Augmented Reality for Windy Cities: 3D Visualization of Future Wind Nature Analysis in City Planning

Shubhi Harbola (), Martin Storz () and Volker Coors ()
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Shubhi Harbola: University of Stuttgart
Martin Storz: University of Applied Science
Volker Coors: Institute for Applied Research, University of Applied Sciences Stuttgart

Chapter 15 in iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 2022, pp 241-250 from Springer

Abstract: Abstract Effective government management, convenient public services, and sustainable industrial development are achieved by the thorough utilization and management of green, renewable resources. The research and the study of meteorological data and its effect on devising renewable solutions as a replacement for nonrenewable ones is the motive of researchers and city planners. Sources of energy like wind and solar are free, green, and popularly being integrated into sustainable development and city planning to preserve environmental quality. Sensor networks have become a convenient tool for environmental monitoring. Wind energy generated through the use and maintenance of wind turbines requires knowledge of wind parameters such as speed and direction for proper maintenance. An augmented reality (AR) tool for interactive visualization and exploration of future wind nature analyses for experts is still missing. Existing solutions are limited to graphs, tabular data, two-dimensional space (2D) maps, globe view, and GIS tool designed for the desktop and not adapted with AR for easy, interactive mobile use. This work aims to provide a novel AR-based mobile supported application (App) that serves as a bridge between three-dimensional space (3D) temporal wind dataset visualization and predictive analysis through machine learning (ML). The proposed development is a dynamic application of AR supported with ML. It provides a user interactive designed approach, presenting a multilayered infrastructure process accessed through a mobile AR platform that supports 3D visualization of temporal wind data through future wind analysis. Thus, a novel AR visualization App with the prediction of wind nature using ML algorithms would provide city planners with advanced knowledge of wind conditions and help in easy decision-making with interactive 3D visualization.

Keywords: Wind speed; 3D visualization; Predictive models; Augmented reality; Green energy; Machine learning; Meteorological data; Mobile App; Planning cites; Wind forecasting (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-92096-8_15

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DOI: 10.1007/978-3-030-92096-8_15

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