The Peak of Data Integration Encore- Navigating Climate Change with Data Innovation & AI
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The document discusses the integration of cloud technology and geospatial data to address challenges such as climate change, resource scarcity, and digitalization. It emphasizes the importance of open collaboration, data as a product, and innovative technologies like artificial intelligence in shaping effective solutions. It also highlights location intelligence's role in facilitating the routing and siting of renewable energy projects through comprehensive data analysis.
The Peak of Data Integration Encore- Navigating Climate Change with Data Innovation & AI
- 1. SHAPING THE FUTURE WITH THE CLOUD AND GEOIT The Peak Of Data Integration Christian Elfers, con terra 05.09.2023
- 2. © con terra TODAYS WORLD EXCITING AND CHALLENGING
- 3. © con terra UNCERTAINTY AND CHANGE
- 4. © con terra | 4 SCARCITY AND SHORTAGE
- 5. © con terra CLIMATE CHANGE AND SUSTAINABILITY
- 6. © con terra | 6 DIGITALIZATION AND TECHNOLOGY ARE ESSENTIAL TOOLS
- 7. © con terra OPEN PEOPLE, OPEN TECHNOLOGY
- 8. © con terra | 8 Open Platform Open Source Open Data Open Identity Open Standards Open Collaboration Open API Open Architecture “OPEN PEOPLE” “OPEN TECHNOLOGY” Open AI
- 9. © con terra | 9
- 10. © con terra | 10 Source: OGC, https://ogcapi.ogc.org/ Source: https://github.com/open-pioneer
- 11. © con terra OPEN TECHNOLOGY | 11 • Drivers are Collaboration, Digital Sovereignty, Adaptability • Cloud and cloud architecture patterns are shaping open solutions • Providing technical flexibility needed to cope with uncertainty • Architectural pattern “MACH”* • Microservices • API-First • Cloud-Native • Headless UI *What is MACH Architecture, K. Aebersold, https://www.elasticpath.com/blog/what-is-mach-architecture
- 12. © con terra DATA AS A PRODUCT
- 13. © con terra DATA AS A PRODUCT | 13 • Key characteristics • Problem/solution fit • Easy to find, understand and to use • Trustworthy, quality assured • Constantly improved, roadmap and vision Data Product Thinking Data-As-A- Product/Service + = Data Data • Rethinking the approach to get value from data
- 14. © con terra | 14 Source: Overture Maps Foundation, https://overturemaps.org
- 15. © con terra | 15 Source: Overture Maps Foundation, https://overturemaps.org Source: Overture Maps Foundation, https://overturemaps.org Buildings Transportation Places Admin Units Source: TomTom, https://www.tomtom.com/newsroom/press-releases/general/27841/tomtom-co-founds- overture-maps-foundation-to-build-open-map-data/
- 16. © con terra | 16 Source: Overture Maps Foundation, https://overturemaps.org Source: Overture Maps Foundation, https://overturemaps.org Buildings Transportation POIs Admin Units
- 17. © con terra | 17 THE BLENDING OF PHYSICAL AND DIGITAL
- 18. © con terra DIGITAL TWIN AS CURRENT STATE OF A CONSTANT DEVELOPEMENT | 18 Look at the map Be active part of the map (digital space) Shared Foundational Data and Standards Information Systems Internet of Things Linked Twins “Metavers” Digital Twin Immersive Experience 3D Connected Data Realtime Analytics Prediction Linked Experience Data Knowledge Mixed Reality Look into the map Actuators Sensors
- 19. © con terra | 19 Source: Netzplan Stadt Münster: https://netzplan-muenster.de/ Source: Esri, https://livemaps360.com/DigitalTwin/HongKong/
- 20. © con terra | 20 The Digital Twin “Emergency Response” is an ongoing activity lead by the ministry of interior of the federal state of NRW. Further information will be given at the GDI-Forum NRW 20.09.2023 IN DEVELOPMENT
- 21. © con terra ARTIFICAL INTELLIGENCE
- 22. © con terra ARTIFICIAL INTELLIGENCE | 22 • Topic made a huge jump in 2023 • Generative models helps dealing with the scarcity theme • Predictive models and natural language processing help further enriching the Digital Twin and beyond
- 23. © con terra | 23 Source: https://developers.arcgis.com/python/guide/point-cloud-segmentation-using-pointcnn/ Source: NVIDIA GET3D https://developer.nvidia.com/blog/rapidly-generate-3d-assets-for-virtual-worlds-with-generative-ai/
- 24. © con terra Source: https://developers.arcgis.com/python/guide/point-cloud-segmentation-using-pointcnn/ | 24 Source: NVIDIA GET3D https://developer.nvidia.com/blog/rapidly-generate-3d-assets-for-virtual-worlds-with-generative-ai/ Source: Meta AI / https://segment-anything.com/ Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
- 25. © con terra Source: https://developers.arcgis.com/python/guide/point-cloud-segmentation-using-pointcnn/ | 25 Source: NVIDIA GET3D https://developer.nvidia.com/blog/rapidly-generate-3d-assets-for-virtual-worlds-with-generative-ai/ Source: Meta AI / https://segment-anything.com/ Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
- 26. © con terra | 26 Collaboration and Openness are key for success. Lets start today It is not only about the What, but also about the How. Digitalization and technology innovations provide essential and exciting tools. Uncertainty, scarcity and climate change will keep us busy for a while.
- 27. © con terra THANK YOU FOR YOUR ATTENTION
- 28. Bring life to data to make better decisions Working Forward: From Data to Location Intelligence for Renewables Siting and Routing
- 29. Presenter The Peak of Data Integration 20 23 Peter Veenstra Director of Data Architecture Pivvot/Terracon 32 years in geospatial industry - analyst, programmer, architect, consultant, director Location Intelligence, Software as a Service Company (4 years, 27 people, remote) Geotechnical, Environmental, Facilities, Materials Engineering Company (50+ years, 5000+ people, 175 offices/remote)
- 30. ThankYou! Brett Holmyard Dean Hintz Emily Prosperiporta Don & Dale/Safe/ConTerra
- 31. The Peak of Data Integration 20 23 Bringing life to data to make better decisions 1. Decisions lead to Results 2. Results are gathered from Information 3. Information is created by Data 4. Data is curated from Sources Image Source: www.pixabay, https://www.pipingengineer.org/pipeline-earth-work/
- 32. The Peak of Data Integration 20 23 FME brings life to our data, our platform and our data brings users to making better decisions
- 33. The Peak of Data Integration 20 23 Pipelines and Electric Transmission Routing Siting Renewables How to do it quickly with the best data ● Highly competitive market ● Land acquisition ● Public Opinion ● Political polarization Image Source: www.shawlocal.com/northwest-herald https://www.wkbn.com/ info@energynews.us
- 34. The Peak of Data Integration 20 23 Routing and Siting require location intelligence Location Intelligence requires data • The geospatial data stack • Classification - Boundary, Geotechnical, Hydrology, Land Use, property ownership, energy, infrastructure, jurisdiction, community, society, ecology, weather • https://pivvot.com/data/ 8 years in the making 123 Spatial Data Layers ½ Billion+ records in PostGIS AWS 180 Data Agencies 4000+ Data Sources FME Flow Ingest, Curate, QA/QC Row Level Metadata 30 Day / 90 Day Refresh Cycle
- 35. The Peak of Data Integration 20 23 The data needs to be right … Sourcing Research Curation Aggregation QA/QC Publication Consumption Requirements
- 36. The Peak of Data Integration 20 23 A lot of workbenches … • All loaded into FME Flow Hosted • 36 Repositories • 850+ Workbenches
- 37. The Peak of Data Integration 20 23 Source Mapping, Transformation, and Classification
- 38. The Peak of Data Integration 20 23 QA/QC • All loaded into FME Flow - Hosted • 36 Repositories • 839+ Workbenches
- 39. The Peak of Data Integration 20 23 QA/QC - Metadata
- 40. The Peak of Data Integration 20 23 QA/QC - Classification
- 41. The Peak of Data Integration 20 23 Aggregation - Smart Parcels
- 42. The Peak of Data Integration 20 23 Landcover/DEM to Tiles to POSTGIS
- 43. The Peak of Data Integration 20 23 Bringing the data to life …
- 44. The Peak of Data Integration 20 23 Raster to H3 4 7 10 ● H3 - Level 4 = 7,705 ● H3 - Level 7 = 2,642,815 ● H3 - Level 10 = 906,485,545 ● H3 - Level 11 = 2,991,492,000
- 45. The Peak of Data Integration 20 23 Raster to H3 4 7 10 Plus Raster ● H3 - Level 10 ● Roughly 20 Pixels ● Show the: ○ Max ○ Min ○ Mean ○ Mode ○ Median ● Go farther ○ Coverage Map ○ Cultural/Historical Resources Heat Map ○ 110+ data layers ○ Built for AI
- 46. The Peak of Data Integration 20 23 Route PLAE → Routing → Best Route
- 47. The Peak of Data Integration 20 23 Composite Parcels → Bigger Solar Sites 800+ 1618 987 700+ 2068 1229 600+ 2738 1583 500+ 3735 2109 400+ 5404 3176 300+ 8799 5053 200+ 17125 9850 CountSize Composite Tax
- 48. The Peak of Data Integration 20 23 Composite Parcels → Better Solar Sites 142 2231
- 49. The Peak of Data Integration 20 23 Composite Parcels → Better Solar Sites
- 50. The Peak of Data Integration 20 23 Composite Parcels → Better Solar Sites
- 51. The Peak of Data Integration 20 23 Composite Parcels → Better Solar Sites
- 52. The Peak of Data Integration 20 23 Composite Parcels → Better Solar Sites
- 53. The Peak of Data Integration 20 23 Vision of Success: Prospect for better routes/sites Make a data-driven decision to remove uncertainty & be better informed
- 54. Resources ● www.safe.com ● www.aws.com ● www.pivvot.com