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Spatial Computing and the Future of Utility GIS
This document discusses spatial computing and its potential applications for utility GIS. It begins by providing context on the evolution of spatial computing technologies like digital twins and sensor webs. It then discusses several emerging ideas for spatial computing in utilities, such as using digital twins to model urban energy systems, integrating predictive models across domains, and enabling geo-enabled edge computing. Finally, it considers the technology evolution required to realize these opportunities through standards, interoperability, and integrating emerging techniques like semantics and artificial intelligence.
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Introduction to spatial computing's future in utility GIS, emphasizing long-term perspectives and current developments.
Explains cartographic principles of place and the emergence of cyber-physical systems, IoT, and Digital Twins.
Focuses on Digital Twins as virtual system representations for urban management and resource optimization.
Discusses various methods for reality capture and how utilities can leverage Digital Twins for energy management.
Explores the use of AR and semantics in enhancing digital twins for better decision-making in utilities.
Details the evolution of semantic web technologies and their application in augmenting AR functionality.
Highlights the shift toward predictive models in utility management and the integration of edge computing.
Discusses the use of geo-context in edge computing and the evolution of technologies in relation to standards.
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Spatial Computing and the Future of Utility GIS
- 1. SPATIAL COMPUTING AND THEFUTURE OF UTILITY GIS George Percivall GeoRoundtable Extended version of a presentation for EPRI Advisor's Conference, 15 Sept. 2021
- 2. GeoScience and RemoteSensing, Standards Co-Chair CTO, Chief Engineer Earth Science Informatics; Digital Earth Systems Engineering; EV1, Autonomous Vehicles, Satellites BS – Engineering Physics, MS EE – Control Systems George Percivall, GeoRoundtable
- 3. SPATIAL COMPUTING AND THEFUTURE OF UTILITY GIS • Long-view perspective on spatial computing • Current spatial computing developments • Emerging ideas for spatial computing in utilities
- 4. WHERE AND WHENAS ORGANIZING PRINCIPLES We trust the sense of place to entice romance, facilitate precision, and encourage generalization as users search and explore our maps and globes - Michael T. Jones, Google Earth www.ogc.org/standards/kml Circa 2000
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- 6. SENSOR WEB Cyber Physical– NIST ~2010 • Internet of Things (IoT) • Industrial Internet • Smart Cities • Smart Grid • "Smart" Anything • Digital Twins Integrating cyber and physical • Sensor Networks • Modeling and Simulation • Decision Support Circa 2000
- 7. MERGING DIGITAL ANDPHYSICAL Source: Gabriel Rene, Spatial Web Foundation medium.com/swlh/an-introduction-to-the-spatial-web-bb8127f9ac45 Killer App of Spatial Web: Digital Twins Circa 2020
- 8. Digital Twin: Virtual representationof a system Visualize system, check status, perform analysis and generate insights in order to predict and affect its performance. 8 DIGITAL TWIN Social-Physical System Digital Twin Observe Affect, Inform Model, Simulate, Predict Analytics and Decision
- 9. Digital Twins formultiple systems in urban setting 9 URBAN DIGITAL TWINS Water Digital Twin Model, Simulate, Predict Analytics and Decision ructure of the i-UR Data or i-UR (which is called "i-UR Data") is the combination of following data (Figure dimentional city objects and city model; tailed information of city objects for analysis; nstraints/conditions (e.g., regulation) related to urban revitalization; and atistical grid data for regional and global analysis and visualization. a) 3-dimentional city model ed information of city ilding structure raints/conditions undation hazardous areas d) Statistical grid data e.g. population distribution o national or worldwide sca Mobility Digital Twin Model, Simulate, Predict Analytics and Decision “Skeleton” 3D physical fabric Urban design Digital Twin Model, Simulate, Predict Analytics and Decision Energy Digital Twin Model, Simulate, Predict Analytics and Decision (other) Digital Twin Model, Simulate, Predict Analytics and Decision Digital replicas of cities – giving access to thematic information, services, models, scenarios, simulations, forecasts, and visualisations. Other Twins - Health services - Economic DT - Urban Planning - Event Planning - … From pairwise coordination towards a system-of- systems Social-Physical System www.locationpowers.net/events/2101urbanvirtual
- 10. • Base Model:Mature technology for reality capture • Methods and Representations: LIDAR, StM; CAD, BIM; CityGML, IndoorGML, Underground, • Digital Twins at urban-scale build on GIS capabilities. • Dynamic Models:. Innovation of integrated, predictive models for resource management. • Combine real-time data sensing with predictive modeling to improve dynamic resource management. • IoT increase the availability of real-time data about devices, location, weather, traffic, people movement, etc. • Dependent upon further developments in dynamic model interoperability. • Energy Digital Twins. Application of successful urban energy models to meet climate goals. • Could cut 87% of greenhouse gas emissions from building energy consumption. • Using pioneering research, cities are using Digital Twins to address urban energy consumption. • ORNL Energy Model: 178K buildings in 2018; 123M buildings In 2021 - every building in the US. 10 URBAN DIGITAL TWIN - RECOMMENDATIONS https://www.linkedin.com/pulse/urban-digital-twins-deployment-geo- roundtable/
- 11. REALITY CAPTURE EPRI DigitalTwin Is Today’s Utility GIS Opportunity 8 July 2021 Multiple Methods for Capture and Data Representation • LIDAR • Structure from Motion • CAD, AEC, BIM • City Modeling: • CityGML • IndoorGML, • Underground, • Geospatial modeling • IOT and Sensor Webs
- 12. I3S Open Services IFC KML Open Content zLAS LAZ Standard Capabilities Geo Package Open Software, Standardsand Data ensure access, guarantee interoperability, enable innovation, and encourage usage and adoption ENABLING DIGITAL TWINS WITH OPEN STANDARDS MQTT gRPC SensorThings CityGML GeoREST GeoJSON Source: Chris Andrews, ESRI, Location Powers: Urban Digital Twins
- 13. INTEGRATED DIGITAL BUILT ENVIRONMENT 13 Improvingintegration between BIM & GIS • Disparities that hinder integration • Operations that underpin use cases • Methods of integration in usage • Proposed action points
- 14. ORNL ENERGY DIGITALTWIN In 2018: Digital twin of 178,368 buildings in the service area for the Electric Power Board of Chattanooga, TN, with comparison to 15-minute electricity data http://web.eecs.utk.edu/~jnew1/publications/2018_PeerReview_AutoBEMposter.pdf In 2021 - a model of every building in the United States: • AutoBEM: process multiple types of data, extract building-specific descriptors, generate building energy models • Dataset of 122.9 million buildings includes: Models, – OpenStudio, and EnergyPlus, building energy models https://doi.ccs.ornl.gov/ui/doi/339
- 15. • EPRI doingDigital Twin Development - Continue to increase scale: 1000x ENERGY DISTRIBUTION DIGITAL TWIN © 2021 Electric Power Research Institute, Inc. All rights reserved. w w w . e p r i . c o m 13 Smart Modeling Example SBSDigital Twin Development © 2021 Electric Power Research Institute, Inc. All rights reserved. w w w . e p r i . c o m 13 Spatial Business Systems and Southern Company – User Conference Video
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- 17. VIEWING DIGITAL TWINSWITH AUGMENTED REALITY Southern Company Augmented Reality Study with EPRI (July 2015)
- 18. • Increasing Intelligencewith Semantics in Augmented Reality • Predictive Models and Simulation for Decision and Control • Geo-Enabled Edge Computing in Utilities EMERGING SPATIAL COMPUTING FOR UTILITIES
- 19. • Semantic WebTech Evolution • Ontologies (2000s); • Linked Data (2010s); • Knowledge Graphs (now) • Geospatial as web of linked data • Knowledge graphs, RDF, Property graphs; • Query languages: SPARQL and GQL • Semantic Enhancement of AR Scene • Semantic inferencing to add content to AR Scene SEMANTIC WEB AND SPATIAL DATA Linda van den Brink; Location Powers 2017
- 20. AUGMENTING AR WITHSEMANTICS AND AI Semantic and AI Tech Augmentation to AR Reality Model Language (RML) A semantic description language suitable for semantic modeling of AR functionality, i.e., visual analysis, spatial reasoning. Domain Ontology tuned to AR RML-based ontology populated with objects, relations and definitions for AR in a particular domain. Object ID and Tagging Algorithm to identify objects in a spatial scene and tag the objects using the Domain AR Ontology. AR Semantic Reasoning Engine Reasoning engine using RML on objects identified in an AR scene, in order to recommend placement of AR Assets Semantic Enhancement of Scene Add semantic recommendations to the AR Scene construction and management For more contact Ethar
- 21. • Shift fromObservations and Measurements to Models and Simulation • AI Models, Predictive Models, Hybrid Models • Space and time as basis for predictive models. • Natural Models and Human Models • Integrating natural models is very hard • Integrating natural and human models is even harder • Model Interoperability • Grand Challenge for Data Science • Open Modeling Foundation PREDICTIVE MODELS FOR DECISION AND CONTROL Social Agriculture Economic Natural Infrastructure Models for Natural-Human System Interactions Weather Power Generation Power Distribution Power Transmission Customers/Social/Economic OGC Geospatial Data Science Tech Note
- 22. INTEGRATED MODELS FORUTILITIES Kezunovic, et.al, Big data analytics for future electricity grids, Electric Power Systems Research, V.189. https://doi.org/10.1016/j.epsr.2020.106788. Model Interoperability is needed for Predictive Models for Decision and Control in Utilities Weather Power Generation Power Distribution
- 23. EDGE COMPUTING edge computing: distributedcomputing in which processing and data storage takes place at or near the edge edge: boundary between pertinent digital and physical entities, delineated by networked sensors and actuators Central Tier often in a data center, wide span of connectivity Edge Tier IoT gateways, control nodes low latency to devices Device Tier sensors, actuators, user interface devices sensor sensor actuator actuator Mike Edwards, IBM, Editor: ISO/IEC 23167 & 23188 - 25 June 2019 edge computing reuses cloud computing: virtualization and containers
- 24. GEO-ENABLED EDGE COMPUTING Usinggeo-context in Edge Computing Edge Tier Device Tier Geo-Area B1 Geo-Area B2 Geo-Area B3 Broker B2 Broker B1 Broker B3 Need to standardized definition of Geo-Areas Devices Devices Devices Hasenburg, Jonathan and David Bermbach. (2020
- 25. 7 2 1 6 8 3 5 4 DISCRETEGLOBAL GRIDS FOR GEO-EDGE COMPUTING referencing by zone identifiers 541 543 532 516 517 544 535 508 Zone Geometry Zone Neighbors • • 540 ® September 09, 2019 Apachecon OGC DGGS Standard cell geometry to fit sphere
- 26. TECHNOLOGY EVOLUTION Long Term Vision CurrentTech • Every technology stands on a pyramid of others – B. Arthur • Interfaces are what give systems there added value – E. Rechtin • Technology evolves more rapidly with stable intermediate standards – H. Simon • The more prototypes, the more polished the final product – M. Schrage Products that define new markets by meeting needs Emerging Tech Complex Adaptive System Heuristics George Percivall, GeoRoundtable