FROM AUTHOR]

This paper describes the methods that are used in the SAPRC mechanism generation system, MechGen, to estimate rate constants and derive mechanisms for gas-phase reactions of volatile organic compounds (VOCs) in the lower atmosphere. Versions of this system have been used for over 20 years in the development of the SAPRC mechanisms for air quality models, but this is the first complete documentation of the scientific basis for the chemical mechanisms it derives. MechGen can be used to derive explicit gas-phase mechanisms for most compounds with C, H, O, or N atoms. Included are reactions of organic compounds with hydroxy (OH) and nitrate (NO3) radicals, O3, and O3P; photolysis or unimolecular reactions; and the reactions of the radicals they form in the presence of O2 and oxides of nitrogen (NOx) at lower-atmospheric temperatures and pressures. Measured or theoretically calculated rate constants and branching ratios are used when data are available, but in most cases rate constants and branching ratios are estimated using various structure–reactivity or other estimation methods. Types of reactions include initial reactions of organics with atmospheric oxidants or by photolysis; unimolecular and bimolecular reactions of carbon-centered, alkoxy, and peroxy radicals; and those of Criegee and other intermediates that are formed. This paper documents the methods, assignments, and estimates currently used to derive these reactions and provides examples of MechGen predictions. Many of the estimation methods discussed here have not been published previously, and others have not been used previously in developing comprehensive mechanisms. Our knowledge of atmospheric reactions of organic compounds rapidly and continuously evolves, and therefore mechanism generation systems such as MechGen also need to evolve to continue to represent the current state of the science. This paper points out areas where MechGen may need to be modified when the system is next updated. This paper concludes with a summary of the major areas of uncertainty where further experimental, theoretical, or mechanism development research is most needed to improve predictions of atmospheric reaction mechanisms of volatile organic compounds. [ABSTRACT FROM AUTHOR]

Extreme cold winter temperatures in Europe have huge societal impacts. Being able to simulate worst-case scenarios for such events for present and future climates is hence crucial for short- and long-term adaptation. In this paper, we are interested in persistent cold events, whose probability will decrease with climate change. Large ensembles of simulations allow us to better analyse the mechanisms and characteristics of such events but can require significant computational resources. Rather than simulating very large ensembles of normal climate trajectories, rare-event algorithms allow the sampling of the tail of distributions more efficiently. Such algorithms have been applied to simulate extreme heat waves. They have emphasized the role of atmospheric circulation in such extremes. The goal of this study is to evaluate the dynamics of extreme cold spells simulated by a rare-event algorithm. We focus on cold winter temperatures that have occurred in France from 1950 to 2021. We investigate winter mean temperatures (December, January and February) and identify a record-shattering event in 1963. We find that although the frequency of extreme cold spells decreases with time, their intensity is stationary. We apply a stochastic weather generator (SWG) approach with importance sampling to simulate the coldest winters that could occur in a factual and counterfactual climate. We thus simulate ensembles of the worst winter cold spells that are consistent with reanalysis data. We find that a few simulations reach colder temperatures than the historical record-shattering event of 1963. This shows that present-day conditions can trigger winters as cold as that record in spite of global warming. The atmospheric circulation that prevails during those events is analysed and compared to the observed circulation during the record-breaking events, showing no main change in the mechanisms leading to this type of extreme event. [ABSTRACT FROM AUTHOR]

Floods pose significant risks to cultural heritage (CH), yet post-disaster damage data on CH remain lacking. In this paper, we address this gap by focusing on the ex post assessment of flood-induced damage to CH. The method involves the identification of damaged assets and a field survey to assess loss in tangible value (LTV) and loss in intangible value (LIV). The potential contributing factors, e.g. water depth and river slope, are analysed through geospatial analysis. Ex post damage data on CH are compared with the outcome of an ex ante analysis based on available methods to verify the quality of exposure data and possible limitations. The method is applied to the 15–16 September 2022 flood event that occurred in the Marche region (Italy). The survey involved 14 CH in 4 municipalities and 3 catchments. Results highlight the inadequacy of existing exposure data for ex ante damage assessment and the importance of building characteristics. However, ex post data confirm that religious architectures are likely to suffer the highest LTV and LIV. The ex post damage analysis provided a semi-quantitative evaluation of both LTV and LIV in relation to flood characteristics. Notably, significant correlations between LTV and flood depth, as well as with the slope of the riverbed (a proxy for river flow velocity), were found. LIV correlates well to flood depth and river slope albeit with lower R2 and larger RMSE, highlighting that intangible impact analysis requires more effort than hazard characterization. Further research should increase the availability of ex post damage data on CH to form the basis for damage model validation and development of empirical vulnerability functions. [ABSTRACT FROM AUTHOR]