A data model for operational and situational information in emergency response: the Dutch case

During emergency response a lot of dynamic information is created and needs to be studied and analysed in the decision-making process. However, this analysis of data is difficult and often not possible. A major reason for this is that a lot of information coming from the field operations is not archived in a structured way. This paper presents a data model for the management of dynamic data, which captures the situational information (incident and its effect) and the operational information (processes activated and people/departments involved). The model is derived from the emergency response procedure and structural organisation in the Netherlands

Models of Dynamic Data for Emergency Response: A Comparative Study

The first hours after a disaster happens are very chaotic and difficult but perhaps the most important for successfully fighting the consequences, saving human lives and reducing damages in private and public properties. Despite some advances, complete inventory of the information needed during the emergency response remains challenging. In the last years several nationally and internationally funded projects have concentrated on inventory of emergency response processes, structures for storing dynamic information and standards and services for accessing needed data sets. A good inventory would clarify many aspects of the information exchange such as data sets, models, representations; a good structuring would facilitate the fast access to a desired piece of information, as well as the automation of analysis of the information. Consequently the information can be used better in the decision-making process.\ud This paper presents our work on models for dynamic data for different disasters and incidents in Europe. The Dutch data models are derived from a thorough study on emergency response procedure in the Netherlands. Two more models developed within the project HUMBOLDT reflect several cross border disaster management scenarios in Europe. These models are compared with the Geospatial Data Model of the Department of Homeland Security in USA. The paper draws conclusions about the type of geographical information needed to perform emergency response operations and the possibility to have a generic model to be used world-wide

NAVIGATION IN INDOOR VOXEL MODELS

The paper proposes to use voxel models of building interiors to perform indoor navigation. The algorithms can be purely geometrical, not relying on semantic information about different building elements, such as floors, walls, stairways etc. Therefore, it is possible to use voxel models from different data sources, in addition to vector-to-raster conversions. The paper demonstrates this on the basis of tree different input types: hand measurements, point clouds and images of floorplans. On the basis of these models, the paper shows how to determine the navigable space in a voxel model for a pedestrian actor, and how to compute paths from arbitrary sources to specified destinationsScopu

EXCASAFEZONE

Excavation work takes place almost continually in most cities around the Western hemisphere. Many cities are already full of infrastructures, buried networks, and street furniture, so excavation work is not without any thread to the operator and surrounding environment. Small construction sites, for example, are often constrained by operating infrastructure on surface level and underground. Although different agencies and network owners have information about the location of the objects that put excavation work at risk, this information is not centralized. Different organizations manage location information of buried cables, unexploded ordnance, and pollution, for example. This significantly complicates the early-stage planning and last minute risk assessment processes because professionals need to manually collect, assess, and integrate data about subsurface objects into a comprehensive risk assessment. To smoothen this process, ExcaSafeZone project, therefore, develops a system that collects location data, defines expert-based rules for safety risk assessment, and that synthesizes this into an open source prototype that visualized safety risks on a heat map. &nbsp

Traffic Control Recognition with AN Attention Mechanism Using Speed-Profile and Satellite Imagery Data

Traffic regulators at intersections act as an essential factor that influences traffic flow and, subsequently, the route choices of commuters. A digital map that provides up-to-date traffic control information is beneficial not only for facilitating the commuters’ trips, but also for energy-saving and environmental protection. In this paper, instead of using expensive surveying methods, we propose an automatic way based on a Conditional Variational Autoencoder (CVAE) to recognize traffic regulators, i. e., arm rules at intersections, by leveraging the GPS data collected from vehicles and the satellite imagery retrieved from digital maps, i. e., Google Maps. We apply a Long Short-Term Memory to extract the motion dynamics over a GPS sequence traversed through the intersection. Simultaneously, we build a Convolutional Neural Network (CNN) to extract the grid-based local imagery information associated with each step of the GPS positions. Moreover, a self-attention mechanism is adopted to extract the spatial and temporal features over both the GPS and grid sequences. The extracted temporal and spatial features are then combined for detecting the traffic arm rules. To analyze the performance of our method, we tested it on a GPS dataset collected by driving vehicles in Hannover, a medium-sized German city. Compared to a Random Forest model and an Encoder-Decoder model, our proposed model achieved better results with both accuracy and F1-score of 0.90 for the three-class (arm rules of uncontrolled, traffic light, and priority sign) task. We also carried out ablation studies to further investigate the effectiveness of the GPS input branch, the image input branch, and the self-attention mechanism in our model

PREFACE

Abstract. Simply defined, a Smart City is a city overlaid by a digital layer, which is used for the governance of the city. A Smart City uses intelligent technology to enhance our quality of life in urban environments, bringing together people and data from disparate sources such as sensors, demographics, topographic and 3D mapping, Building Information Models and many more. Increasingly, Smart Cities use this data in a variety of ways, to address key challenges related to transportation, communications, air quality, noise, well-being of the citizens, decision making relating to education and health and urban planning, as well as in relation to initiatives such as startups and fostering economic growth and employment within the city. As more data becomes available, the challenges of storing, managing and integrating such data are also multiplied.The first Urban Data Management Symposium (UDMS) was held in 1971 in Bonn, Germany, made the choice of hosting the 6th international conference on Smart Data and Smart Cities (SDSC) in Stuttgart a very natural one. SDSC was established in 2016 as the successor of the UDMS, and this year we celebrate the 40th anniversary of the series of symposia and conferences. The SDSC 2021 will be part of the scientific week on intelligent cities at HFT Stuttgart. Together four events were held during the week of 14th – 17th September 2021, and alongside SDSC participants were invited to attend the "Energy, water and food for the cities of the future" conference, the "LIS-City – liveable, intelligent, and sustainable City" workshop, and the mobility day Stuttgart. Participant interaction – and the ability to attend sessions across the four events – was particularly encouraged. SDSC 2021 itself was organised by the Urban Data Management Society (UDMS www.udms.net), ISPRS and HFT Stuttgart (the University of Applied Science Stuttgart), and Professor Volker Coors Chaired the SDSC committee.As in previous years, three key conference themes were proposed to represent the Smart Cities: Smart Data (sensor network databases, on-the-fly data mining, geographic and urban knowledge modeling and engineering, green computing, urban data analytics and big data, big databases and data management), Smart People (volunteered information, systems for public participation) and Smart Cities (systems of territorial intelligence, systems for city intelligence management, 3D modeling of cities, internet of things, social networks, monitoring systems, mobility and transportation, smart-city-wide telecommunications infrastructure, urban knowledge engineering, urban dashboard design and implementation, new style of urban decision-making systems, geovisualization devoted to urban problems, disaster management systems).This volume consists of 18 papers, which were selected from 41 submissions on the basis of peer review. These papers present novel research concerning the use of spatial information and communication technologies in Smart Cities, addressing different aspects relating to Smart Data. Selected papers tackle different aspects of Smart Cities: transport, sustainable mobility; dashboards and web GIS; citizen engagement and participation; sensors; urban decision making.The editors are grateful to the members of the Scientific Committee for their time and valuable comments, which contributed to the high quality of the papers. Reviews were contributed by: Alias Abdul-Rahman, Giorgio Agugiaro, Ken Arroyo Ohori, John Barton, Martina Baucic, Filip Biljecki, Lars Bodum, Pawel Boguslawski, Azedine Boulmakoul, Matteo Caglioni, Caesar Cardenas, Eliseo Clementini, Volker Coors, Youness Dehbi, Abdoulaye Abou Diakité, Adil El Bouziri, Claire Ellul, Tarun Ghawana, Gesquiere Gilles, Didier Grimaldi, Ori Gudes, Stephen Hirtle, Martin Kada, Lamia Karim, Robert Laurini, Christina Mickrenska-Cherneva, Christopher Petit, Alenka Poplin, Ivana Racetin, Dimos Pantazis, Preston Rodrigues, Camilo Leon Sanchez, Genoveva Vargas Solar, Nils Walravens, Parag Wate, Besri Zineb, Sisi Zlatanova. We are also grateful to the work of the local organising committee at HFT Stuttgart, without whom this conference would not have been possible

Improving 3d pedestrian detection for wearable sensor data with 2d human pose

Collisions and safety are important concepts when dealing with urban designs like shared spaces. As pedestrians (especially the elderly and disabled people) are more vulnerable to accidents, realising an intelligent mobility aid to avoid collisions is a direction of research that could improve safety using a wearable device. Also, with the improvements in technologies for visualisation and their capabilities to render 3D virtual content, AR devices could be used to realise virtual infrastructure and virtual traffic systems. Such devices (e.g., Hololens) scan the environment using stereo and ToF (Time-of-Flight) sensors, which in principle can be used to detect surrounding objects, including dynamic agents such as pedestrians. This can be used as basis to predict collisions. To envision an AR device as a safety aid and demonstrate its 3D object detection capability (in particular: pedestrian detection), we propose an improvement to the 3D object detection framework Frustum Pointnet with human pose and apply it on the data from an AR device. Using the data from such a device in an indoor setting, we conducted a comparative study to investigate how high level 2D human pose features in our approach could help to improve the detection performance of orientated 3D pedestrian instances over Frustum Pointnet

PREFACE

Abstract. Simply defined, a Smart City is a city overlaid by a digital layer, which is used for the governance of the city. A Smart City uses intelligent technology to enhance our quality of life in urban environments, bringing together people and data from disparate sources such as sensors, demographics, topographic and 3D mapping, Building Information Models and many more. Increasingly, Smart Cities use this data in a variety of ways, to address key challenges related to transportation, communications, air quality, noise, well-being of the citizens, decision making relating to education and health and urban planning, as well as in relation to initiatives such as startups and fostering economic growth and employment within the city. As more data becomes available, the challenges of storing, managing and integrating such data are also multiplied.The first Urban Data Management Symposium (UDMS) was held in 1971 in Bonn, Germany, made the choice of hosting the 6th international conference on Smart Data and Smart Cities (SDSC) in Stuttgart a very natural one. SDSC was established in 2016 as the successor of the UDMS, and this year we celebrate the 40th anniversary of the series of symposia and conferences. The SDSC 2021 will be part of the scientific week on intelligent cities at HFT Stuttgart. Together four events were held during the week of 14th – 17th September 2021, and alongside SDSC participants were invited to attend the "Energy, water and food for the cities of the future" conference, the "LIS-City – liveable, intelligent, and sustainable City" workshop, and the mobility day Stuttgart. Participant interaction – and the ability to attend sessions across the four events – was particularly encouraged. SDSC 2021 itself was organised by the Urban Data Management Society (UDMS www.udms.net), ISPRS and HFT Stuttgart (the University of Applied Science Stuttgart), and Professor Volker Coors Chaired the SDSC committee.As in previous years, three key conference themes were proposed to represent the Smart Cities: Smart Data (sensor network databases, on-the-fly data mining, geographic and urban knowledge modeling and engineering, green computing, urban data analytics and big data, big databases and data management), Smart People (volunteered information, systems for public participation) and Smart Cities (systems of territorial intelligence, systems for city intelligence management, 3D modeling of cities, internet of things, social networks, monitoring systems, mobility and transportation, smart-city-wide telecommunications infrastructure, urban knowledge engineering, urban dashboard design and implementation, new style of urban decision-making systems, geovisualization devoted to urban problems, disaster management systems).This volume consists of 14 papers, which were selected from 41 submissions on the basis of double blind review, with each paper being reviewed by a minimum of three reviewers. These papers present novel research concerning the use of spatial information and communication technologies in Smart Cities, addressing different aspects of Smart Data and Smart Citizens. The selected papers tackle different aspects of Smart Cities: 3D; Citizen Engagement; transport, sustainable mobility; dashboards and web GIS; citizen engagement and participation; sensors; urban decision making.The editors are grateful to the members of the Scientific Committee for their time and valuable comments, which contributed to the high quality of the papers. Reviews were contributed by: Alias Abdul-Rahman, Giorgio Agugiaro, Ken Arroyo Ohori, John Barton, Martina Baucic, Filip Biljecki, Lars Bodum, Pawel Boguslawski, Azedine Boulmakoul, Matteo Caglioni, Caesar Cardenas, Eliseo Clementini, Volker Coors, Youness Dehbi, Abdoulaye Abou Diakité, Adil El Bouziri, Claire Ellul, Tarun Ghawana, Gesquiere Gilles, Didier Grimaldi, Ori Gudes, Stephen Hirtle, Martin Kada, Lamia Karim, Robert Laurini, Christina Mickrenska-Cherneva, Christopher Petit, Alenka Poplin, Ivana Racetin, Dimos Pantazis, Preston Rodrigues, Camilo Leon Sanchez, Genoveva Vargas Solar, Nils Walravens, Parag Wate, Besri Zineb, Sisi Zlatanova. We are also grateful to the work of the local organising committee at HFT Stuttgart, without whom this conference would not have been possible

PREFACE

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