Geoskill: Drone Survey QC

Automated quality inspection for UAV/drone survey deliverables including aerial images, orthomosaics, DSM/DEM, control points, and aerial triangulation reports. Generates coverage, clarity, seam, and accuracy QA.

Prerequisites / 先准备 X 文件

⚠️ 必读 — 本 skill 不属于即用型，需要先准备特定文件才能跑。

本 skill 需要 无人机航测项目目录（含 ortho.tif / dsm.tif / 相机位置 / 控制点）。

👉 完整教程见仓库根目录 PREREQUISITES.md 2.2 节。

先准备 X 文件：把航测交付包按指定结构组织好；没有就先用 --synthetic 跑。

快速试跑命令：

bash

python drone_survey_qc.py --project-dir ./my_drone_project --output-dir ./qc

Drone Survey QC

Automated quality inspection for UAV survey deliverables. Checks drone aerial images, orthomosaics, DSM/DEM, control points, and aerial triangulation reports. Generates coverage, clarity, seam, and accuracy QA.

Trigger

Use when the user wants to:

• Check drone orthomosaic for holes, blur, or seam issues
• Summarize control point residuals and generate acceptance reports
• Verify image overlap and GSD meet project specifications
• Inspect DSM/DEM for nodata holes and elevation anomalies
• Generate a comprehensive QC report for survey deliverables

CLI Usage

bash

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

# With project directory
python scripts/drone_survey_qc.py --project-dir ./survey-project --output-dir ./dsq-output

# With custom QC standards
python scripts/drone_survey_qc.py --standard-config ./my-standards.json --output-dir ./dsq-output

Parameters

Parameter	Default	Description
--project-dir	None	Project directory to analyze
--orthomosaic	None	Path to orthomosaic GeoTIFF
--dsm	None	Path to DSM/DEM GeoTIFF
--camera-positions	None	Path to camera positions CSV/JSON
--control-points	None	Path to control points CSV/JSON
--standard-config	None	Path to QC standards JSON (default: references/qc_standards.json)
--output-dir	./dsq-output	Output directory

Output

File	Description
qc.json	Comprehensive QC results with all metrics
issues.geojson	GeoJSON FeatureCollection of QC issues
image_quality.csv	Per-image quality metrics (blur, exposure)
control_point_residuals.csv	Control point residual analysis
qc_report.html	Human-readable HTML QC report
request.json	Analysis request metadata
dataset-manifest.json	Dataset inventory
output-manifest.json	Output file inventory
qa.json	Quality assurance checks

QC Standards

Default thresholds from references/qc_standards.json:

Check	Minimum	Preferred
Forward overlap	70%	80%
Side overlap	60%	70%
GSD	<5.0 cm/px	<3.0 cm/px
Blur (Laplacian var)	>50	>100
GCP RMSE_XY	<5 cm	<3 cm
Ortho nodata	<2%	-
DSM nodata	<5%	-

Key Algorithms

Blur Detection

Uses Laplacian variance — lower values indicate blurrier images. Threshold: variance < 50 = blurry.

Overlap Analysis

Computes ground footprint from camera parameters (altitude, focal length, sensor size) and calculates intersection-over-minimum-area for adjacent pairs. Classifies pairs as forward (same strip) or side (cross-strip) using strip clustering on cross-strip coordinate.

Control Point Analysis

Computes XY, Z, and 3D residuals. Reports RMSE, max residual, and detects outliers using Median Absolute Deviation (MAD) with σ ≈ 1.4826 × MAD.

GSD Computation

GSD (cm/px) = (altitude × sensor_width) / (focal_length × image_width) × 100

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/CSV input
• Blur detection is resolution-dependent; calibrate thresholds for your sensor
• Control point outlier detection requires ≥7 points for MAD-based method
• Does not replace certified survey inspection for legal/compliance purposes

References

• CH/T 9024-2014 无人机航测规范
• DJI Pilot flight planning specifications
• ASPRS Positional Accuracy Standards for Digital Geospatial Data

数据下载

本 skill 可自动从 Microsoft Planetary Computer 下载数据 (无需 API key):

bash

python drone_survey_qc.py --bbox 116,39,117,40 --date-range 2024-06-01,2024-06-30 --output-dir <tmp>

• --bbox W,S,E,N: WGS-84 边界框 (西, 南, 东, 北)
• --date-range START,END: 日期范围 (YYYY-MM-DD,YYYY-MM-DD)
• --aoi-file <path.geojson>: 替代 --bbox 的 GeoJSON 多边形
• --cache-dir <path>: 缓存目录 (默认 ~/.geoskill_cache)

当用户只给 --bbox + --date-range (没有 --orthomosaic) 时，skill 自动下载数据。 当用户给 --orthomosaic 时，走原文件路径 (向后兼容)。