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Inspire Helsinki 2019 Data Challenge Let´s make the most out of INSPIRE, team Minerva IntelligenceTeam minerva intelligence


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The Inspire Helsinki 2019 event brought together around 170 people from 29 countries to foster discussion and new ideas on how to realise the full potential of spatial data. The three-day event featured data challenges, practical hands-on workshops and future-oriented keynote presentations. The event was summed up in a panel discussion, in which perspectives on tackling remaining challenges were brought up.

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Inspire Helsinki 2019 Data Challenge Let´s make the most out of INSPIRE, team Minerva IntelligenceTeam minerva intelligence

  1. 1. Let’s Make the Most of INSPIRE A Landslide Application In Veneto, Italy Inspire Helsinki 2019 – Data Challenge
  2. 2. Minerva | Who we are 2 Name (from left to Right) Role Sharon Lam Web Map Development Jake McGregor Team Lead Blake Boyko GIS Analysis Gio Roberti Landslide expert Bryan Barnhart Dev-Ops Clinton Smyth Project Supervisor • Vancouver, Canada • Daramstadt, Germany Our technology combines machine intelligence with human intelligence, to reach conclusions faster than possible with humans alone, but with the explanations needed to trust the results.
  3. 3. Preview | Web Application 3 1. Web Map 2. Feature Pop Up 3. Match Report 4. Explanation
  4. 4. Preview | Web Application 4 1. Web Map 2. Feature Pop Up 3. Runout Hazard Landslide Hazard Runout Visualization
  5. 5. INSPIRE | The Use Case The INSPIRE data structure and code list vocabularies simplifies and expedites pre-processing of data. 5 Standard Workflow: Variable and Time Consuming .shp .csv .pdf SEMANTICS INSPIRE workflow: Streamlined .gml! .wfs! SEMANTICS
  6. 6. DATA CHALLENGE | Workflow 6 Collect Data Align to INSPIRE Encode Spatial Features Calculate Susceptibility Present Results ExtendINSPIRE
  7. 7. TECHNICAL SUBMISSION | Code list Extension 7 Landslide code list extension • Based on the Varnes landslide classification • Built with ACE • 48 different classes structured hierarchically • Logically consistent structure • Publicly available via the Minerva Re3gistry
  8. 8. TECHNICAL SUBMISSION | Schema Extension The Natural Risk Zone Core Schema show here with the susceptibilityExtension Included 8
  9. 9. Unfortunately INSPIRE data could not be found for the chosen study area. To overcome this challenge we aligned critical input layers to INSPIRE terminology and data structure. All input layers play a role in assessing the relative spatial likelihood of Susceptibility Area features. INSPIRE aligned datasets were mapped to INSPIRE schemas using Hale studio and hosted as WMS in Hale connect for view in the web map. Input data was sourced primarily from the Veneto geoportal: Complete list of data sources can be found on the project website. ANALYSIS | Input Data 9 Italian Landslide Inventory • IFFI Landslides Geology • Faults • Soils Transportation • Railroads • Roads INSPIRE aligned • Streams • Land Cover (CORINE) • Geology Environment • Lakes • Watersheds • Permafrost • Fires • Slope
  10. 10. ANALYSIS | Instance Generation 10 Spatial overlay analysis • Custom QGIS tool used to attribute vector features with information from all input datasets. • Different encoding relationships are used for different data types Semantic network conversion • Alignment to terminological standards • Conversion to semantic network format Spatial unit of analysis generation • Stream buffer polygons for flow type hazards • Slope unit polygons for slide and fall type hazards
  11. 11. MODEL INSTANCE ITEM 1 ITEM 2 ITEM 3 THIS THAT ITM 1 ITEM 3 OTHER THING ITEM 1 THIS THIS ITEM 2 has has has has has has has has has has has has Conceptual models of different landslide types Slope unit and stream buffer features • Spatial features are encoded as semantic networks and compared against conceptual models of different landslide types. • Scores are award based on type of match and semantic distance within the taxonomy. • Matching report is generated to provide explainability of scoring. • Features are symbolized based on percentage matched. • Landslide runouts are generated polygons with highest relative spatial likelihood of occurrence. ANALYSIS | Minerva Reasoning Engine 11
  12. 12. ANALYSIS | Ontological control Ontology Definition In an ontology, we define: • The classification of things • The relationship between things Therefore the ontology understands the semantics of defined domain Ontology significance • An ontology controls the vocabulary used in data • Ontology is used in logic based reasoning Debris Flow Conceptual models of different landslide types Erosional Process Steep 2 Fan(s) Colluvium 12
  13. 13. 13 ANALYSIS | Output Output Alignment with Susceptibility Schema • Analysis output data of both susceptibility area features and hazard area features are aligned with the natural risk zone susceptibility Extension. Runout calculation • Most susceptible instances for each model were selected for hazard assessment • Runouts calculated based on Landslide Size Class Add to map • Outputs and Input datasets were visualized in the web mapping application. • Map is then moved to a production environment.
  14. 14. WORKFLOW | Tools r.avaflow 2.0 To calculate landslide impact zones Slope Units Delineation To calculate slope unit instance polygons Hale Studio To align input and output datasets to INSPIRE Hale Connect To serve INSPIRE dataset map services Open Layers + Boundless SDK To visualize interactiveweb map Azure Kubernetes Service To host scalable web applications 14
  15. 15. 15 Application | The Map
  16. 16. 16 | Thank You Special Thanks The folks at We Transform Alex and Marco Daniele Francioli at the JRC Sharon Lam , Gio Roberti, Blake Boyko, Bryan Barnhart & Clinton Smyth