Towards A Virtual Planning Support Theatre for City Planning and Design

In the era of 'Smart Cities', Planning Support Systems play an important role in providing a suite of digital tools to support evidenced based planning and design (Pettit et al. 2019). Planning Support Theatres vary from the traditional City Command Control Centres which are used to manage the real-time city (Kitchin 2014) in that they look at planning and design of future of cities and regions through collaboration (Healey 2003) between various stakeholders. Such methodologies which lend themselves to collaborative planning and design including Geo-design (Steinitz 2012; Pettit et al. 2019) and Co-design (Punt et al. 2020). There has been a plethora of virtual learning environments applicable to any discipline-specific application, each with their own names, terminology, feature sets and degrees of interoperability. In addition to providing the regular set of advantages such as ease of use across large geographies, improving collaboration between diverse set of stakeholders, these virtual platforms have become essential in the current world with travel restrictions and social distancing. In this context, it is imperative to explore the use of virtual systems for the purpose designing and implementing planning support theatre for city planning and design. This research builds a prototype for web based, 3D, immersive, planning support theatre and evaluates its functionality against similar physical theatres

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

CHALLENGES IN FLYING QUADROTOR UNMANNED AERIAL VEHICLE FOR 3D INDOOR RECONSTRUCTION

Three-dimensional modelling plays a vital role in indoor 3D tracking, navigation, guidance and emergency evacuation. Reconstruction of indoor 3D models is still problematic, in part, because indoor spaces provide challenges less-documented than their outdoor counterparts. Challenges include obstacles curtailing image and point cloud capture, restricted accessibility and a wide array of indoor objects, each with unique semantics. Reconstruction of indoor environments can be achieved through a photogrammetric approach, e.g. by using image frames, aligned using recurring corresponding image points (CIP) to build coloured point clouds. Our experiments were conducted by flying a QUAV in three indoor environments and later reconstructing 3D models which were analysed under different conditions. Point clouds and meshes were created using Agisoft PhotoScan Professional. We concentrated on flight paths from two vantage points: 1) safety and security while flying indoors and 2) data collection needed for reconstruction of 3D models. We surmised that the main challenges in providing safe flight paths are related to the physical configuration of indoor environments, privacy issues, the presence of people and light conditions. We observed that the quality of recorded video used for 3D reconstruction has a high dependency on surface materials, wall textures and object types being reconstructed. Our results show that 3D indoor reconstruction predicated on video capture using a QUAV is indeed feasible, but close attention should be paid to flight paths and conditions ultimately influencing the quality of 3D models. Moreover, it should be decided in advance which objects need to be reconstructed, e.g. bare rooms or detailed furniture

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

AN EXTRACTION APPROACH OF THE TOP-BOUNDED SPACE FORMED BY BUILDINGS FOR PEDESTRIAN NAVIGATION

The navigation of pedestrians can be regarded as their movements from one unoccupied space to another unoccupied and connected space. These movements generally occur in three types of environments: indoor, outdoor, and semi-bounded (top-bounded, and/or side-bounded) spaces. While the two former types of spaces are subject to most of the attention, the latter (semi-bounded) also presents a valuable impact on the navigation behaviour. For example, top-bounded environments (e.g. roofs, shelters, etc.) are very popular for pedestrian navigation since a top structure can offer protection from harsh weather, rain, or strong sun. However, such semibounded spaces are completely missing in current navigation models and systems. This is partly explained by the fact that modelling the space, which is by defining a three-dimensional boundless and extensible component (mainly out of the indoor environment), is a very challenging task. In this paper, we propose a structure-based approach for top-bounded space extraction in the built environment, relying on 3D models. Thanks to the rapid expansion and availability of 3D city models, our approach can help to account for such type of spaces in 3D pedestrian navigation systems

Constraints, Histones, and the 30 Nanometer Spiral

We investigate the mechanical stability of a segment of DNA wrapped around a histone in the nucleosome configuration. The assumption underlying this investigation is that the proper model for this packaging arrangement is that of an elastic rod that is free to twist and that writhes subject to mechanical constraints. We find that the number of constraints required to stabilize the nuclesome configuration is determined by the length of the segment, the number of times the DNA wraps around the histone spool, and the specific constraints utilized. While it can be shown that four constraints suffice, in principle, to insure stability of the nucleosome, a proper choice must be made to guarantee the effectiveness of this minimal number. The optimal choice of constraints appears to bear a relation to the existence of a spiral ridge on the surface of the histone octamer. The particular configuration that we investigate is related to the 30 nanometer spiral, a higher-order organization of DNA in chromatin.Comment: ReVTeX, 15 pages, 18 figure

Distributed GIS for automated natural hazard zonation mapping internet-SMS warning towards sustainable society

Today, open systems are needed for real time analysis and warnings on geo-hazards and over time can be achieved using Open Source Geographical Information System (GIS)-based platform such as GeoNode which is being contributed to by developers around the world. To develop on an open source platform is a very vital component for better disaster information management as far as spatial data infrastructures are concerned and this would be extremely vital when huge databases are to be created and consulted regularly for city planning at different scales, particularly satellite images and maps of locations. There is a big need for spatially referenced data creation, analysis, and management. Some of the salient points that this research would be able to definitely contribute with GeoNode, being an open source platform, are facilitating the creation, sharing, and collaborative use of geospatial data. The objective is development of an automated natural hazard zonation system with Internet-short message service (SMS) warning utilizing geomatics for sustainable societies. A concept of developing an internet-resident geospatial geohazard warning system has been put forward in this research, which can communicate alerts via SMS. There has been a need to develop an automated integrated system to categorize hazard and issue warning that reaches users directly. At present, no web-enabled warning system exists which can disseminate warning after hazard evaluation at one go and in real time. The objective of this research work has been to formalize a notion of an integrated, independent, generalized, and automated geo-hazard warning system making use of geo-spatial data under popular usage platform. In this paper, a model of an automated geo-spatial hazard warning system has been elaborated. The functionality is to be modular in architecture having GIS-graphical user interface (GUI), input, understanding, rainfall prediction, expert, output, and warning modules. A simplified but working prototype of the system without the GIS-GUI module has been already tested, validated, and reported. Through this paper, a significantly enhanced system integrated with web-enabled-geospatial information has been proposed, and it can be concluded that an automated hazard warning system has been conceptualized and researched. However, now the scope is to develop it further

Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets

The support of geospatial data and technologies for the United Nations Sustainable Development Goals (SDG) framework is critical for assessing and monitoring key indicators, revealing the planet’s trajectory towards sustainability. The availability of global open geospatial datasets, especially high-resolution land cover datasets, provides significant opportunities for computing and comparing indicators across different regions and scales. However, barriers to their proficient use remain due to a lack of data awareness, management and processing capacities using geographic information systems software. To address this, the”Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets” project created open training material on discovering, accessing, and manipulating global geospatial datasets for computing SDG indicators. The material focuses on water and terrestrial ecosystems, urban environments, and climate, by leveraging world-class global geospatial datasets and using the Free and Open Source Software QGIS. The training material is released under a Creative Commons Attribution 4.0 License, ensuring broad accessibility and facilitating continuous improvement

CAPACITY BUILDING FOR GIS-BASED SDG INDICATORS ANALYSIS WITH GLOBAL HIGH-RESOLUTION LAND COVER DATASETS

The support of geospatial data and technologies for the United Nations Sustainable Development Goals (SDG) framework is critical for assessing and monitoring key indicators, revealing the planet’s trajectory towards sustainability. The availability of global open geospatial datasets, especially high-resolution land cover datasets, provides significant opportunities for computing and comparing indicators across different regions and scales. However, barriers to their proficient use remain due to a lack of data awareness, management and processing capacities using geographic information systems software. To address this, the ”Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets” project created open training material on discovering, accessing, and manipulating global geospatial datasets for computing SDG indicators. The material focuses on water and terrestrial ecosystems, urban environments, and climate, by leveraging world-class global geospatial datasets and using the Free and Open Source Software QGIS. The training material is released under a Creative Commons Attribution 4.0 License, ensuring broad accessibility and facilitating continuous improvement