geoskill-建筑高度估算

原始内容

---
name: building-footprint-height
description: >
  Extract building footprints and estimate height, floor count proxy, and
  volume from DSM/DTM/LiDAR data. Produces 2.5D urban models for 3D city
  modeling, population downscaling, and risk exposure analysis.
---

# Building Footprint Height

Extract building heights from elevation data (DSM, DTM, LiDAR point cloud)
and building footprints. Estimates height, floor count proxy, volume, and
quality codes for each building.

## Trigger

Use when the user wants to:
- Estimate building heights from DSM/DTM raster data
- Compute building volumes and floor count proxies
- Generate 2.5D urban models for 3D city visualization
- Assess building data quality and flag anomalies
- Prepare building data for population downscaling or risk exposure

## CLI Usage

```bash
# Synthetic demo mode (no input files needed)
python scripts/building_footprint_height.py --output-dir ./bfh-output

# With custom floor height assumption
python scripts/building_footprint_height.py --floor-height 3.6 --output-dir ./bfh-output

# With point cloud method
python scripts/building_footprint_height.py --height-method point_cloud_quantile --output-dir ./bfh-output

# With custom standards
python scripts/building_footprint_height.py --standard-config ./my-standards.json --output-dir ./bfh-output
```

## Parameters

| Parameter | Default | Description |
|---|---|---|
| `--dsm` | None | Path to DSM GeoTIFF |
| `--dtm` | None | Path to DTM GeoTIFF |
| `--footprints` | None | Path to building footprints GeoJSON/Shapefile |
| `--point-cloud` | None | Path to LiDAR point cloud (LAS/CSV) |
| `--height-method` | `dsm_minus_dtm` | Height estimation method |
| `--floor-height` | 3.0 | Assumed floor height in meters |
| `--output-dir` | ./bfh-output | Output directory |
| `--standard-config` | None | Path to building height standards JSON |
| `--bbox` | None | W,S,E,N in WGS-84 (auto-downloads Copernicus GLO-30 DEM) |
| `--date-range` | None | START,END ISO-8601 (optional for time-invariant DEM) |
| `--aoi-file` | None | GeoJSON polygon; its bbox is used for the query |
| `--cache-dir` | None | Override the data-fetcher cache directory |

## 数据下载 (Data Download)

This skill can auto-download the elevation input from the Microsoft
Planetary Computer STAC catalog. No API key is required.

```bash
# Download one Copernicus GLO-30 DEM tile over central Beijing and run the
# pipeline using it as a stand-in DSM (the script falls back to a
# percentile-DTM approximation when no DTM is supplied).
python scripts/building_footprint_height.py \
  --bbox 116.0,39.5,116.8,40.0 \
  --output-dir ./bfh-output
```

The downloaded asset is cached under `~/.geoskill_cache/` so a second run
with the same `--bbox` reuses the file. The download route also accepts
`--aoi-file my_polygon.geojson` instead of `--bbox`.

## Height Methods

| Method | Priority | Requirements | Quality |
|---|---|---|---|
| `dsm_minus_dtm` | 1 (recommended) | DSM + DTM rasters | Code 1 (best) |
| `point_cloud_quantile` | 2 | LiDAR point cloud | Code 2 |
| `shadow_based` | 3 | Shadow length + solar angle | Code 3 |

## Output

| File | Description |
|---|---|
| `buildings_3d.geojson` | Building footprints with height/volume/floors |
| `height.tif` (.npy + meta) | Building height raster |
| `building_stats.csv` | Per-building statistics |
| `quality_flags.geojson` | Buildings with quality issues |
| `report.html` | Human-readable HTML report |
| `request.json` | Analysis request metadata |
| `dataset-manifest.json` | Dataset inventory |
| `output-manifest.json` | Output file inventory |
| `qa.json` | Quality assurance checks |

## Quality Codes

| Code | Label | Meaning |
|---|---|---|
| 1 | 高度可靠 | DSM-DTM, coverage >80% |
| 2 | 高度较可靠 | Point cloud quantile, >50 points |
| 3 | 高度估算 | Shadow-based or coarse DEM |
| 4 | 高度可疑 | Coverage <50% or anomaly detected |
| 5 | 高度缺失 | No valid data |

## Key Algorithms

### DSM-DTM Height
Height = quantile(DSM_footprint, 0.95) - median(DTM_footprint)

Uses robust quantile to exclude outliers (antennas, trees). Edge buffer
(default 0.5m) excludes mixed-boundary pixels.

### Point Cloud Quantile
Height = quantile(points_z, 0.95) - quantile(points_z, 0.05)

Requires ≥10 points per building. Ground reference is 5th percentile.

### Floor Count Proxy
floors = round(height / floor_height)

Floor height is an assumption (default 3.0m residential). Min/max range
accounts for ±0.5m uncertainty.

### Volume
V = footprint_area × height × roof_factor

Roof factors: flat=1.0, pitched=0.85, complex=0.9

## Exit Codes

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

## Limitations

- Synthetic demo mode only; file-based mode requires GeoTIFF/GeoJSON input
- Floor count is a proxy — actual floors may differ
- Shadow method requires manual shadow length measurement
- Does not produce true 3D mesh models (2.5D only)
- Coarse DEM (e.g., SRTM) should NOT be used for individual building heights
- Tree mixing may inflate height estimates in vegetated areas

## References

- OSM Building Heights dataset
- Microsoft Building Footprints
- AHN (Actueel Hoogtebestand Nederland) DSM/DTM
- USGS 3DEP LiDAR point clouds

name: building-footprint-height description: > Extract building footprints and estimate height, floor count proxy, and volume from DSM/DTM/LiDAR data. Produces 2.5D urban models for 3D city modeling, population downscaling, and risk exposure analysis.

Building Footprint Height

Extract building heights from elevation data (DSM, DTM, LiDAR point cloud) and building footprints. Estimates height, floor count proxy, volume, and quality codes for each building.

Trigger

Use when the user wants to:

Estimate building heights from DSM/DTM raster data
Compute building volumes and floor count proxies
Generate 2.5D urban models for 3D city visualization
Assess building data quality and flag anomalies
Prepare building data for population downscaling or risk exposure

CLI Usage

# Synthetic demo mode (no input files needed)
python scripts/building_footprint_height.py --output-dir ./bfh-output

# With custom floor height assumption
python scripts/building_footprint_height.py --floor-height 3.6 --output-dir ./bfh-output

# With point cloud method
python scripts/building_footprint_height.py --height-method point_cloud_quantile --output-dir ./bfh-output

# With custom standards
python scripts/building_footprint_height.py --standard-config ./my-standards.json --output-dir ./bfh-output

Parameters

Parameter	Default	Description
`--dsm`	None	Path to DSM GeoTIFF
`--dtm`	None	Path to DTM GeoTIFF
`--footprints`	None	Path to building footprints GeoJSON/Shapefile
`--point-cloud`	None	Path to LiDAR point cloud (LAS/CSV)
`--height-method`	`dsm_minus_dtm`	Height estimation method
`--floor-height`	3.0	Assumed floor height in meters
`--output-dir`	./bfh-output	Output directory
`--standard-config`	None	Path to building height standards JSON
`--bbox`	None	W,S,E,N in WGS-84 (auto-downloads Copernicus GLO-30 DEM)
`--date-range`	None	START,END ISO-8601 (optional for time-invariant DEM)
`--aoi-file`	None	GeoJSON polygon; its bbox is used for the query
`--cache-dir`	None	Override the data-fetcher cache directory

数据下载 (Data Download)

This skill can auto-download the elevation input from the Microsoft Planetary Computer STAC catalog. No API key is required.

# Download one Copernicus GLO-30 DEM tile over central Beijing and run the
# pipeline using it as a stand-in DSM (the script falls back to a
# percentile-DTM approximation when no DTM is supplied).
python scripts/building_footprint_height.py \
  --bbox 116.0,39.5,116.8,40.0 \
  --output-dir ./bfh-output

The downloaded asset is cached under ~/.geoskill_cache/ so a second run with the same --bbox reuses the file. The download route also accepts --aoi-file my_polygon.geojson instead of --bbox.

Height Methods

Method	Priority	Requirements	Quality
`dsm_minus_dtm`	1 (recommended)	DSM + DTM rasters	Code 1 (best)
`point_cloud_quantile`	2	LiDAR point cloud	Code 2
`shadow_based`	3	Shadow length + solar angle	Code 3

Output

File	Description
`buildings_3d.geojson`	Building footprints with height/volume/floors
`height.tif` (.npy + meta)	Building height raster
`building_stats.csv`	Per-building statistics
`quality_flags.geojson`	Buildings with quality issues
`report.html`	Human-readable HTML report
`request.json`	Analysis request metadata
`dataset-manifest.json`	Dataset inventory
`output-manifest.json`	Output file inventory
`qa.json`	Quality assurance checks

Quality Codes

Code	Label	Meaning
1	高度可靠	DSM-DTM, coverage >80%
2	高度较可靠	Point cloud quantile, >50 points
3	高度估算	Shadow-based or coarse DEM
4	高度可疑	Coverage <50% or anomaly detected
5	高度缺失	No valid data

Key Algorithms

DSM-DTM Height

Height = quantile(DSM_footprint, 0.95) - median(DTM_footprint)

Uses robust quantile to exclude outliers (antennas, trees). Edge buffer (default 0.5m) excludes mixed-boundary pixels.

Point Cloud Quantile

Height = quantile(points_z, 0.95) - quantile(points_z, 0.05)

Requires ≥10 points per building. Ground reference is 5th percentile.

Floor Count Proxy

floors = round(height / floor_height)

Floor height is an assumption (default 3.0m residential). Min/max range accounts for ±0.5m uncertainty.

Volume

V = footprint_area × height × roof_factor

Roof factors: flat=1.0, pitched=0.85, complex=0.9

Exit Codes

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

Limitations

Synthetic demo mode only; file-based mode requires GeoTIFF/GeoJSON input
Floor count is a proxy — actual floors may differ
Shadow method requires manual shadow length measurement
Does not produce true 3D mesh models (2.5D only)
Coarse DEM (e.g., SRTM) should NOT be used for individual building heights
Tree mixing may inflate height estimates in vegetated areas

References

OSM Building Heights dataset
Microsoft Building Footprints
AHN (Actueel Hoogtebestand Nederland) DSM/DTM
USGS 3DEP LiDAR point clouds