geoskill-parking-lot-detection

停车场检测 | Parking Lot Detection

Detects parking lots by fusing spectral, textural, and geometric features, supporting urban facility surveys and land-use mapping.

Core algorithm: asphalt score = low-brightness factor × low-vegetation factor (absolute scale); marking density extracts high-frequency bright lines via Sobel gradient + brightness threshold; regularity characterizes row/column periodicity with the ratio of local to global variance; the composite score is a weighted sum clipped to [0,1], and thresholding segments out the parking lots.

Dependencies / 依赖

pip install 'numpy' 'rasterio' 'scipy'

Usage / 使用方法

Basic usage

python geoskill-parking-lot-detection.py --bbox 116.0 39.0 117.0 40.0 [other options]

Examples

Example 1 (synthetic data (offline))

python geoskill-parking-lot-detection.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

Example 2 (use case 2)

python geoskill-parking-lot-detection.py --input multispectral.tif --output-dir ./out

Example 3 (use case 3)

python geoskill-parking-lot-detection.py --bbox 121.0 31.0 122.0 32.0 --threshold 0.5 --output-dir ./out --quiet

Example 4 (use case 4)

python geoskill-parking-lot-detection.py --input ms.tif --regularity-block 24 --output-dir ./out

Example 5 (use case 5)

python geoskill-parking-lot-detection.py --bbox 116.0 39.0 117.0 40.0 --synthetic --threshold 0.35 --output-dir ./out --quiet

Output / 输出

Data Source / 数据源 / Source

Local multispectral GeoTIFF (Red, NIR); --synthetic mode simulates parking lots with regular markings plus vegetation/roof control areas.

Privacy / 隐私声明 / Privacy

• Runs offline by default; --synthetic mode requires no network at all.
• All processing is performed locally; user data is never uploaded.

License / License

MIT

---

---

name: geoskill-parking-lot-detection description: 'Detect parking lots using asphalt spectral signature, regular row and column texture, and painted marking density.'

停车场检测 | Parking Lot Detection

融合光谱、纹理与几何特征检测停车场，服务于城市设施调查与用地制图。

核心算法：沥青分数 = 低亮度因子 × 低植被因子（绝对标度）；标线密度由 Sobel 梯度 + 亮度阈值提取高频亮线；规则性用局部方差/全局方差之比刻画行列周期性；综合评分 = 加权和，裁剪到 [0,1]，阈值分割出停车场。

依赖

pip install 'numpy' 'rasterio' 'scipy'

使用方法

基本用法

python geoskill-parking-lot-detection.py --bbox 116.0 39.0 117.0 40.0 [其他参数]

示例

示例 1（合成数据（离线））

python geoskill-parking-lot-detection.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

示例 2（用法 2）

python geoskill-parking-lot-detection.py --input multispectral.tif --output-dir ./out

示例 3（用法 3）

python geoskill-parking-lot-detection.py --bbox 121.0 31.0 122.0 32.0 --threshold 0.5 --output-dir ./out --quiet

示例 4（用法 4）

python geoskill-parking-lot-detection.py --input ms.tif --regularity-block 24 --output-dir ./out

示例 5（用法 5）

python geoskill-parking-lot-detection.py --bbox 116.0 39.0 117.0 40.0 --synthetic --threshold 0.35 --output-dir ./out --quiet

输出

数据源 / Source

本地多光谱 GeoTIFF（Red, NIR）；--synthetic 模式模拟含规则标线的停车场与植被/屋顶对照区。

隐私声明 / Privacy

• 默认离线运行，--synthetic 模式完全无网络。
• 所有处理在本地完成，不上传用户数据。

License

MIT