栅格重采样 | Raster Resampling

Implemented purely in numpy, this skill changes the raster resolution (pixel density) using three classic resampling methods while keeping the geographic extent unchanged:

• nearest: takes the value of the nearest input pixel, preserving the original value set; suitable for classification / thematic rasters.
• bilinear: distance-weighted averaging over a 2×2 neighborhood, exactly reconstructing interior pixels of linear fields; suitable for continuous data (DEM, temperature fields).
• cubic: 4×4 convolution with the Keys 1981 kernel (a=-0.5) for sharper edges.

The core is implemented as an "output pixel center → input continuous coordinate" mapping, supporting arbitrary scale factors; nodata pixels are filled with the neighborhood mean during interpolation to avoid contamination. The --synthetic mode generates a 64×64 test raster with a linear slope on the left half and a classification block on the right half.

Dependencies / 依赖

pip install numpy rasterio geopandas shapely fiona pyproj

Usage / 使用方法

Basic Usage

python geoskill-raster-resampling.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

Example 1 (synthetic data, bilinear downsampling to half, offline)

python geoskill-raster-resampling.py --bbox 116.0 39.0 117.0 40.0 --synthetic --method bilinear --scale 0.5 --output-dir ./half

Example 2: nearest-neighbor 2× upsampling (preserving class values)

python geoskill-raster-resampling.py --input landcover.tif --method nearest --scale 2.0 --output-dir ./up2

Example 3: cubic convolution resampling of a DEM

python geoskill-raster-resampling.py --input dem.tif --method cubic --scale 0.25 --output-dir ./dem_quarter

Example 4: nearest-neighbor downsampling of a synthetic raster

python geoskill-raster-resampling.py --bbox 121.0 31.0 122.0 32.0 --synthetic --method nearest --scale 0.5 --output-dir ./nn --quiet

Example 5: custom synthetic size

python geoskill-raster-resampling.py --bbox 116.0 39.0 117.0 40.0 --synthetic --size 128 --method bilinear --scale 0.5 --output-dir ./big

Output / 输出

Data Source / 数据源 / Source

• --input: local GeoTIFF
• --synthetic: locally generated test raster

Privacy / 隐私声明 / Privacy

• Runs offline by default; --synthetic mode is fully network-free.
• All processing is done locally; no user data is uploaded.

License / License

MIT

name: geoskill-raster-resampling description: '用最近邻 / 双线性 / 三次卷积改变栅格分辨率，输出重采样后的 GeoTIFF 与统计。Resample raster resolution with nearest / bilinear / cubic convolution and emit a GeoTIFF plus statistics.'

栅格重采样 | Raster Resampling

用纯 numpy 实现三种经典重采样方法改变栅格分辨率（像元密度），地理范围 保持不变：

• nearest（最近邻）：取最近输入像元值，保持原始取值集合，适合分类/ 专题栅格。
• bilinear（双线性）：2×2 邻域距离加权，对线性场内部像元精确重构， 适合连续数据（DEM、温度场）。
• cubic（三次卷积）：Keys 1981 核（a=-0.5）的 4×4 卷积，边缘更锐利。

核心按“输出像元中心 → 输入连续坐标”映射实现，支持任意缩放因子；nodata 像元在插值时用邻域均值填充以避免污染。--synthetic 模式生成左半线性 坡面、右半分类块的 64×64 测试栅格。

依赖

pip install numpy rasterio geopandas shapely fiona pyproj

使用方法

基本用法

python geoskill-raster-resampling.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

示例 1（合成数据，双线性降采样到一半，离线）

python geoskill-raster-resampling.py --bbox 116.0 39.0 117.0 40.0 --synthetic --method bilinear --scale 0.5 --output-dir ./half

示例 2：最近邻 2 倍上采样（保分类值）

python geoskill-raster-resampling.py --input landcover.tif --method nearest --scale 2.0 --output-dir ./up2

示例 3：三次卷积重采样 DEM

python geoskill-raster-resampling.py --input dem.tif --method cubic --scale 0.25 --output-dir ./dem_quarter

示例 4：合成栅格最近邻降采样

python geoskill-raster-resampling.py --bbox 121.0 31.0 122.0 32.0 --synthetic --method nearest --scale 0.5 --output-dir ./nn --quiet

示例 5：自定义合成尺寸

python geoskill-raster-resampling.py --bbox 116.0 39.0 117.0 40.0 --synthetic --size 128 --method bilinear --scale 0.5 --output-dir ./big

输出

数据源 / Source

• --input：本地 GeoTIFF
• --synthetic：本地生成测试栅格

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