A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT

Mapping the visual magnitude of popular tourist sites in Edinburgh city

There is value in being able to automatically measure and visualise the visual magnitude of city sites (monuments and buildings, tourist sites) – for example in urban planning, as an aid to automated way finding, or in augmented reality city guides. Here we present the outputs of an algorithm able to calculate visual magnitude – both as an absolute measure of the façade area, and in terms of a building’s perceived magnitude (its lesser importance with distance). Both metrics influence the photogenic nature of a site. We therefore compared against maps showing the locations from where geo-located FlickR images were taken.  The results accord with the metrics and therefore help disambiguate the meaning  of FlickR tags

Estimating the Available Sight Distance in the Urban Environment by GIS and Numerical Computing Codes

The available sight distance (ASD) is that part of the roadway ahead which is visible to the driver, and should be of sufficient length to allow a vehicle traveling at the designated speed to stop before reaching a stationary object in its path. It is fundamental in assessing road safety of a project or on an existing road section. Unfortunately, an accurate estimation of the available sight distance is still an issue on existing roads, above all due to the lack of information regarding the as-built condition of the infrastructure. Today, the geomatics field already offers different solutions for collecting 3D information about environments at different scales, integrating multiple sensors, but the main issue regarding existing mobile mapping systems (MMSs) is their high cost. The first part of this research focused on the use of a low-cost MMS as an alternative for obtaining 3D information about infrastructure. The obtained model can be exploited as input data of specific algorithms, both on a GIS platform and in a numerical computing environment to estimate ASD on a typical urban road. The aim of the investigation was to compare the performances of the two approaches used to evaluate the ASD, capturing the complex morphology of the urban environment

Describing urban soils by a faceted system ensures more informed decision-making

Urban areas are increasing worldwide at a dramatic rate and their soils definitely deserve more attention than they have received in the past. In urban environments, soils potentially provide the same ecosystem services as in rural and wild environments, although in some cases they are depleted of their basic functions, such as when they lose their productive and filtering capacities because of sealing, and become mere supports for infrastructures. In other cases, soils of urban areas acquire new functions that are unique to these environments. Current soil classifications fail to effectively account for the complexity of urban soils and the information that is required for their management. Additionally, the survey of urban soils is difficult, due to fragmentation and rapid land use change and the fact that due to human pressure their properties seldom vary linearly and predictably according to landforms, which hinders the effectiveness of geostatistics. The conventional practice of grouping similar soils and transferring their information in a concise manner is not viable for urban soils. We advocate the introduction of a faceted system - i.e., a scheme using semantic categories, either general or subject-specific, that are combined to create the full classification entry - to organize the information on urban soils to support decision making. The facets that such a system should be based on are not only the intrinsic physical and chemical properties that are usually used to describe any soil, but also other tangible or even immaterial properties that are particularly meaningful in an urban context, such as landscape metrics, or aesthetic, social and historical values. As well as providing more adequately the information of the type requested by urban planners and policymakers, a faceted system of classification of urban soil resources would have the flexibility to accommodate all available or future scattered, rapidly changing, or incomplete data

Multi-scale transport and exchange processes in the atmosphere over mountains. Programme and experiment

TEAMx is an international research programme that aims at improving the understanding of exchange processes in the atmosphere over mountains at multiple scales and at advancing the parameterizations of these processes in numerical models for weather and climate prediction–hence its acronyms stands for Multi-scale transport and exchange processes in the atmosphere over mountains – Programme and experiment. TEAMx is a bottom-up initiative promoted by a number of universities, research institutions and operational centres, internationally integrated through a Memorandum of Understanding between inter- ested parties. It is carried out by means of coordinated national, bi-national and multi-national research projects and supported by a Programme Coordination Office at the Department of Atmospheric and Cryospheric Sciences of the University of Innsbruck, Austria. The present document, compiled by the TEAMx Programme Coordination Office, provides a concise overview of the scientific scope of TEAMx. In the interest of accessibility and readability, the document aims at being self-contained and uses only a minimum of references to scientific literature. Greyboxes at the beginning of chapters list the literature sources that provide the scientific basis of the document. This largely builds on review articles published by the journal Atmosphere between 2018 and 2019, in a special issue on Atmospheric Processes over Complex Terrain. A few other important literature pieces have been referenced where appropriate. Interested readers are encouraged to examine the large body of literature summarized and referenced in these articles. Blue boxes have been added to most sub-chapters. Their purpose is to highlight key ideas and proposals for future collaborative research

Discovering optimal strategy in tactical combat scenarios through the evolution of behaviour trees

In this paper we address the problem of automatically discovering optimal tactics in a combat scenario in which two opposing sides control a number of fighting units. Our approach is based on the evolution of behaviour trees, combined with simulation-based evaluation of solutions to drive the evolution. Our behaviour trees use a small set of possible actions that can be assigned to a combat unit, along with standard behaviour tree constructs and a novel approach for selecting which action from the tree is performed. A set of test scenarios was designed for which an optimal strategy is known from the literature. These scenarios were used to explore and evaluate our approach. The results indicate that it is possible, from the small set of possible unit actions, for a complex strategy to emerge through evolution. Combat units with different capabilities were observed exhibiting coordinated team work and exploiting aspects of the environment

3D Analytics: Opportunities and Guidelines for Information Systems Research

Progress in sensor technologies has made three-dimensional (3D) representations of the physical world available at a large scale. Leveraging such 3D representations with analytics has the potential to advance Information Systems (IS) research in several areas. However, this novel data type has rarely been incorporated. To address this shortcoming, this article first presents two showcases of 3D analytics applications together with general modeling guidelines for 3D analytics, in order to support IS researchers in implementing research designs with 3D components. Second, the article presents several promising opportunities for 3D analytics to advance behavioral and design-oriented IS research in several contextual areas, such as healthcare IS, human-computer interaction, mobile commerce, energy informatics and others. Third, we investigate the nature of the benefits resulting from the application of 3D analytics, resulting in a list of common tasks of research projects that 3D analytics can support, regardless of the contextual application area. Based on the given showcases, modeling guidelines, research opportunities and task-related benefits, we encourage IS researchers to start their journey into this largely unexplored third spatial dimension

Mobile 2D and 3D Spatial Query Techniques for the Geospatial Web

The increasing availability of abundant geographically referenced information in the Geospatial Web provides a variety of opportunities for developing value-added LBS applications. However, large data volumes of the Geospatial Web and small mobile device displays impose a data visualization problem, as the amount of searchable information overwhelms the display when too many query results are returned. Excessive returned results clutter the mobile display, making it harder for users to prioritize information and causes confusion and usability problems. Mobile Spatial Interaction (MSI) research into this “information overload” problem is ongoing where map personalization and other semantic based filtering mechanisms are essential to de-clutter and adapt the exploration of the real-world to the processing/display limitations of mobile devices. In this thesis, we propose that another way to filter this information is to intelligently refine the search space. 3DQ (3-Dimensional Query) is our novel MSI prototype for information discovery on today’s location and orientation-aware smartphones within 3D Geospatial Web environments. Our application incorporates human interactions (interpreted from embedded sensors) in the geospatial query process by determining the shape of their actual visibility space as a query “window” in a spatial database, e.g. Isovist in 2D and Threat Dome in 3D. This effectively applies hidden query removal (HQR) functionality in 360º 3D that takes into account both the horizontal and vertical dimensions when calculating the 3D search space, significantly reducing display clutter and information overload on mobile devices. The effect is a more accurate and expected search result for mobile LBS applications by returning information on only those objects visible within a user’s 3D field-of-view