---

name: impervious-surface-mapping description: > Estimate impervious surface fraction from multi-band satellite imagery (Sentinel-2) using spectral indices (NDBI, NDVI, MNDWI). Supports binary classification and continuous fraction estimation, with zone-level aggregation and change detection. Use when mapping urban impervious surfaces, computing impervious ratios by watershed/admin unit, or analyzing temporal changes in built-up areas.

Impervious Surface Mapping

GIS/remote sensing workflow for estimating impervious surface fraction from multi-band satellite imagery. Uses spectral indices and sub-pixel estimation to produce continuous impervious fraction maps, with optional binary thresholding, zone aggregation, and change detection.

Trigger

Use when the user wants to:

• Estimate impervious surface fraction from satellite imagery
• Map built-up areas using NDBI and related spectral indices
• Compute impervious ratios by street, community, or watershed
• Compare impervious surface changes between years
• Mask out water and vegetation before impervious analysis

CLI Usage

# Basic fraction estimation
python scripts/impervious_surface_mapping.py \
  --raster sentinel2.tif \
  --year 2024 \
  --mode fraction

# Binary classification with threshold
python scripts/impervious_surface_mapping.py \
  --raster sentinel2.tif \
  --year 2024 \
  --mode binary \
  --threshold 0.5

# Zone aggregation
python scripts/impervious_surface_mapping.py \
  --raster sentinel2.tif \
  --year 2024 \
  --mode fraction \
  --aggregation-layer watersheds.geojson

# Change detection
python scripts/impervious_surface_mapping.py \
  --raster sentinel2_2024.tif \
  --year 2024 \
  --compare-year 2020 \
  --raster-compare sentinel2_2020.tif \
  --mode fraction

Data Download

This skill can auto-fetch a Sentinel-2 L2A scene from the Microsoft Planetary Computer when given a bounding box + date range. The script picks the B04 (red) asset by default; you can change prefer_assets in the code to use visual for an RGB composite.

python scripts/impervious_surface_mapping.py \
  --bbox 116,39,117,40 \
  --date-range 2024-06-01,2024-06-30 \
  --output-dir ./impervious-output

The PYTHONPATH must include the parent of _geoskill_data_fetcher/ (the same directory the 50 skills live in). Set it once:

export PYTHONPATH="/path/to/行业Skill创意-20260727"

Parameters

Parameter	Default	Description
--raster	required	Multi-band raster (Sentinel-2: B2,B3,B4,B8,B11)
--year	required	Analysis year
--mode	fraction	binary or fraction
--training-data	None	Training samples GeoJSON (with impervious field)
--threshold	0.5	Threshold for binary mode
--aggregation-layer	None	Zone layer GeoJSON for aggregation
--compare-year	None	Comparison year for change detection
--raster-compare	None	Raster for comparison year
--ndvi-mask	0.6	NDVI threshold to mask dense vegetation
--mndwi-mask	0.0	MNDWI threshold to mask water
--output-dir	./impervious-output	Output directory

Output

File	Description
impervious_fraction.tif	Continuous impervious fraction [0, 1]
impervious_binary.tif	Binary impervious mask (1=impervious)
zones_summary.csv	Zone-level statistics (if aggregation-layer given)
change.tif	Change raster (fraction difference)
accuracy.json	Accuracy metrics (if training-data given)

Exit Codes

Code	Meaning
0	Success
2	Argument error
3	Dependency missing
6	Data validation failure
7	Processing failure