Add GeoWind2Plan data
#2
by ShaoxiangQin - opened
- README.md +21 -2
- geowind2plan_data/block_A/buildings.npy +3 -0
- geowind2plan_data/block_A/sdf.npy +3 -0
- geowind2plan_data/block_A/wind_cfd.npy +3 -0
- geowind2plan_data/block_A/wind_cfd_2ms.npy +3 -0
- geowind2plan_data/block_A/wind_cfd_8ms.npy +3 -0
- geowind2plan_data/block_A/wind_predicted.npy +3 -0
- geowind2plan_data/block_B/buildings.npy +3 -0
- geowind2plan_data/block_B/sdf.npy +3 -0
- geowind2plan_data/block_B/wind_cfd.npy +3 -0
- geowind2plan_data/block_B/wind_predicted.npy +3 -0
- geowind2plan_data/block_C/buildings.npy +3 -0
- geowind2plan_data/block_C/sdf.npy +3 -0
- geowind2plan_data/block_C/wind_cfd.npy +3 -0
- geowind2plan_data/block_C/wind_predicted.npy +3 -0
- geowind2plan_data/block_D/buildings.npy +3 -0
- geowind2plan_data/block_D/sdf.npy +3 -0
- geowind2plan_data/block_D/wind_cfd.npy +3 -0
- geowind2plan_data/block_D/wind_predicted.npy +3 -0
- geowind2plan_data/block_E/buildings.npy +3 -0
- geowind2plan_data/block_E/sdf.npy +3 -0
- geowind2plan_data/block_E/wind_cfd.npy +3 -0
- geowind2plan_data/block_E/wind_predicted.npy +3 -0
- geowind2plan_data/profile_wind.npy +3 -0
README.md
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@@ -4,7 +4,7 @@ license: cc-by-nc-4.0
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# CityFFD-UrbanMicroclimate
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Time-averaged 3D wind velocity fields for 33 urban areas in 11 cities, solved with the CityFFD urban airflow solver, together with the gridded building geometry used in each simulation. The dataset was introduced in [FLUME](https://arxiv.org/abs/2503.19708) and is also used in [GeoWind2Plan](https://arxiv.org/abs/2609.36056) ([code](https://github.com/DUAL-Xiao/GeoWind2Plan)).
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**License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
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The inflow is along +x at 4 m/s, with a power-law profile of exponent 0.15 and reference height 10 m. Velocities are time averages taken after the flow statistics converged.
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If you use this dataset, please cite:
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# CityFFD-UrbanMicroclimate
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Time-averaged 3D wind velocity fields for 33 urban areas in 11 cities, solved with the CityFFD urban airflow solver, together with the gridded building geometry used in each simulation. The dataset was introduced in [FLUME](https://arxiv.org/abs/2503.19708) and is also used in [GeoWind2Plan](https://arxiv.org/abs/2609.36056) ([demo](https://geowind2plan.com/), [code](https://github.com/DUAL-Xiao/GeoWind2Plan)). The data used in GeoWind2Plan, ready to use with its code, are in `geowind2plan_data/`.
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**License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
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The inflow is along +x at 4 m/s, with a power-law profile of exponent 0.15 and reference height 10 m. Velocities are time averages taken after the flow statistics converged.
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# GeoWind2Plan data
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`geowind2plan_data/` is the input data of the [GeoWind2Plan code](https://github.com/DUAL-Xiao/GeoWind2Plan). To run the code, place the contents of this folder in the `data/` folder of the code.
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It holds five of the urban areas above, `hkg13`, `mtr01`, `mel02`, `syd02` and `osa01`, which are blocks A to E in GeoWind2Plan. Their building geometry and CFD wind are cropped to the UAV flight heights of GeoWind2Plan (30-120 m) and stored in the axis order of the code, which differs from `data/<id>.nc`. The folder also holds the wind predicted by neural operator for each block.
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```
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geowind2plan_data/
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profile_wind.npy height-only wind profile, one value per layer (m/s)
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block_A/ ... block_E/
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buildings.npy building occupancy
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sdf.npy signed distance to buildings (m), positive in free space
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wind_cfd.npy CFD wind, 4 m/s inflow
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wind_predicted.npy wind predicted by neural operator, 4 m/s inflow
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block_A/
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wind_cfd_2ms.npy CFD wind, 2 m/s inflow
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wind_cfd_8ms.npy CFD wind, 8 m/s inflow
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```
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# Citation
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If you use this dataset, please cite:
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geowind2plan_data/block_A/buildings.npy
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version https://git-lfs.github.com/spec/v1
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geowind2plan_data/block_A/sdf.npy
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version https://git-lfs.github.com/spec/v1
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size 21960128
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geowind2plan_data/block_A/wind_cfd.npy
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version https://git-lfs.github.com/spec/v1
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size 65880128
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geowind2plan_data/block_A/wind_cfd_2ms.npy
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version https://git-lfs.github.com/spec/v1
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size 65880128
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geowind2plan_data/block_A/wind_predicted.npy
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version https://git-lfs.github.com/spec/v1
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size 65880128
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geowind2plan_data/block_B/buildings.npy
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version https://git-lfs.github.com/spec/v1
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geowind2plan_data/block_B/sdf.npy
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version https://git-lfs.github.com/spec/v1
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geowind2plan_data/block_B/wind_cfd.npy
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version https://git-lfs.github.com/spec/v1
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size 65880128
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geowind2plan_data/block_B/wind_predicted.npy
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version https://git-lfs.github.com/spec/v1
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size 65880128
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geowind2plan_data/block_C/buildings.npy
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version https://git-lfs.github.com/spec/v1
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geowind2plan_data/block_C/sdf.npy
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geowind2plan_data/block_C/wind_cfd.npy
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size 65880128
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geowind2plan_data/block_D/buildings.npy
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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size 65880128
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geowind2plan_data/block_E/buildings.npy
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version https://git-lfs.github.com/spec/v1
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geowind2plan_data/block_E/sdf.npy
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version https://git-lfs.github.com/spec/v1
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size 137250128
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geowind2plan_data/block_E/wind_cfd.npy
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version https://git-lfs.github.com/spec/v1
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size 411750128
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geowind2plan_data/block_E/wind_predicted.npy
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version https://git-lfs.github.com/spec/v1
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size 411750128
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geowind2plan_data/profile_wind.npy
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version https://git-lfs.github.com/spec/v1
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size 372
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