skills.rest
bettercallfan
bettercallfanCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

impervious-surface-mapping

Detect and measure impervious surfaces from optical remote sensing images using NDBI or threshold methods.

Updated Jun 2, 2026
Search Tags:#image processing#remote sensing#impervious surface#Urban Planning#Flood Risk
One-click install
npx skills add https://github.com/bettercallfan/deerflow --skill impervious-surface-mapping
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: impervious-surface-mapping
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/remote_sensing_image/impervious-surface-mapping
Command: npx skills add https://github.com/bettercallfan/deerflow --skill impervious-surface-mapping

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires rasterio, numpy, scipy, and includes scripts (resource) and assets (resource) components.

What problem does it solve?

This Skill extracts impervious surfaces like buildings and roads from remote sensing imagery and calculates impervious surface ratios, supporting sponge city planning and urban waterlogging risk assessment.

Core Features & Use Cases

  • Impervious Surface Extraction: Detects impervious surfaces using NDBI or threshold methods from optical remote sensing imagery.
  • Surface Ratio Calculation: Computes impervious surface ratios and generates coverage maps.
  • Use Case: For urban planning and waterlogging risk assessment, this skill identifies areas with high imperviousness to assist in海绵城市 development and flood risk reduction.

Quick Start

Invoke the impervious-surface-mapping skill by providing an optical image and specifying the extraction method.

Frequently Asked Questions about impervious-surface-mapping

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract impervious surfaces from optical remote sensing imagery?▼

Impervious surface extraction from optical remote sensing imagery detects buildings, roads, and parking lots using NDBI or threshold methods. It identifies non-absorbent areas to support urban planning and flood risk assessment.

What is an impervious surface ratio and how is it calculated?▼

An impervious surface ratio measures the proportion of non-absorbent surfaces like buildings and roads within a specific area. It is calculated using optical remote sensing imagery to generate coverage maps for waterlogging risk assessment.

Can I use rasterio and numpy for urban waterlogging risk assessment?▼

Yes, rasterio and numpy process optical remote sensing imagery for waterlogging risk assessment. These dependencies compute impervious surface ratios and identify high-risk flood zones for sponge city development.

What is the best way to generate an impervious surface coverage map for urban planning?▼

Generating an impervious surface coverage map for urban planning involves applying NDBI or threshold methods to optical remote sensing images. This calculates surface ratios to identify high imperviousness areas for sponge city development.

Do I need scipy to calculate impervious surface ratios?▼

Scipy is optional for calculating impervious surface ratios. While rasterio and numpy handle core image processing, scipy is only needed for additional zone identification tasks during coverage map generation.

Related Skills

Explore More Data & Analytics Skills→
tondevreltondevrel
19

rasterio

Read and write georeferenced raster data with a Pythonic GDAL interface.

Community
Intermediate
bettercallfanbettercallfan

flood-risk-mapping

Generate urban flood risk maps from optical imagery and DEM elevation data.

Community
Intermediate
xjtulycxjtulyc
33

urban-remote-sensing

Extract urban land cover and heat island patterns from satellite imagery.

Community
Advanced
bettercallfanbettercallfan

urban-expansion-analysis

Analyze multi-year land use change and urban expansion patterns from classified satellite imagery.

Community
Intermediate