Skip to content

Overview

DamageScanner() is a Python toolkit for direct damage assessments of natural hazards. While originally designed for flood risk analysis, it can be used for any hazard where vulnerability can be expressed as a one-dimensional curve (e.g., flood depth, wind speed, ground shaking).

The tool is optimized for both raster-based (e.g. land use) and vector-based (e.g., roads, power plants, buildings) damage assessments. This page walks you through how it works, and what is required for running a successful analysis.

📚 For a very extensive overview of real-world examples, please refer to the GlobalInfraRisk documentation.

Core Workflow

The DamageScanner logic consists of three key steps:

  1. Exposure Analysis — identifies what assets intersect the hazard.
  2. Damage Calculation — estimates damage using vulnerability curves.
  3. Risk Assessment — aggregates damage across hazard return periods.

Inputs Required

The DamageScanner class requires four key inputs:

1. Hazard Data

  • Raster (GeoTIFF, NetCDF) or path to raster files
  • Represents hazard intensity (e.g., flood depth, wind speed)

2. Exposure Data (aka feature_data)

  • Vector formats: .shp, .gpkg, .pbf, .geoparquet, or GeoDataFrames
  • Raster exposure layers (.tif, .nc) also supported
  • Automatically detects type from file extension

3. Vulnerability Curves

  • CSV or pandas.DataFrame
  • Relates hazard intensity to damage (as fraction of max damage)
Intensity residential industrial farmland
0.0 0.00 0.00 0.00
0.5 0.25 0.15 0.10
1.0 0.50 0.35 0.25

⚠️ Important: The unit of the first column/index must match the unit of the hazard layer (e.g., meters for flood depth).

4. Maximum Damage Values

  • Specifies the max value per asset type (e.g., €/m² or €/asset)
  • Provided as dict, CSV, or DataFrame
landuse damage
residential 1000
industrial 5000
farmland 50

Interactive Examples

To see DamageScanner in action with real-world data, explore our interactive Jupyter Notebook examples:

  • 📊 Vector-based assessment — Demonstrates how to use OSM polygons and points (e.g., land use, buildings) with raster hazard maps.
  • 🗺️ Raster-based assessment — Shows how to perform rapid large-scale analysis using land-use rasters.

📚 Next Steps