Performance of extract_points vs rasterio sample
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评估
- 难度
- 5/5
- 预计耗时
- 一周以上
- 新手友好度
- 30/100
- Issue 类型
- 缺陷
- 描述清晰度
- 需要澄清
- 活跃度
- 停滞
- 技术栈
- python
- 领域
- performance
调研方向
Reproduce the comparison using the linked notebook and the Prague DTM, then inspect xvec/accessor.py around lines 1261-1263 where extract_points delegates to sel(method='nearest'). Compare that behavior with rasterio's sample path, and consider the available xarray and rioxarray routes. Done means identifying and documenting a supported approach that materially improves lazy-raster point extraction.
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描述
One of the questions in the recent Earthmover's webinar on xvec was about the performance of extract_points compared to rasterio's sample method. I have never tested this before so wanted to give it a go and for a large lazy-loaded raster (digital terrain model), our extract_points is waaaay slower. See https://notebooksharing.space/view/4459f651d27b2f214f8590c30aba0782f7515d4c16b00dcd3283492f19f8e694#displayOptions=
The DTM is from https://geoportalpraha.cz/en/data-and-services/97d2c9c11aa9478cb21b469b8a4f820e in case you'd like to test the same but any raster should do the trick I assume.
Under the hood, extract_points is simply passing the coordinates to .sel with method='nearest', which should be doing exactly the same as rasterio's sample. https://github.com/xarray-contrib/xvec/blob/66b541bd509b4bcaade0bbeed3dd90b852b602a3/xvec/accessor.py#L1261-L1263
This is not optimal.
We could possibly use sample via rioxarray if the raster is loaded via xarray as it is available through dtm_da.rio._manager.acquire().sample(list(zip(x, y))) but that is relying on a private API of rasterio. I'll open an issue there if there's an appetite to expose sample on the rio accessor.
Outside of relying on rasterio, is there a way of speeding it up using some xarray magic?
- 主要语言
- Python
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