Hazard Mapping Essentials with Earth Observation (EO) Data

This Quest centers on collecting and annotating Earth Observation imagery to pinpoint hazard-prone geospatial features such as floodplains, landslide corridors, urban heat islands, or coastal erosion zones. The objective is twofold: (1) gather fine-grained hazard intelligence essential to local risk planning, and (2) develop an open geospatial resource for subsequent modeling, forecast validation, and policy alignment. By systematically tagging and verifying these features, contributors foster a baseline reference that helps unify multi-layer data (demographics, historical incidents) into meaningful risk-lens analytics. The focus on robust design includes adopting standardized naming conventions (e.g., OGC-compliant metadata), employing spatiotemporal indexing, and using geostatistical checks to minimize error margins. This ensures that newly tagged data remains scalable for advanced correlation (e.g., displacement triggers, parametric thresholds) and ethically grounded under responsible research and innovation (RRI) guidelines.

On an expert-architecture level, the hazard mapping pipeline might utilize containerized microservices to process input imagery, store annotations in version-controlled layers (e.g., Cloud Optimized GeoTIFF or Zarr format), and produce a unified shapefile or vector layer for open risk knowledge libraries. By collaborating with local domain experts (coastal engineers, climatologists), participants ensure the mapping remains inclusive of ephemeral hazards or unregistered localities often ignored by standard risk classification. This Quest thus merges geospatial analytics with RRI-based disclaimers on data resolution, local sovereignty, and possible uncertainties.

Key Outputs

1. Annotated Geospatial Layers: Containing hazard polygons (e.g., flood-prone zones, slope stability classes) plus metadata (projection, date, disclaimers).
2. Hazard Classification Summary: A descriptive overview that documents recognized patterns, uncharted anomalies, and recommended disclaimers around ephemeral or marginalized zones.
3. Integration Plan: Steps to feed these annotated layers into subsequent analyses (e.g., parametric finance triggers, early-warning notifications) while maintaining RRI compliance.

10 Steps

• Data Provisioning: Acquire EO imagery (e.g., Sentinel-2 or Landsat archives) from open data repositories
• Environment Setup: Configure a specialized geospatial annotation environment (QGIS or an online labeling tool) ensuring consistent coordinate reference systems (CRS)
• Review Existing Mappings: Cross-check official or local hazard references to identify gap regions or unverified risk areas (earns initial eCredits)
• Labeling & Version Control: Delineate polygons for suspected flood basins, slopes, or erosion hotspots, tagging them with local attributes (soil type, vegetation, land use)
• Peer Collaboration: Post partial annotation files to a shared workspace for feedback from domain experts or local community participants
• Statistical Consistency Check: Compare your hazard polygons with historical incident data (landslides, floods), evaluating precision or recall metrics (pCredits on validated findings)
• Sovereignty & RRI Note: Create disclaimers if image resolution or local privacy norms raise concerns. Document potential biases (urban vs. rural coverage)
• Draft Hazard Inventory: Summarize key observations, noting data-limited areas that might need further ground-truth or local interviews
• Peer-Driven Refinement: Solicit final commentary from local environment authorities or researchers, adjusting polygons or disclaimers accordingly (partial vCredits upon robust peer validation)
• Merge & Final Submission: Integrate the final shapefile or vector layers into the platform’s open risk library, awarding full credit distribution once an RRI oversight committee acknowledges the new hazard layer