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Vector-based Approach

This page explains how to use DamageScanner with vector-based exposure data (e.g. shapefiles, GeoPackages, or OSM data). This approach is ideal for object-level damage estimation, such as buildings, roads, power plants, and other individual infrastructure assets.

When to Use Vector-Based Workflows

Scenario Raster-Based Vector-Based
Infrastructure objects (e.g. bridges, roads) ❌ ✅
OpenStreetMap data ❌ ✅
Detailed local studies ❌ ✅
Exposure linked to specific geometries ❌ ✅

Required Inputs

As described in the Overview, you need:

  1. Hazard raster (e.g. flood depth, wind speed)
  2. Exposure vector (e.g. buildings, roads — as .shp, .gpkg, .pbf, or GeoDataFrame)
  3. Vulnerability curves (CSV or DataFrame)
  4. Maximum damage values (CSV, dict, or DataFrame)

⚠️ Your exposure data must include a column specifying asset type, matching the keys used in the vulnerability curves and max damage data.

Minimal Working Example

from damagescanner import DamageScanner

hazard = "data/hazard_flood_depth.tif"
feature_data = "data/infrastructure_osm.gpkg"
curves = "data/vulnerability_curves.csv"
maxdam = "data/maxdam.csv"

scanner = DamageScanner(hazard, feature_data, curves, maxdam)
damage = scanner.calculate()

The result is a GeoDataFrame of features with estimated direct damage values per asset.

Key Behavior

  • The exposure file must contain geometry columns (Point, LineString, or Polygon)
  • DamageScanner overlays each geometry with the hazard raster
  • It then samples the hazard value and looks up the damage fraction from the corresponding vulnerability curve
  • The damage is calculated as:

\ exposure_area * damage_fraction * max_damage

⚠️ Geometry Handling Tips

⚠️ Mixed Geometry Types — Avoid mixing Points and Polygons for the same asset type.

⚠️ CRS Alignment — Both hazard and vector data must be in the same coordinate reference system.

⚠️ Object Column — You must define a column that links each object to a vulnerability curve and max damage.

📚 See Also