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Information given in topographic map captions or in GIS models is often insufficient to recognize interesting geographical patterns. Some prototypes of GIS have already been extended with a knowledge-base and some reasoning capabilities to support sophisticated map interpretation processes. Nevertheless, the acquisition of the necessary knowledge is still a demanding task for which machine learning techniques can be of great help. This paper presents INGENS, a prototypical GIS which integrates machine learning tools to assist users in the task of topographic map interpretation. The system can be trained to learn operational definitions of geographical objects that are not explicitly modeled in the database. INGENS has been applied to the task of Apulian map interpretation in order to discover geographic knowledge of interest to town planners. [Copyright &y& Elsevier]

Sensitivity analysis (SA) has been used to evaluate the behavior and quality of environmental models by estimating the contributions of potential uncertainty sources to quantities of interest (QoI) in the model output. Although there is an increasing literature on applying SA in environmental modeling, a pragmatic and specific framework for spatially distributed environmental models (SD-EMs) is lacking and remains a challenge. This article reviews the SA literature for the purposes of providing a step-by-step pragmatic framework to guide SA, with an emphasis on addressing potential uncertainty sources related to spatial datasets and the consequent impact on model predictive uncertainty in SD-EMs. The framework includes: identifying potential uncertainty sources; selecting appropriate SA methods and QoI in prediction according to SA purposes and SD-EM properties; propagating perturbations of the selected potential uncertainty sources by considering the spatial structure; and verifying the SA measures based on post-processing. The proposed framework was applied to a SWAT (Soil and Water Assessment Tool) application to demonstrate the sensitivities of the selected QoI to spatial inputs, including both raster and vector datasets - for example, DEM and meteorological information - and SWAT (sub)model parameters. The framework should benefit SA users not only in environmental modeling areas but in other modeling domains such as those embraced by geographical information system communities. • A pragmatic framework of sensitivity analyses is provided for spatially distributed environmental models. • The framework prescribes sequential steps in which important considerations are highlighted. • The framework benefits users of sensitivity analyses in environmental modeling and GIS communities. [ABSTRACT FROM AUTHOR]

Geospatial Data Analytics (GDA) is a futuristic platform for analyzing and processing volumetric data in remote sensing and GIS applications. GDA utilizes the Internet of Spatial Things (IoST), mist, fog, and cloud computing architecture as a backend tool for analyzing and processing big geospatial data. This paper introduces utilizing these interconnected network architectures of cloud, fog, and mist to process a large volume of geospatial data. Also, the paper presents a flexible, interconnected distributed network system, i.e., IoST-mist-fog-cloud GIS architecture, to analyze and manage geospatial data. The proposed system helps cloud platforms when MIST devices are trying to cut down on latency and boost throughput at the edge of the IoST tier. It also performs the geospatial crime data visualization of the total number of stolen vehicles from 2001 to 2011 from all the states of India as a case study by using the proposed model. It explains the mathematical and analytical queueing model of the proposed system. In addition, it performs a performance evaluation and experimental findings on the proposed architecture and uses graphs to represent the various arithmetic outcomes. The experimentation result proves the proposed interconnected network architecture's efficacy in terms of reliability and efficiency. [ABSTRACT FROM AUTHOR]

Decision analysis is a key aspect in improving slums intervention strategies utilizing a variety of tools to evaluate all relevant information that support decision-making process for dealing with the complexity of slums interventions policies. This paper presents a framework integrating Multi Criterion Decision Analysis (MCDA) with (GIS) based decision support tool to quantitatively assess land suitability for site relocation of an urban slum site utilizing Analytical Hierarchy Process (AHP), which is commonly used for fastest growing decision-analytic techniques in several disciplines for the determination of various aspects weights. Herein Batn Al Baqara region (which is located in south of Cairo) was selected as study area to be relocated as it was considered among the most difficult to deal with, three proposed relocation sites were selected and analyzed using MCDA-GIS Model. Twelve different criteria were utilized to evaluate the degree of suitability of the relocation sites. A five degree of suitability were employed according to the different criteria scores for each site. The results supported that Al Asmarat is the most suitable site for the relocation of slum since it achieved the highest evaluation score (84.92 %) when the model was applied to the relocation sites. [ABSTRACT FROM AUTHOR]

This paper introduces an implementation of different GIS tools into Streamlit application: FCNN Terrain Classification, Earth Engine Dataset Parsing, and GIS Timelapse Animations. This toolkit is integrated with terrain multi-classification models using Fully Convolutional Neural Networks (FCNNs) for imagery data into Streamlit microservices. The proposed methodology involves labeled and unlabeled data collection from ESA WorldCover and Sentinel-2 MSI on the Google Earth Engine, compressing datasets into TFRecords format with 9 diverse terrain types, and handling Google Cloud training computations. The experimental results demonstrate the effectiveness of the CNN-based approach, achieving a tolerable from 60% up to 80% accuracy of the model and robust classification performance. The simplicity and efficiency of the proposed method make it suitable for real-world tasks requiring reliable and fast GIS analytics. [ABSTRACT FROM AUTHOR]

• Exploit deep transfer learning and anomaly detection for cycling safety. • Reduce the need for data labeling and model training. • Use case based on real data collected with high-frequency GNSS. • Application of the scheme is presented by plotting detected anomalies on a map in order to identify dangerous locations in the city of Catania Italy. Introduction: Despite the general improvements in road safety, with the growing number of bicycle users, cycling safety is still a challenge as demonstrated by the fact that it is the only road transport mode with an increase in the number of fatalities in EU cities. Problem: Moreover, to analyze the problem to improve the road transport system, the traditional network screening based on crash statistics is a reactive approach and less effective due to the lack of suitable bicycle data availability, as well. In such a framework, new opportunities for data collection in smart cities and communities are emerging as proactive approaches to identify critical locations where safety treatments can be effectively applied to prevent bicycle crashes. Method: This research applied a deep transfer learning model to detect anomalies in cycling behavior that can be associated with traffic conflicts or near-miss crashes. Results: The paper presents how to build a users' tailored riding model named DTL AD to detect and localize riding anomalies by using a set of data in the National Marine Electronics Association (NMEA) string of Global Navigation Satellite System (GNSS) recorded with instrumented bicycles by different cyclists. Conclusion: More specifically, DTL AD exploits a convolutional autoencoder (CAE) with transfer learning to reduce data labelling and training effort. Practical Application: A case study demonstrates the identification of anomalies in cycling behavior visually represented on Geographic Information Systems (GIS) maps, showing how data clustering is well located in high-risk areas. [ABSTRACT FROM AUTHOR]

In response to the obstacles posed by finite resources and environmental issues, Sarawak has transitioned its focus towards sustainable and green energy to establish a resilient and eco-friendly energy landscape. Generally, Sarawak is blessed with abundant green energy resources; however, constructive green energy integration methods were absent. Proper green energy integration is necessary due to its intermittent properties and geographical dispersion. Therefore, the paper proposes a novel methodology using Geographical Information System tools incorporating geographical databases, fuzzy logic operations and Traveling Salesman Problem-Binary Integer Programming algorithms to integrate green energies focusing on optimal power line routing design. The methodology begins with clustering the green energies based on geographical divisions. Then, it considers three influential factors (distance, elevation difference, and average ground flash density) and transforms them into matrix data for each cluster. Fuzzy logic operations optimize the trade-off among these factors and form fuzzy values. Following this, the Traveling Salesman Problem-Binary Integer Programming formulation creates pairs of green energy, distance vectors, equality constraints, and binary bounds. The optimization process constructs and eliminates multiple subtours to generate an optimal single loop with minimal value, representing the optimal power line routing design. Rigorous analyses, comparison, and validation demonstrate that the proposed method consistently outperforms ordinary Traveling Salesman Problem-Binary Integer Programming, ranking top 1 across all clusters. Further evaluation against the state-of-the-art fuzzy Traveling Salesman Problem algorithms reveals that the proposed model secures the lowest fuzzy values with extremely low computation times across all clusters. Furthermore, the paper presents an innovative way to consider the ground flash density factor in green energy integration. The comprehensive algorithms and coding provided are valuable assets for researchers and investors to explore and conduct in-depth research on this prospect. Finally, this paper provides valuable directions for regional development, especially in harnessing and integrating green energies to boost economic and state infrastructure. • Innovative and novel methodology for optimal green energy integration. • Clustering green energy locations with optimal power line routing design. • Incorporation of ground flash density for enhanced green energy efficiency. • Proposed methods VS ordinary TSP-BIP and other fuzzy TSP algorithms. • Valuable complete algorithms for research, investment, and regional development. [ABSTRACT FROM AUTHOR]

Semantic web technologies are essential for seamless data exchange between Building Information Modelling and technologies such as the Internet of Things and Geographic Information Systems. However, rapid advancements complicate their implementation, making past reviews obsolete. This paper investigates recent applications of semantic web technologies in the construction industry and addresses why and where semantic web technologies are adopted, how and when they are used, and what the significant knowledge gaps undermining semantic web technologies' applications are. These questions collectively provide a much-needed context for this evolving field. A systematic literature review covering the past five years identifies 65 relevant papers from 419 works. Findings indicate the dominance of the entire building lifecycle applications (27.7%), and a trend shifting to specific lifecycle stages. This study provides insights into the latest applications of semantic web technologies and their potential to enhance efficiency and interoperability in the AEC sector. • Reviews recent research about semantic web technologies in the AEC industry. • Undertakes a systematic literature review identifying 65 key articles. • Answers 'why', 'where', and 'how' semantic web technologies are used in the AEC sector. • Results highlight key efficiencies and future opportunities for applications. [ABSTRACT FROM AUTHOR]

Egypt abounds with multiple heritage sites that have lots of outstanding universal values (OUV). However, there are only 7 Egyptian heritage sites in the permanent list of UNESCO for world heritage. Egypt faces obstacles in registering world heritage sites as a result of the absence of a registration strategy to increase the Egyptian world heritage sites. Therefore, this paper aims propose a strategy to convert the heritage sites included in the tentative list of UNESCO to the permanent list in order to create an Egyptian guide for registration as the world heritage of UNESCO. The paper follows an analytical deduction approach based on the Geographic Information System maps for the city of Rashid. The results have been classified into four categories in light of the (OUV) and ease of rehabilitation as a world heritage. Nonetheless, the results prove that the proposed strategy will increase the world heritage sites in Egypt. [ABSTRACT FROM AUTHOR]

The full utilization of solar energy resources along the road is an effective method to solve the energy shortage in transportation. The key to this is an accurate evaluation of solar energy resources, which provides the rationale for the optimal location of road photovoltaic (PV) projects. However, determining the availability of solar energy resources in road areas before route corridor planning remains difficult. To address the issue, this paper developed a standardized method. Firstly, this paper analyzed the critical factors affecting the availability of solar energy resources. They were meteorology, topography, land use type, geology, and location. On that basis, we developed a four-level evaluation indicator system. Then, it proposed a multi-indicator evaluation method based on the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). It used AHP to calculate the weights of each indicator, with solar radiation being the most important. It used GIS to analyze and overlay the spatial distribution of multiple indicators to obtain the map of suitability grades, which could be used to route corridor selection for road PV projects. It also used a case study to demonstrate how the method is applied. The findings show that the method can accurately evaluate the availability of solar energy resources. Furthermore, it studied the appropriate division of evaluation units and it is advised that they shouldn't be larger than 30 m × 30 m. These results provide guidance as a reference for the application of road PV projects and site selection for route corridors worldwide. That will promote the integration of transportation and energy in the future. • The paper addresses the problem of solar energy resources evaluation in road areas. • The paper establishes a universal evaluation indicator system. • The paper proposes a standardized evaluation method based on AHP and GIS. • This method presents the obtained evaluation results as suitability grade map. • This method helps select route corridors with good power generation potential. [ABSTRACT FROM AUTHOR]

With the continuous improvement of people's living standards, more and more people are pursuing spiritual satisfaction. Landscape planning and design need to adapt to people's needs based on the current situation. In response to the problems of slow landscape data processing speed and inaccurate landscape layout positioning in traditional public landscape spatial hierarchical planning systems, this paper would study the public landscape spatial hierarchical system based on Geographic Information System (GIS) technology, and use GIS technology to build a public landscape planning system to help better build public landscape. Through the experiment, it can be found that the use of GIS technology can effectively improve the speed of the system for landscape data processing and reduce the time required for processing. When the landscape data is 50GB, the processing time of the public landscape planning system based on GIS technology is 8.95 minutes, much faster than other systems. Based on GIS technology, urban public landscape design can be effectively implemented, helping to achieve a hierarchical layout of public landscapes during design. [ABSTRACT FROM AUTHOR]

Numerous computational tools for the simulation and design of emergency evacuation and egress are now available. Many evacuation models have been studied at different scales, from micro to macro models. To examine the problem in detail, the popular approach solicited is that of agent-based models (ABMs). ABMs take into account the heterogeneity of pedestrian behaviors and the unspecified conditions of the road network. However, the computational cost is enormous when applied to numerous evacuees. Coupled with ABM, the available Shapefile data can be used to develop simulation models to improve the analysis of spatial data and spatial processes. One such application concerns the evacuation of buildings in hazardous situations, where ABM is integrated with geographic information system (GIS) Shapefiles indoor spatial data to model humans during evacuation events and to simulate evacuation scenarios visualized in the Shapefiles. The research presented in this paper develops a multi-agent geosimulation model for building evacuation, integrating a Shapefile dataset of the case study building as input to ABM through the GAMA simulation platform. This model is intended to complement and enhance traditional approaches to building evacuation planning and management, such as earthquake and fire drills. The framework has been elaborated in such a way that it works for a wide range of scenarios, both in terms of hazards, geographical configurations, individual behaviors and crisis management. To demonstrate its adaptability, a real-life case study is presented concerning the evacuation of the Station NightClub from a fire. [ABSTRACT FROM AUTHOR]

• A spatiotemporal risk prediction of wildlife-vehicle collisions (WVCs) is presented. • A Bavarian dataset of around 731,000 accidents over a period of 10 years (2010–2019) was used. • Heterogenous datasets of impact factors are pre-processed using GIS. • Different machine learning (ML) approaches are applied - random forest, feedforward neural networks, and support vector machine classifier to predict the risk of WVCs. • ML algorithms provide high risk prediction quality of over 86% (double-weighted sensitivity and single-weighted specificity rate). • Spatial transferability and temporal forecast of WVC prediction was verified. Introduction: The technology in the automotive industry is becoming increasingly safer in the age of automated driving, but the number of accidents is still high, especially in wildlife-vehicle collisions (WVCs). To better avoid these accidents, patterns involved in these accidents must be detected. Method : This paper presents a spatiotemporal risk prediction of WVCs, including various road and environmental characteristics. A process of data preparation using GIS automated by Python scripts was developed to enable a spatiotemporal link of diverse features for the subsequent predictive data analysis. Different machine learning (ML) approaches were applied- random forest (RF), feedforward neural networks (FNN), and support vector machine classifier (SVM) - including automated ML to predict the risk of WVCs. Therefore, a dataset of approximately 731,000 accidents reported to the police in Bavaria over a period of 10 years (2010–2019) was used. In addition, non-accidents were randomly generated in Python over time and space for the supervised ML processes. As the actual risk probability for WVCs and non-WVCs is not entirely known, the impact of different training ratios between accidents and non-accidents was tested on the risk prediction quality (RPQ) (25%, 50%, 75%, 90% WVCs) of the double-weighted sensitivity and single-weighted specificity rate. Results: The test yielded high mean values of RPQ as an indicator for a suitable WVC prediction. Both RF (86.6%) and FNN (86.7%) were identified as suitable choices for WVC risk prediction in terms of RPQ. The SVM yielded a lower prediction quality, even though acceptable results could be achieved within a shorter runtime. Practical Applications: Spatial transferability was verified since the algorithm was trained on the dataset of Bavaria (excluding Upper Bavaria) and successfully tested in Upper Bavaria. WVC forecasts were also proven through training with datasets from 2010-2017 and in prediction for 2018 and 2019. [ABSTRACT FROM AUTHOR]

The earthquake and its secondary geological disasters have some long-term effects on vegetation recovery, and the spatiotemporal differentiation of vegetation coverage in earthquake-stricken areas are often not valued by researchers. Beichuan County in Sichuan Province is the worst-hit area during Wenchuan earthquake, tracking the vegetation coverage in this disaster area can provide feedback for post-earthquake ecological restoration. Based on remote sensing (RS) and Geographic information system (GIS), this paper relies on dimidiate pixel model (DPM) and vegetation coverage transfer matrix to explore the vegetation coverage of the earthquake worst-hit area. The results show that the overall level of vegetation coverage in Beichuan county is high, and earthquakes are dominant in the areas with high vegetation coverage. Besides, earthquake can do most severe damages to the areas with high vegetation coverage, followed by medium damage impacting to the areas with medium vegetation coverage, but the damage of other areas are relatively small. From 2007 to 2020, the average vegetation coverage in Beichuan County decreased from 0.835 in 2007 to 0.755 in 2008, then gradually recovered to 0.826 in 2020, experiencing a sharp decline and then a steady increase. The recovery of vegetation coverage is improved over time, but the spatiotemporal differences of the recovery process are obvious. Elevation has the largest driving force on vegetation coverage (q = 0.569, p less than 0.001), followed by temperature and rainfall; the interaction between factors has a significant increasement in the spatial differentiation of vegetation coverage, indicating that vegetation coverage is a combination result of multiple factors. The research findings can help construct the scientific management of the eco-environment in the disaster area, it can also help builda medium to long-term ecological restoration plan according to the variation in vegetation coverage. [ABSTRACT FROM AUTHOR]

The quantitative identification of rural land functions serves as a basis for rural land planning, coordinated spatial development, and control capacity improvement and is significant for rural transformation and sustainable development. This paper constructs an analysis framework to identify and evaluate "production-living-ecological" space (PLES) in rural areas of central and western China. On the Loess Plateau, which was taken as the study area, a combination of qualitative and quantitative analysis was completed to determine framework indicators and their thresholds. Resource and environmental carrying capacity and land suitability were measured through GIS spatial analysis under different land functional orientations. This sought to recognize production space (PS), living space (LS), and ecological space (ES) in rural areas, reveal their distribution patterns on the Loess Plateau, and determine alternative areas for future land use. The results indicate that the PS and LS of the Loess Plateau rural land show a relatively low resource and environmental carrying capacity but vary significantly in different functional regions. The space distribution of PS and LS with higher carrying capacity shows obvious consistency. Although the Loess Plateau has vast sustainable land for development, the intensity of development should be carefully controlled. Additionally, significant regional differences remain in rural PLES, so reasonable future rural land planning should be formulated according to the PLES pattern. This study provides a scientific basis for promoting coordinated development of Loess Plateau rural areas and implementing a rural revitalization strategy. It is also a reference for identifying PLES and its coordinated spatial development in rural central and western China. [ABSTRACT FROM AUTHOR]

Core domains to establish digital twins of the built environment are Building Information Modeling (BIM), Geographic Information System (GIS) and Internet of Things (IoT). RESTful Application Programming Interfaces (APIs) of these domains allow easy access to the digital twin. While multiple information models are reasonable since the focus of these domains is distinct, interoperability becomes a challenge. Therefore, in this paper we propose a hypermedia-driven RESTful API for digital twins of the built environment. While the GIS and IoT interfaces are covered by established standards, we define a RESTful interface for Industry Foundation Classes (IFC) models of the BIM domain. The APIs are interconnected with a federated approach though hypermedia navigation links. An evaluation by two use cases – a planning model and a structural health monitoring application – shows that the API can be used to navigate through the models crossing seamlessly the scope of the domains and overcoming barriers of interoperability. • Exposing IFC models and their items by a hypermedia-driven RESTful API. • Interacting with IFC models and their items using HTTP verbs (e.g., POST, DELETE). • Provisioning of GIM layers from IFC files through OGC API compliant endpoints. • Modeling sensors in IFC and applying a linked model approach to their observations. • Implementation of the API4BE server and client to access BIM, GIM and IoT endpoints. [ABSTRACT FROM AUTHOR]

Green innovation (GI) can increasingly contribute to green total factor productivity (GTFP) in the process of high-quality development transformation. However, the heterogeneous, spatial, and non-linear characteristics of GI are usually ignored when investigating its impact on GTFP. Using panel data on 275 Chinese cities from 2004 to 2022, two-way fixed, spatial Durbin, threshold, and mediation effect models are adopted to comprehensively examine how GI affects GTFP. The benchmark regression shows that GI can promote GTFP, and this result is maintained after robustness tests. The invention innovation is more likely to increase GTFP than green utility and green design innovations; furthermore, the enhanced impact driven by GI is primarily manifested via technological progress. GI can promote GTFP in neighboring regions through spillover effects under various spatial weight matrixes. Using the technological gap as the threshold variable, GI can promote GTFP through a gradually increasing feature. Furthermore, industrial upgrading, pollution reduction, and factor agglomeration are the three main mechanisms through which GI promotes GTFP. Based on the perspective of GI promoting sustainable development, the findings of this paper provide valuable recommendations for formulating GI and sustainable development policies. [ABSTRACT FROM AUTHOR]

This paper aims to make recommendations about the convenience of using low-noise pavements to mitigate traffic noise under different traffic and population scenarios. To do so, a life cycle approach has been applied by performing a Life-cycle Assessment (LCA) and Life-cycle Cost Analysis (LCCA). To account for the noise mitigation achieved by the road surfaces, which is the main benefit compared to conventional road surfaces, the damage effect of traffic noise has been incorporated in both analyses. Thus, open source noise emission and propagation modelling and Geographical Information System (GIS) tools have been used to determine the noise level at which residents are exposed. The impact of noise emissions on human health is estimated and expressed in disability-adjusted life years (DALYs) and the related external cost have been calculated by monetarizing the environmental impacts and by integrating them within the cost assessment. The methodology followed allowed the comparison of different scenarios and the results show a huge potential for reducing environmental impacts and costs when using low-noise road surfaces although accurate information about the acoustic aging of the materials is key in order to ensure that benefits are obtained. [Display omitted] • The feasibility of low-noise road surfaces has been evaluated combining LCA and LCCA. • Noise exposure is assessed using open-source GIS and noise modelling tools. • Population and traffic density influence low-noise surfaces viability. • Traffic noise impacts significantly outweigh other LCA/LCCA categories. • Acoustic ageing estimates of low-noise surfaces affect low-noise surfaces viability. [ABSTRACT FROM AUTHOR]

• State-of-the-art research tools and methodologies to assess environmental benefits. • Proposed methodology to identify habitat area change using global Copernicus data. • Translation of land use, land cover classes to habitat classification system. • ArcGIS toolbox developed and applied to Denmark and Netherlands study area. • Positive or negative effect evaluation of the measure from biodiversity response. Nature-Based solutions (NBS) are the measures supported by natural processes that can adapt to changing climates and generate diverse social, economic, and environmental benefits. Recognising the potential for additional NBS benefits, and quantifying these benefits is essential as it encourages decision-makers to implement and scale-up NBS initiatives. This paper presents findings from a systematic literature review. The review focused on tools and methodologies used for assessing the environmental benefits of implementing NBS. This review provides a detailed compilation of environmental indicators supported by assessment tools. It also includes a catalogue of tools for evaluating environmental benefits, thereby identifying research gaps. Moreover, this research proposes a methodology that uses an ArcGIS (Architecture of Geographic Information Systems) toolbox to identify habitat changes resulting from the implementation of NBS. The methodology translates CORINE (Coordination of Information on the Environment) land cover classes to EUNIS (European Nature Information System) habitat classes. The developed toolbox was applied to two case studies: Denmark (12 NBS) and the Netherlands (3 NBS). The assessment aimed to compare the habitat changes between 2000 and 2018 as two extreme time points for NBS implementation for both case studies. Results indicate that NBS implementation can change habitats leading to an increase in the Red-necked Grebe population in Denmark and a decline in the Black-tailed Godwit population in the Netherlands (two threatened species). The population change highlights the potential positive and potential negative impacts of NBS in their respective cases. These findings suggest Denmark could benefit from lake construction and restoration projects. At the same time, the Netherlands could invest in wetlands and meadows construction and restoration projects to protect the respective species. They could establish designated breeding zones to ensure their population does not decline rapidly. [ABSTRACT FROM AUTHOR]

Modern cities dynamically face several challenges including digitalization, sustainability, resilience and economic development. Urban planners and designers must develop urban forms that address these challenges. With the integration of new communication and information technologies (Smartphone, GIS, Drones, IoT, Sensors, etc.), urban activities have generated large volumes of urban data. The rapid growth in terms of collection and big data storage capacities combined with the ever-increasing computational power of modern machines have made possible their efficient treatment using machine (ML) and deep learning (DL) algorithms. The emergence of such groundbreaking methods has in turn helped to address the challenges of modern-day cities in several domains (health, security, mobility, etc). ML algorithms have been proposed to model the urban form's indicators for intelligent urban planning decision making. They have been proven to perform better than the traditional methods. However, the potential of ML has not yet been fully explored in research for urban planning decision support. This paper presents a comprehensive review of ML applications for mitigating the challenges of modern cities planning. First and foremost, an overview of the urban forms, sources of urban data, the ML and DL techniques as well as their potential in solving the aforementioned challenges. For each ML method, we will highlight it working principle, advantages, disadvantages and potential applications using comparative tables. Finally, we will discuss the issues and challenges of ML methods in urban form's modeling while ultimately advocating some future research directions. [ABSTRACT FROM AUTHOR]

Disaster waste management received increasing attention in recent years, but there was no review updating the evolving development after the study of Brown et al. (2011a). To explore how the topics in disaster waste management evolved in recent years and to analyze whether the gaps identified by Brown et al. (2011a) are covered, 82 papers published from 2011 to 2019 were selected from the Scopus database based on the defined process and criteria. This paper systematically examines the disaster waste management research from nine aspects of planning, waste, waste treatment options, environment, economics, social considerations, organizational aspects, legal frameworks and funding. The results suggested that there were no obvious changes or developments in the field of disaster waste management, although a few research gaps have been addressed, such as waste separation, waste quantities, case studies of incineration and waste to energy, direct economic effects, social considerations as well as application of GIS technology. Except for the comparative studies, future directions were suggested by the gaps that persist since Brown et al. (2011a) and the new gaps that were identified in this review. • A systematic review was done to review recent advances in disaster waste management. • There were no dramatic new developments in the field of disaster waste management. • Recent studies mainly focus upon technical aspects. • Planning, legislation, organizations and funding were not well addressed. • Future research directions are proposed. [ABSTRACT FROM AUTHOR]

Abstract Building information modelling (BIM) and geographical information systems (GIS) provide digital representation of architectural and environmental entities. BIM focuses on micro-level representation of buildings themselves, and GIS provide macro-level representation of the external environments of buildings. Moreover, their combination can establish a comprehensive view of a built environment based on data integrated, which underpins the development and transition of the architecture, engineering and construction (AEC) industries in the digital era. This paper gives a comprehensive review on BIM-GIS integration in sustainable built environments in order to analyse the status quo and practical applications from four viewpoints: technologies for data integration, applications in the life cycle of AEC projects, building energy management, and urban governance. Three typical modes of BIM-GIS integration, namely, "BIM leads and GIS supports", "GIS leads and BIM supports", and "BIM and GIS are equally involved", are categorised based on the different dominant positions of the two technologies. Furthermore, the research trends and future directions for the applications of BIM-GIS integration are discussed. Specifically, we underline that semantic models and third-party integration platforms should be optimised technically, and information about the whole process of AEC projects needs to be improved. Comprehensive information for building energy management should be digitised and quantified to improve its systematic integration and application to the urban built environment. This review can serve as a roadmap for researchers who focus on studies of BIM-GIS integration in the sustainable built environment. Graphical abstract This paper reviews the applications of BIM-GIS integration in the field of the sustainable built environment, analyses the status quo and practical applications of such integration, and discusses research trends for its future development. Firstly, relevant literature on BIM-GIS integration is collected, quantified and analysed according to multiple indicators such as journals and keywords. Secondly, the status and practical applications of BIM-GIS integration are described and analysed from four perspectives: data integration, the life cycle of AEC projects, building energy management and urban governance. Finally, research trends and prospects for the development of integrated BIM-GIS applications are discussed. This review aims to provide a roadmap for researchers who focus on studies of BIM-GIS integration in the sustainable built environment and to expand its range of applications in sustainable urban development. Unlabelled Image Highlights • A detailed roadmap for research on the integration of BIM and GIS is provided. • A bibliometric analysis is used to quantitatively review literature from multiple viewpoints. • A critical review is conducted from four major perspectives of BIM and GIS applications. • The limitations of existing studies and future research directions are discussed. [ABSTRACT FROM AUTHOR]

Highlights • Geospatial maps are used to estimate the charging load of electric vehicles in cities. • Three distinct charging profiles are assumed in the city: Home, Work, and Other. • Charging stations belong to a mixture of profiles depending on their nearby buildings. • Using 22 kW chargers resulted in a load with a peak of 1.47 kW/electric vehicle. • Fast charging causes high variability in the load when many cars start/stop charging. Abstract In the recent years, the number of electric vehicles (EVs) on the road have been rapidly increasing. Charging this increasing number of EVs is expected to have an impact on the electricity grid especially if high charging powers and opportunistic charging are used. Several models have been proposed to quantify this impact. Multiple papers have observed that the charging stations are used by multiple users during the day. However, this observation was not assumed in any previous model. Moreover, none of the previous models relied on geospatial maps to extract information about the parking lots—where charging stations are installed—and the charging profiles of the potential users of these charging stations. In this paper, a spatial Markov chain model is developed to model the charging load of EVs in cities. The model assumes three distinct charging profiles: Work, Home, and Other. Geospatial maps were used to estimate the charging profile, or mixture of profiles, of the charging stations based on the nearby building types. A case study was made on the city of Uppsala, Sweden—a city with approximately 44,000 cars. The results of the case study indicated that the aggregate load of the EVs in the city reduced the charging impact. For example when using 22 kW chargers, the peak load in the city per EV was estimated to be 1.29 kW/car in case of spatio-temporally opportunistic charging, and 1.47 kW/car in case of residential only opportunistic charging. This is to say that the Swedish grid operators can expect that every EV in the city will increase the peak load by at most 1.47 kW due to aggregation; this is assuming that 22 kW chargers were used. In addition, we showed that the minute-minute variability of the charging load in cities might cause some future challenges. In our case, up to 3% of the EVs in the city simultaneously started charging. This caused a one-minute-ramp in the charging load of 1.1 MW—if charging using 3.7 kW. Charging with higher powers will exacerbate these ramps, e.g., charging with 22 kW will cause sudden one-minute-increases as high as 6.7 MW in the charging load. Such a finding indicates that using high charging powers might cause high variability in the charging load of EVs in cities. This high variability might limit the synergy potentials between EVs and variable renewable energy sources (RES). The proposed model can potentially be used along with RES models to estimate the spatio-temporal synergy potentials between the two technologies. Evaluating the synergy potentials might be of value to grid operators, policy makers, market traders, etc. [ABSTRACT FROM AUTHOR]

Abstract This paper introduces an improved method to specify the potential areas at buildings' top surface for installation of photovoltaic (PV) power units in a defined urban area (UA). Additionally, optimum selection between various (PV) modules is addressed. The proposed approach is based on spatial data analysis and implementation of probabilistic approach (PA) in order compute the power capacity factor (CF) of the PV modules. According this estimation the module with highest average capacity factor is selected for installation at the defined UA. A dedicated case study is proposed and implemented through three main stages. In the first stage, the spatial data of studied buildings are analyzed based on the digitized SIR-DS using Google Earth imagery and ArcGIS software as a Geo-Model. Thus, the planner defines the potential areas for installing PV modules which linked with the buildings' database. In the second stage, various PV modules which produced by different manufacturers, are compared together based on the concept of the highest average CF estimated. In proceedings, firstly, a mathematical modeling of solar irradiance data-set (SIR-DS) is presented using statistical probability distribution function (PDF). These data are approved by Egyptian Meteorological Authority, and collected over a long-term period (7 years). Then, the most fitted PDF in matching with the measured data is then utilized to determine the average output power of each PV module. After that, the CF is estimated for all modules analyzed, such that the module with the highest average CF over the year is identified. Finally, the last stage integrates the results obtained from the prior stages. Accordingly, this paper introduces effective solution for the optimum selection between different PV modules at a given UA, in addition to specifying the potential areas for PV system installation which subjected to the studied buildings' database. [ABSTRACT FROM AUTHOR]

The paper focuses on the spatial distribution and influencing factors of China's newborn digital enterprises (NDEs). Based on digital enterprises in 287 cities across China from 2005 to 2020, the paper uses the negative binomial model coupled with Geographic Information System (GIS) maps methods. The results reveal that NDEs are concentrated in the east of the country and are more sparsely distributed in the center and west. The number of NDEs in different Chinese regions varies significantly with location choice featuring agglomeration characteristics. The empirical analysis confirms that new location factors are of growing importance to the location choice of NDEs while the impact of traditional factors is waning. We also report that NDEs prefer developed eastern cities, while location factors are less important in the center and west. [ABSTRACT FROM AUTHOR]

• Both static and dynamic characteristics of city are captured through the two-tier integration approach of BIM-GIS-IoT. • The concepts in the underlying data models of BIM, GIS and IoT are extracted and merged by ontology technology. • Brick and SSN ontologies are introduced to represent the underlying logics of IoT. • An integrated City Information Model ontology-OntoCIM proposed in this paper will play a fundamental role in digital twin city. With the development of digital city and smart city construction, the City Information Model (CIM) has played a critical role as a container of spatial–temporal data to establish the Digital Twin City. For a digital twin city, a virtual high-fidelity CIM model that corresponds closely to the real physical world is the premise and cornerstone of its construction. Therefore, the integration of BIM, GIS and IoT has become the preferred topic for researchers and has received much more attention from a wide academic circle. However, traditional integration mainly focuses on the conversion of both IFC and CityGML, and IoT data are also often used as visualizations. More importantly, the underlying data formats of GIS, BIM and IoT are still independent of each other without a unified data structure expression, so real data-driven analysis and decision-making cannot be implemented. This study aims to establish a general CIM ontology to integrate heterogeneous BIM, GIS and IoT data. First, the related work of BIM, GIS and IoT integration is studied and analyzed. A comparison of three mainstream approaches, data conversion, standard extension and data linking, is conducted, and it illustrates the advantages of ontology techniques in solving data interoperability problems. Second, a technical framework of BIM, GIS and IoT data integration based on ontology technology is proposed. The approach is mainly divided into five steps: geometry processing, data instantiation, ontology construction, ontology mapping and querying application. On the basis of the CIM ontology, an application ontology is built for a specific application domain to illustrate rule-based mapping, querying and inferring. Finally, the case study shows that the Ontology-based methodology in this paper has contributed to establish a general pattern for CIM data integration by mapping and linking concepts from the semantic level. It avoids changes in the original data sources and the missing data problem. [ABSTRACT FROM AUTHOR]

The information system of low-voltage distribution network correctly records the topological structure of distribution network, which is the premise of fine management and safe operation of electric power grid. In the topology, the most important element is the household change relationship [1]. At present, the low-voltage distribution network topology data in the information system is manually entered, so the correctness of the information can not be guaranteed. With the continuous expansion of the scale of power grid, the structure of low-voltage distribution network is complex, the amount of data of marketing measurement [2] , GIS and other information systems is increasing rapidly, each information system operates independently, and the data circulation is poor, which makes it difficult to identify the topology data, so it is urgent to carry out topology verification. The method proposed in this paper effectively solves the problem of electric data verification, and carries out corresponding experiments and demonstrations on the results, with high accuracy. [ABSTRACT FROM AUTHOR]

This paper uses a combination of the contingent valuation method (CVM) and value transfer (VT) to estimate the value of non-market benefits associated with the achievement of good (marine) environmental status (GES) as specified in the EU Marine Strategy Framework Directive (MSFD) for Atlantic member states. The increased use of geographic information systems in VT means that many VT exercises now include spatial elements such as distance decay and population density. This paper explores impact of distance decay on welfare estimates as well as the impact from the modifiable area unit problem (MAUP) when population density is included as an explanatory variable. These issues can have a large effect on a VT estimate. In this study the overall value for achieving GES for Atlantic member states varied between €2.37billion and €3.64 billion. It was found that the different distance decay specifications changed values between −3% and 82% with a mean absolute difference of 25% and by adjusting the spatial scale in an effort to overcome the MAUP changed aggregate values between 13% and 25% with a mean of 17%. [ABSTRACT FROM AUTHOR]

In recent years, CubeSats have considerably extended their range of possible applications, from a low cost means to train students and young researchers in space related activities up to possible complementary solutions to larger missions. Increasingly popular, whereas CubeSats are still not a solution for all types of missions, they offer the possibility of performing ambitious scientific experiments. Especially worth considering is the possibility of performing Distributed Space Missions, in which CubeSat systems can be used to increase observation sampling rates and resolutions, as well as to perform tasks that a single satellite is unable to handle. The cost of access to space for traditional Earth Observation (EO) missions is still quite high. Efficient architecture design would allow reducing mission costs by employing CubeSat systems, while maintaining a level of performance that, for some applications, could be close to that provided by larger platforms, and decreasing the time needed to design and deploy a fully functional constellation. For these reasons many countries, including developing nations, agencies and organizations are looking to CubeSat platforms to access space cheaply with, potentially, tens of remote sensing satellites. During disaster management, real-time, fast and continuous information broadcast is a fundamental requirement. In this sense, a constellation of small satellites can considerably decrease the revisit time (defined as the time elapsed between two consecutive observations of the same point on Earth by a satellite) over remote areas, by increasing the number of spacecraft properly distributed in orbit. This allows collecting as much data as possible for the use by Disaster Management Centers. This paper describes the characteristics of a constellation of CubeSats built to enable access over the most remote regions of Brazil, supporting an integrated system for mitigating environmental disasters in an attempt to prevent the catastrophic effects of natural events such as heavy rains that cause flooding. In particular, the paper defines the number of CubeSats and the orbital planes required to minimize the revisit time, depending on the application that is the mission objective. Each CubeSat is equipped with the suitable payloads and possesses the autonomy and pointing capabilities needed to meet the mission requirements. Thanks to the orbital features of the constellation, this service could be exploited by other tropical countries. Coverage of other areas of the Earth might be provided by adjusting the number and in-orbit distribution of the spacecraft. [ABSTRACT FROM AUTHOR]

Systematically assessing the influence of new urban plans is an important challenge for designing them efficiently. In this paper, we propose a method to assess the influence of local ‘Right to Build’ regulations on constructibility. Our method is based on an optimization algorithm that generates building configurations. This method requires a geographic model that supports the formalization of the Right to Build regulation in order to check if a building respects it. The proposed approach relies on the trans-dimensional simulated annealing optimization method, which produces building configurations composed of a set of parametric objects (boxes in our implementation). Our proposition is released as the SimPLU3D Open-Source project ( http://ignf.github.io/simplu3D/ ). In this paper, we present some tests and results based on this implementation and a use related to the assistance to ‘Right to Build’ designers. [ABSTRACT FROM AUTHOR]

In the context of considerable change in the use of groundwater pattern, particularly with continuously increasing demand for groundwater due to growing population, expansion of area under irrigation and economic progress, the present paper makes an attempt to delineate groundwater potential zones using integrated remote sensing, geographic information system, and analytic hierarchy process techniques. Integration of geographic information system with analytic hierarchy process can exemplify as a process that transforms and harmonizes geographical data and weightage ranking to retrieve information for accurate decision-making. Accordingly, mapping and identification of groundwater potential zones are carried out in the Ganga Alluvial Plain of Hooghly district of India. Application of the same for Indo-Gangetic plain is made (new approach) to contribute the applicability Geographic Information System and Analytic Hierarchy Process for the delineation of groundwater potential zone. Predominant criteria (e.g., land use, land cover, soil type, geomorphology, geology, elevation, slope, rainfall, normalized difference vegetation index, drainage density, recharge rate, groundwater depth) were employed for computation of groundwater potential index. Overlay weighted sum method is applied to integrate all thematic criteria to generate groundwater potential zone map of the study area. The resulting groundwater potential index map has been classified into three groundwater potential zones, namely good, moderate and poor. Finally, groundwater potential zone map is validated using average groundwater level data from 32 wells scattered over the study area. The findings of the present paper have important implications for designing sustainable groundwater plan in the area. [ABSTRACT FROM AUTHOR]

Searching for a near-optimal highway alignment is essential to achieve safe, efficient, and cost-effective transportation infrastructure. The challenges with the traditional design approach lie in its reliance on geometric elements which may lead to alignments that are complex, financially burdensome, and environmentally disruptive despite exploring a wide search area. This paper presents a novel and intelligent approach for highway alignment optimization, focusing on the development of the alignment using station points within a reduced domain. The approach involves utilizing a pre-generated Least Cost Path from a Geographic Information System (GIS) model, serving two purposes: first, determining the reduced search domain, and second, using it as input for genetic algorithms (GAs) until a near-optimal alignment is achieved. This approach simplifies the design process, reduces costs, and yields alignments that better harmonize with various relevant factors. The integrated GIS-GA model of this study helped reduce the whole search space to a confined tubular square corridor (CTSC), where the best alternative exists. Additionally, the method introduced a new perspective on highway alignment design, challenging conventional practices. The model is validated using comprehensive and sensitive scenarios for key parameter values. The results reveal that the search time is significantly reduced and that better solutions are located in real-time. This methodology has the potential to revolutionize the way transportation engineers and planners approach highway alignment projects, resulting in more efficient, well-informed, cost-effective, and context-sensitive designs. Further studies are highly recommended to fully explore the implications and applications of the proposed methodology. • Highway alignment development concerns researchers, aiming for optimal solutions, reducing time/cost, and lowering construction expenses. • This project reports the reduction of the search space from the whole to a CTSC, where optimal highway alignment exists in 3D space. • A problem oriented GIS model was integrated with a Genetic Algorithm Model into a single one to address the development of an optimum highway alignment solution in real time. • The resulting alignment was configured via a novel approach using station points along its length in 3D, rather than traditional highway design elements. • The resulting optimum highway alignment was promising, validating space reduction, cutting processing time, and improving solution quality. [ABSTRACT FROM AUTHOR]

[Display omitted] • A single tree carbon storage model was establish based on shelterbelt age. • The tree numbers were detected by high resolution remote sensing image and geographic information system (GIS) methods. • A new model was proposed to estimate the carbon storage of farmland shelterbelt on the regional scale based on the single tree carbon storage and tree numbers. The carbon storage potential of planted shelterbelts was gradually recognized in recent years, but there was a lack of shelterbelt knowledge for carbon inventory analyses because of their narrow linear feature and extensive distribution. Considering its own characteristics of the shelterbelt, this paper proposed a new remote sensing model for estimating the carbon storage of farmland shelterbelt (CSBelt) based on the single tree carbon storage and tree numbers. In this model, the single tree carbon storage model was established by shelterbelt age, which could be identified from time series images. Tree numbers of each shelterbelt, as another important parameter in the CSBelt model, was detected by numbers of rows, columns, and preservation rate, which were monitored by high resolution remote sensing image and geographic information system method. Finally, overall accuracy (OA), mean absolute error (MAE), and mean error (ME) was used to validate the remotely identified data. The results showed that, the OA of the single tree carbon storage was 80.4% within the error range of 0.05 MgC/tree, the MAE was 0.036 MgC/tree, and ME was 0.020 MgC/tree; the OA of tree numbers was 71.7% within the error range of 100 trees/km, the MAE was 94 trees/km, and ME was 73 trees/km; the OA of shelterbelt carbon storage was 82.6% within the error range of 100 MgC/km, the MAE was 49.63 MgC/km, and ME was 33.78 MgC/km. Compared to other carbon storage models, this model requires fewer parameters and is not restricted by the stand conditions, which has better universality and realizability. This research is helpful for the accurate assessment of shelterbelt ecosystem carbon storage, which is of great significance for predicting climate change and formulating strategies. The results of this research will also provide data support for shelterbelt management on a regional scale. [ABSTRACT FROM AUTHOR]

This paper satisfies the requirements for reliable and inexpensive congestion detection in urban road networks. The objective of this research is to use fuzzy logic to detect the traffic conditions states based on sets of rules that compare the filed traffic states. An alternative approach that enables knowledge based on effective and efficient methods of detecting traffic congestion. The main strategies in this work include the detection of congestion based on index measures of traffic speed, index speed reduction, and speed ratio. Spatial analysis of traffic data utilizing ArcGIS application to produce digitized street maps of congestion assessments through GIS traffic data. Utilizing the Fuzzy Inference System (FIS) approach for adopted traffic parameters provides an analytical solution for ambiguous and uncertain problems. The categories of traffic input parameters distinguish states of congestion levels through the determination of values of congestion index levels. The analysis of the speed reduction index illustrated the hot spots within the study network which represent Bab Al-Moathum zone; Al-Mawal (Mustansiriyah University zone); Al-Kindi government hospital induced heavy congestion with an index range of (0.651–0.717). A precise threshold to describe the level of congestion using the Fuzzy Inference System to evaluate urban streets into different categories; Free-flow, Normal, Moderate, Heavy congested, and Blocked. A contribution of two input traffic parameters is considered to quantify congestion in one output that combines different congestion field measures. Over 15 links (Bab Al-Moathum zone, Palestine Street near Mustansiriyah University, Al-Kindi govern ate hospital with black color induced worse traffic conditions, resulting in a blocked effect. The rest of the segments within the case study range from heavily congested to normal. A more realistic and detailed view of traffic congestion for selected street networks is obtained based on a fuzzy inference system as to traditional methods that consider one parameter for traffic performance. [ABSTRACT FROM AUTHOR]

Ongoing climate change has increased the impact of landslides and related slope disasters on infrastructure and human lives. Microwave satellite remote sensing, particularly interferometric analysis of synthetic aperture radar (InSAR) data, is a powerful tool for routine monitoring of the displacement of slopes on small wavelength scales, independently from solar illumination and cloud coverage. Although various sophisticated techniques for time-series InSAR have been developed, practical application in conjunction with geological analysis to reveal intrinsic and triggering factors of slope displacement is still limited. In this paper, we describe a practical case of time-series InSAR analysis with a special focus on its geological implications in northwestern Kyushu, a high-risk area for slope-related disasters in Japan. The extracted susceptible polygons from the InSAR results show accuracy comparable with that of other recent research on landslide mapping. Intrinsic factor analysis based on a geographic information system reveals that the displacement occurs on relatively gentle slopes instead of steep areas, corresponding to the transient zone of Paleogene-Neogene sedimentary rocks and basalt. Triggering factor analysis based on correlation coefficients reveals a significant link between some displacement areas with mean and/or maximum precipitation for each observation duration. Those findings confirm the importance of carefully accounting for the geological background for landslide susceptibility assessment and policy making, apart from topographic and meteorological conditions. [ABSTRACT FROM AUTHOR]

Geographic Information Science (GIScience) technology has been used to document, investigate, and predict patterns that may be of utility in both forensic academic research and applied practice. In examining spatial and temporal trends of the mass disaster that is occurring along the U.S.-Mexico border, other researchers have highlighted predictive patterns for search and recovery efforts as well as water station placement. The purpose of this paper is to use previously collected spatial data of migrant deaths from Arizona to address issues of data uncertainty and data accuracy that affect our understanding of this phenomenon, including local and federal policies that impact the U.S.-Mexico border. The main objective of our study was to explore how the locations of migrant deaths have varied over time. Our results confirm patterns such as a lack of relationship between Border Patrol apprehensions and migrant deaths, as well as highlight new patterns such as the increased positional accuracy of migrant deaths recorded closer to the border. This paper highlights the importance of using positionally accurate data to detect spatio-temporal trends in forensic investigations of mass disasters: without qualitative and quantitative information concerning the accuracy of the data collected, the reliability of the results obtained remains questionable. We conclude by providing a set of guidelines for standardizing the collection and documentation of migrant remains at the U.S.-Mexico border. [ABSTRACT FROM AUTHOR]

This paper presents a model for the realistic planning of multi-microgrids in the context of Active Distribution Networks with the assistance of Geographic Information Systems. The model considers the distribution system grid as well as the geographic features of the Region of Interest. It also includes long-term purchase decisions and short-term operational constraints, and considers uncertainties associated with electricity demand and Renewable Energy Resources using an existing Two-Stage Stochastic Programming approach. Geographic Information Systems along with Deep Learning are used to estimate the areas of rooftops within the Region of Interest and model the Low Voltage grid. The planning model is used to study the feasibility of implementing a multi-microgrid system consisting of 4 individual microgrids at an Active Distribution Network in a municipality in the state of São Paulo, Brazil. The results of the model presented in this paper are compared with the results obtained using Monte Carlo Simulations and an existing, less detailed, Two Stage Stochastic model. It is demonstrated that the stochastic solutions are close to those obtained with Monte Carlo at a lower computational cost, and that the use of Geographic Information allows to determine both the capacity and location of the PV panels, batteries, and distribution transformers on the microgrids grid, thus providing more precise and useful planning results. • Geographic Information (GI) -based model for the planning of Multi-microgrids (MMGs). • An existing Two-Stage Stochastic Programming (TSSP) model is used. • Uncertainties in demand and solar generation are considered for study and analysis. • GI allows the modeling of the Low Voltage (LV) distribution network. • Results show where rooftop solar panels and batteries can be physically deployed. [ABSTRACT FROM AUTHOR]

The quantitative estimation of changes in terrain surfaces caused by water erosion can be carried out from precise descriptions of surfaces given by means of digital elevation models (DEMs). Some stages of water erosion research efforts are conducted in the laboratory using rainfall simulators and soil boxes with areas less than 1 m 2 . Under these conditions, erosive processes can lead to very small surface variations and high precision DEMs are needed to account for differences measured in millimetres. In this paper, we used a photogrammetric Structure from Motion (SfM) technique to build DEMs of a 0.5 m 2 soil box to monitor several simulated rainfall episodes in the laboratory. The technique of DEM of difference (DoD) was then applied using GIS tools to compute estimates of volumetric changes between each pair of rainfall episodes. The aim was to classify the soil surface into three classes: erosion areas, deposition areas, and unchanged or neutral areas, and quantify the volume of soil that was eroded and deposited. We used a thresholding criterion of changes based on the estimated error of the difference of DEMs, which in turn was obtained from the root mean square error of the individual DEMs. Experimental tests showed that the choice of different threshold values in the DoD can lead to volume differences as large as 60% when compared to the direct volumetric difference. It turns out that the choice of that threshold was a key point in this method. In parallel to photogrammetric work, we collected sediments from each rain episode and obtained a series of corresponding measured sediment yields. The comparison between computed and measured sediment yields was significantly correlated, especially when considering the accumulated value of the five simulations. The computed sediment yield was 13% greater than the measured sediment yield. The procedure presented in this paper proved to be suitable for the determination of sediment yields in rainfall-driven soil erosion experiments conducted in the laboratory. [ABSTRACT FROM AUTHOR]

Air pollutions from transportation sector have become a serious urban environmental problem, especially in developing countries with expending urbanization. Cleaner technologies advancement and optimal regulation on the transporting behaviors and related design in infrastructures is critical to address above issue. To understand the spatial and temporal emissions pattern within transportation lays the foundation for design on better infrastructures and guidance on low-carbon transportation behaviors. The feasibility of Global Positioning System (GPS) and emerging big data analysis technique enable the in-depth analysis on this topic, while to date, applications had been rather few. With this circumstance, this paper analyzed the taxi's energy consumption and emissions and their spatial-temporal distribution in Shanghai, one of the most famous mega cities in China, applying big data analysis on GPS data of taxies. Spatial and temporal features of energy consumptions and pollutants emissions were further mapped with geographical information system (GIS). Results highlighted that, spatially, the energy consumption and emission presented a distribution of dual-core cyclic structure, in which, two hubs were identified. One was the city center, the other was Hongqiao transport hub, the activities and emission was more concentrated in the west par of Huangpu River. Temporally, the highest activity and emission moment was 9–10AM, the second peak occurred in 7–8PM, which were both the traffic rush period. The lowest activity/emission moment was 3–4AM. Causal mechanism for such distribution was further investigated, so as to improve the driving behaviors. Through the exploration of spatial and temporal emissions distribution of taxis via big dada technique, this paper provided enlightening insights to policy makers for better understanding on the travel patterns and related environmental implications in Shanghai metropolis, so as to support better planning on infrastructures system, demand side management and the promotion on low-carbon life styles. [ABSTRACT FROM AUTHOR]

GIS modeling of environment is a complex process of collecting, processing, organization, storage and access on huge amount of data which represents certain part of reality (space). Database, formed in this way, is applicable for analysis, forecasting and decision making about activities and aims related to the environment. This paper, in spite of their undoubted advantages and importance, focuses on some deficiencies of GIS. Those deficiencies could appear as a consequence of fact that analysis of dynamic reality is made on the base of historical data, which consequently means that available data represent the past i.e. they are not, or they are only partially adequate to present state of the considered reality. In the same time, the collected data contains particular errors, which could influence quality of decision related to activities connected with environment, feasibility and sustainability of technical solutions. Errors especially could influence the sensitive and rare resources as freshwaters because of increasing demands and limited available sources. This paper deals with GIS modelling error of environment influence on waterpower engineering. [ABSTRACT FROM AUTHOR]

Supply chain operations with sustainability considerations have become an increasingly important issue in recent years and location planning for sustainable development plays an important role in guiding future of local, regional and national systems. Geographic information system is a technology for making better decisions about location. To solve location planning problem, in this paper, a new preference aggregation algorithm using complementary slackness condition and discriminant analysis is developed. Applying a voting system for solving sustainable location planning problem is new and cannot be found in literature. Using geographic information system and factor analysis, a multiple attribute decision making problem is developed. In this paper, multiple attribute decision making problems are solved by our proposed method for ranking a voting system and also the sustainable location is obtained. A case study demonstrates efficiency of proposed method. For this purpose, the proposed method is used to determine the most appropriate locations for constructing agro-industries in Markazi province. In our real application, ten main criteria using obtained variances and eigen values are recognized. Seven locations are selected and ranked. Results show that Komain is the best location for constructing agro-industry and Komijan is the worst location. [ABSTRACT FROM AUTHOR]

The present paper proposes an automatic procedure in GIS providing maps which suggest the more appropriate and feasible intervention type to mitigate landslide risk. These maps are not intended for replacing the designer engineering judgment, nor his work, but to help him in the choice and to help the territorial protection agencies in planning interventions and allocating funds. In order to make available these results to technical staff of public administrations, designers and insurance companies, operating in landslide risk management, the maps referred to Genoa have been published, as an example, on a webGIS site. In the paper, the proposed methodology is described and validation cases are reported. [ABSTRACT FROM AUTHOR]

• The 3D scenes are created automatically with only open source Geographic Information Systems data. • Diverse and complex 3D intersections are generated automatically. • The 3D road surfaces are high-detailed, and they are in accordance with the code of civil engineering. • The semantic structure for 3D roads network can be used in traffic simulation. [Display omitted] How to automatically generate a realistic large-scale 3D road network is a key point for immersive and credible traffic simulations. Existing methods cannot automatically generate various kinds of intersections in 3D space based on GIS data. In this paper, we propose a method to generate complex and large-scale 3D road networks automatically with the open source GIS data, including satellite imagery, elevation data and two-dimensional(2D) road center axis data, as input. We first introduce a semantic structure of road network to obtain high-detailed and well-formed networks in a 3D scene. We then generate 2D shapes and topological data of the road network according to the semantic structure and 2D road center axis data. At last, we segment the elevation data and generate the surface of the 3D road network according to the 2D semantic data and satellite imagery data. Results show that our method does well in the generation of various types of intersections and the high-detailed features of roads. The traffic semantic structure, which must be provided in traffic simulation, can also be generated automatically according to our method. [ABSTRACT FROM AUTHOR]

Estimating the total wind power output from the meteorological information at a province level (called Provincial Regional Wind Power Conversion Model, PRWPCM) plays vital and fundamental roles in energy modeling community and regional wind power forecasting. How to construct a reliable PRWPCM is a real challenge, since PRWPCM involves a large number of widely distributed wind turbines, massive meteorological data across the whole province, and complex nonlinear correlations. This paper proposes a lightweight PRWPCM by integrating Geographic Information System (GIS) analysis technology and Convolutional Neural Network (CNN). First, we conduct the land suitability analysis for wind turbine sites through the multi-criteria GIS layer overlay method to make the provincial wind turbine land suitability map (WTLSM) with scored divisions from the least suitable to the most suitable areas. On this basis, a new fusion mechanism for geographic and meteorological information is proposed, through which the raw meteorological data matrix can be reconstructed to filter and amplify the meteorological information that is more relevant to the total wind power output of the province, and avoid the time-consuming and labor-intensive data collection and processing, large-size model construction and validation. Second, a CNN-based regression architecture is designed to further capture the mapping relationship between the reconstructed meteorological data and total wind power output of the province; each type of meteorological factor is considered as an input channel and the attention modules are introduced to adaptively enhance useful channels and suppress less useful ones. Numerical experiments based on the wind power operation data of Yunnan Province, China, are conducted to validate the superiority of the proposed PRWPCM via benchmarking against 13 classical methods. • Mapping relationship between wind power output and meteorological information at a province level is recognized by integrating GIS and CNN. • A new fusion mechanism for geographic and meteorological information is proposed to achieve lightweight modeling. • Comparative experimental studies are conducted in Yunnan province, China. [ABSTRACT FROM AUTHOR]

Stormwater green infrastructure (GI) has been applied as a method to address urban flooding for over forty years. However, GI has not yet been widely utilized across the US. Prior studies identified some challenges with technical, engineering, and socioeconomic aspects of GI, but detailed examination of non-scientific papers to identify and analyze real-world barriers that may hinder the implementation of GI has not yet been done. To achieve this goal, we conducted a meta-analysis of 351 public documents from federal, state, and 62 municipalities in Eastern Pennsylvania to systematically review the support of different levels of government for implementing GI. We summarized barriers in three categories: 1) governance and policy: failure to integrate GI into existing stormwater management policies due to unclearly defined responsibility and infrequent policy updates, 2) stormwater fees and credits: inequity in stormwater fee structures and debate of stormwater credits connected to GI, and 3) public education and outreach: most municipal governments provide little or no stormwater and GI-related information to their residents, but some municipalities with higher population density have tried to offer more. These barriers will restrain the original intention and vision of GI implementation and cause difficulties in effectively conveying GI's information from the federal and state levels to municipalities and residents. Our discussion highlights these difficulties of GI implementation at all governmental levels and shed light on potential solutions to address these barriers. • Barriers to Green Infrastructure (GI) adoption in the US context are identified. • Qualitative and quantitative methods are conducted to identify GI barriers. • Three categories of GI barriers are summarized at different levels of government. [ABSTRACT FROM AUTHOR]

• An approach that assists policymakers for more equitable decisions regarding UGS. • Incorporate population activity intensity effect to analyze UGS equity. • Offer insights into UGS renewal by assessing cost to UGS updates. • Advances in understanding UGS dynamics for more sustainable urban environment. Urban green space (UGS) plays a pivotal role in enhancing citizens' living standards, well-being, and overall physical and mental health. As urbanization continues to progress, ensuring equitable access to UGS becomes increasingly vital in addressing the social resource disparities. This paper proposes an innovative approach to inform UGS planning and updates, placing a strong emphasis on equitable UGS distribution. In pursuit of a more balanced UGS layout, we present a novel recreational demand model that can evaluate the citizen's recreational preferences by taking into account the population activity levels and the agglomeration effect. Leveraging point of interest (POI) data within the geographic information system (GIS) platform, we quantitatively analyze these factors. Additionally, we propose a refined resistance model, enhancing the accuracy of UGS update cost estimation. Applying these models, we undertake a case study in Changsha City to advance UGS update strategies. Our findings reveal disparities not only across different city sectors but also within individual spaces, underscoring the unequal distribution of diverse UGS resources. The proposed models can effectively guide the incorporation of new UGS points in urban planning, mitigating the challenge of inequitable UGS resource allocation. The study provides a new approach to more comprehensively evaluate the UGS quality and offers additional valuable insights for improved UGS planning, and therefore, contributes to fostering a more just, inclusive and sustainable urban environment. [ABSTRACT FROM AUTHOR]

During major floods, rivers erode their banks and thus recruit large wood pieces from the riparian zones. There is still a lack of knowledge about the transport of large wood, the volumes involved and the flux distribution, i.e. the large wood connectivity at catchment scale. During storm Alex (October 2020), the French Roya catchment (394 km2) experienced a paroxysmal morphogenic flood involving massive bank erosion. The riparian vegetation was largely recruited, with large wood contributing to logjams and bridge destruction. This paper presents a methodology for volumetric assessment of the large wood fluxes involved. Simple approaches are used to (i) quantify the inputs from stand density data from the national forest inventory and from source areas based on diachronic analysis of active channels highlighting the erosion of 87 ha of wooded areas; and (ii) quantify the volumes deposited via an exhaustive manual digitisation of 16,846 pieces of large wood deposited on 59 km of channels on the Roya and its tributaries. This catchment-scale, large wood connectivity analysis shows that the flood recruited and transported downstream a volume of around 14,000 m3 of large wood (uncertainty range: 7000–29,500 m3). Drone observations of the Roya River mouth in Italy and satellite images showing a raft of driftwood, several km long, drifting off the Roya River mouth in the aftermath of the flood corroborate our findings. • Storm Alex (Oct. 2020) triggered dramatic geomorphic changes in the Roya River (FR). • We quantified the volume of large wood (LW) eroded, conveyed and deposited. • Large wood production is deduced from eroded forest maps and forest stand density. • Large wood deposition is estimated by digitizing logs on aerial pictures. • Circa 14,000 m3 of LW were recruited, 900 m3 deposited, the rest went to the sea. [ABSTRACT FROM AUTHOR]

Use of biomass for energy purposes has many advantages, but it is not always profitable due to the characteristics of biomass. In order to improve the utilization of biomass it is necessary to create optimal conditions which require employment of several methods and technologies. This paper describes developed approach for consideration of options for the optimal utilization of available biomass potential under the current conditions (technological, economic, environmental and social). The developed approach is focused on support for decision making process on community level. Proposed approach includes mapping of biomass potential with defining both potential primary storage locations and potential locations of plant using geographic information system technologies. Also, it includes determining the optimal amount of biomass that can be justifiably used for energy purposes under current conditions by using the mathematical optimization with objective function related to maximizing net present value quotient. Four groups of technologies that can be used for biomass valorisation have been considered in the paper; biofuels production, thermal power plants, district heating and cogeneration. The proposed approach has been applied (as a case study) on Municipality of Ivanjica located in South-West region of Serbia. Municipality of Ivanjica is characterized by both significant forest area and significant forest biomass potential. There are 30 potential primary storages locations and 57 potential plants locations determined by geographic information system. However, only one of potential locations of plant and two of potential primary storage locations (6% of available potential) are determined by mathematical optimization as optimal and could be used under current conditions. [ABSTRACT FROM AUTHOR]

Increasing levels of urbanization, combined with growing populations and a need to manage urban redevelopment more sustainably has prompted the need for new tools for urban regeneration in established urban areas. While significant activity is occurring in the areas of volumetric analysis and 3D visualization, utilising these technologies in the development of urban planning tools requires a data schema for defining precinct objects for performance assessment while simultaneously addressing the complexity and interconnected nature of issues relevant to the urban built environment. This paper presents the outcomes of the research and development of a web-based 3D precinct visualization and assessment system, Envision Scenario Planner (ESP), which uses a library of housing typologies to generate easy-to-use, bottom-up, precinct-scale reports on residential infill. The paper illustrates how, through the specification of a residential precinct object data schema and the provision of a set of housing typologies, end users can quickly, and without domain knowledge, generate visualizations and assessments for a variety of housing scenarios, which allows them to determine fit-for-purpose solutions that address a range of issues relevant to contemporary planners and policy makers. [ABSTRACT FROM AUTHOR]