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Do we need a new standard for visualizing the invisible?

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SANGHEE SHIN
SANGHEE SHIN

At the OGC member meeting in Delft, Netherlands, my team from Gaia3D and I shared our experiences and the challenges we faced while visualizing large spatio-temporal datasets in digital twins. In conclusion, we discussed the necessity for a new standard, referred to as 'Voxel Tiles,' for visualizing spatio-temporal data.

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Do we need a new standard for visualizing the invisible? The 128th OGC Member Meeting Sanghee Shin(shshin@gaia3d.com) Hakjoon Kim(hjkim@gaia3d.com) 25th March 2024 Meeting Sponsors
Agenda • Background: Blending Spatio-Temporal Data with Digital Twins • The Hurdles: The Big Data Dilemma and Standards • Tackling the Problems: Smart Fixes for Data Overload • Lessons Learned: Map Tiles, Vector Tiles, 3D Tiles, and Voxel Tiles(?)
Background • We’ve been developing web based digital twins for decision makers and citizens. • To support informed decision-making and to provide comprehensive understanding of real-world problems, it is essential to reconstruct the real-world in digital twins from diverse data sources in an intuitive manner with terrain, buildings, spatio-temporal data, and others. • We’ve developed digital twins on top of OGC standard blocks such as WMS, WMTS, WFS, and 3D Tiles. • When we tried to meld spatio-temporal data with digital twins, we realized there are problems!
Spatio-Temporal Data Chemical Diffusion Prediction Noise Propagation Simulation Drainage and Flooding Simulation Before and After Construction Wind Flow Simulation Before and After Construction
The Hurdles • There are challenges with data volume in aerodynamics modeling or computational fluid dynamics(CFD), e.g., wind field, air pollution, odors spreading, or chemical diffusion simulations. • The size of these data are very huge by 2 reasons. 1. Multi-layered(3D gridded) data covering very large areas, from hundreds to thousands of square kilometers. 2. Datasets consisting of numerous temporal snapshots.
In addition…. • Our visualization process required the use of all snapshots in their entirety to accurately represent changes over time. • Users expressed a desire to track the temporal evolution of the data. • Decision makers and citizens preferred animation as the optimal method to illustrate these dynamic changes. • And hard to find a standardized method to visualize these spatio-temporal data
Real Cases <Chemical diffusion prediction during a chemical leak incident> <NO2 diffusion simulation for EIA of a landfill construction> Source data spec: - Coverage area : 15km x 15km(cell size 100m x 100m) - Coverage height : 0~3km (8 layers) - Time duration : 3 hours (1 min interval, 180 timestamps) - Data size : 1.3GB (ASCII format) Source data spec: - Coverage area : 30km x 30km(cell size 200m x 200m) - Coverage height : 0~200m (7 layers) - Time duration : 1 year(1 hour interval, 8,760 timestamps) - Data size : 114GB (ASCII format)
Tackling the Problems • Implementing value coding to reduce and compress the data size. • Converted the original data to PNG format, achieving a reduction to approximately 1.5% of the original size.
And, • Spatial and temporal interpolation were performed on the client side to minimize discontinuities between vertical layers and temporal snapshots. spatial interpolation temporal interpolation
And, • Each frame of the animation was created using volume rendering. concept of volume rendering 4 steps of volume rendering (1. ray casting, 2. sampling, 3.shading, 4. compositing)
Implementations ,Chemical diffusion prediction during a chemical leak incident>
<NO2 diffusion simulation for EIA of a landfill construction> Implementations
Lessons Learned • We need lightweight data handling strategies for effective visualization of large size spatio-temporal data. • For dynamic data that evolves over time, considering a streaming approach is imperative to ensure efficient data delivery.(In the case of the NO2 diffusion simulation, retrieving the complete set of PNGs took between 12 to 18 minutes, which we deemed unsatisfactory. Consequently, we are now in the process of developing a streaming service to efficiently deliver this data.) • We’ve used Map Tiles, Vector Tiles, 3D Tiles to represent the real-world features and phenomena. So, how about having a new standard for visualizing the spatio-temporal data, maybe named Voxel Tiles? Acknowledgment: This work was supported by Korea Environment Industry & Technology Institute(KEITI) through 'Advanced Technology Development Project for Predicting and Preventing Chemical Accidents' program and 'The Decision Support System Development Project for Environmental Impact Assessment' program, funded by Korea Ministry of Environment(MOE)(grant number: RS-2022-KE002176 and 2020002990005).
500+ International Members 110+ Member Meetings 60+ Alliance and Liaison partners 50+ Standards Working Groups 45+ Domain Working Groups 25+ Years of Not for Profit Work 10+ Regional and Country Forums Innovation 120+ Innovation Initiatives 380+ Technical reports Quarterly Tech Trends monitoring Standards 65+ Adopted Standards 300+ products with 1000+ certified implementations 1,700,000+ Operational Data Sets Using OGC Standards Copyright © 2023 Open Geospatial Consortium Thank You Community

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Do we need a new standard for visualizing the invisible?

  • 1. Do we need a new standard for visualizing the invisible? The 128th OGC Member Meeting Sanghee Shin(shshin@gaia3d.com) Hakjoon Kim(hjkim@gaia3d.com) 25th March 2024 Meeting Sponsors
  • 2. Agenda • Background: Blending Spatio-Temporal Data with Digital Twins • The Hurdles: The Big Data Dilemma and Standards • Tackling the Problems: Smart Fixes for Data Overload • Lessons Learned: Map Tiles, Vector Tiles, 3D Tiles, and Voxel Tiles(?)
  • 3. Background • We’ve been developing web based digital twins for decision makers and citizens. • To support informed decision-making and to provide comprehensive understanding of real-world problems, it is essential to reconstruct the real-world in digital twins from diverse data sources in an intuitive manner with terrain, buildings, spatio-temporal data, and others. • We’ve developed digital twins on top of OGC standard blocks such as WMS, WMTS, WFS, and 3D Tiles. • When we tried to meld spatio-temporal data with digital twins, we realized there are problems!
  • 4. Spatio-Temporal Data Chemical Diffusion Prediction Noise Propagation Simulation Drainage and Flooding Simulation Before and After Construction Wind Flow Simulation Before and After Construction
  • 5. The Hurdles • There are challenges with data volume in aerodynamics modeling or computational fluid dynamics(CFD), e.g., wind field, air pollution, odors spreading, or chemical diffusion simulations. • The size of these data are very huge by 2 reasons. 1. Multi-layered(3D gridded) data covering very large areas, from hundreds to thousands of square kilometers. 2. Datasets consisting of numerous temporal snapshots.
  • 6. In addition…. • Our visualization process required the use of all snapshots in their entirety to accurately represent changes over time. • Users expressed a desire to track the temporal evolution of the data. • Decision makers and citizens preferred animation as the optimal method to illustrate these dynamic changes. • And hard to find a standardized method to visualize these spatio-temporal data
  • 7. Real Cases <Chemical diffusion prediction during a chemical leak incident> <NO2 diffusion simulation for EIA of a landfill construction> Source data spec: - Coverage area : 15km x 15km(cell size 100m x 100m) - Coverage height : 0~3km (8 layers) - Time duration : 3 hours (1 min interval, 180 timestamps) - Data size : 1.3GB (ASCII format) Source data spec: - Coverage area : 30km x 30km(cell size 200m x 200m) - Coverage height : 0~200m (7 layers) - Time duration : 1 year(1 hour interval, 8,760 timestamps) - Data size : 114GB (ASCII format)
  • 8. Tackling the Problems • Implementing value coding to reduce and compress the data size. • Converted the original data to PNG format, achieving a reduction to approximately 1.5% of the original size.
  • 9. And, • Spatial and temporal interpolation were performed on the client side to minimize discontinuities between vertical layers and temporal snapshots. spatial interpolation temporal interpolation
  • 10. And, • Each frame of the animation was created using volume rendering. concept of volume rendering 4 steps of volume rendering (1. ray casting, 2. sampling, 3.shading, 4. compositing)
  • 11. Implementations ,Chemical diffusion prediction during a chemical leak incident>
  • 12. <NO2 diffusion simulation for EIA of a landfill construction> Implementations
  • 13. Lessons Learned • We need lightweight data handling strategies for effective visualization of large size spatio-temporal data. • For dynamic data that evolves over time, considering a streaming approach is imperative to ensure efficient data delivery.(In the case of the NO2 diffusion simulation, retrieving the complete set of PNGs took between 12 to 18 minutes, which we deemed unsatisfactory. Consequently, we are now in the process of developing a streaming service to efficiently deliver this data.) • We’ve used Map Tiles, Vector Tiles, 3D Tiles to represent the real-world features and phenomena. So, how about having a new standard for visualizing the spatio-temporal data, maybe named Voxel Tiles? Acknowledgment: This work was supported by Korea Environment Industry & Technology Institute(KEITI) through 'Advanced Technology Development Project for Predicting and Preventing Chemical Accidents' program and 'The Decision Support System Development Project for Environmental Impact Assessment' program, funded by Korea Ministry of Environment(MOE)(grant number: RS-2022-KE002176 and 2020002990005).
  • 14. 500+ International Members 110+ Member Meetings 60+ Alliance and Liaison partners 50+ Standards Working Groups 45+ Domain Working Groups 25+ Years of Not for Profit Work 10+ Regional and Country Forums Innovation 120+ Innovation Initiatives 380+ Technical reports Quarterly Tech Trends monitoring Standards 65+ Adopted Standards 300+ products with 1000+ certified implementations 1,700,000+ Operational Data Sets Using OGC Standards Copyright © 2023 Open Geospatial Consortium Thank You Community