| Name | cnwigee JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | |
| Summary | Your description of the project |
| upload_time | 2023-06-22 14:15:43 |
| maintainer | |
| docs_url | None |
| author | Ryan Hamilton |
| requires_python | |
| license | |
| keywords |
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| VCS |
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No requirements were recorded.
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# CNWI-GEE
Canadian National Wetland Inventory Google Earth Engie Random Forest Classifications. Provides high
level access for doing standardized random forest wetland classifications.
# Installation and Setup
This package is built to be used with the annaconda project. For best user experience use some
recent version of conda. These walk though will be using a miniconda3
## Dependencies
- Geopandas
- google earth engine
```sh
conda create -n cnwi-gee python=3.10 -c conda-forge earthengine-api geopandas pandas
```
```sh
# Step 2): activate new conda env and authenticate earth engine api
$ conda activate cnwi-gee
# authenticate earth engine api
(cnwi-gee) $ earthengine authenticate
```
# Example Pipeline
```python
from dataclasses import dataclass
import ee
from cnwi import inputs, rf, td, funcs, sfilters
from cnwi import prebuilt
def main():
# load dataset
dataset = ee.FeatureCollection("users/ryangilberthamilton/BC/williston/fpca/willistonA_no_floodplain")
williston = prebuilt.WillistonA()
# create a training object
training = td.TrainingData(
collection=dataset,
label='cDesc'
)
# create s1 inputs
s1s: List[ee.Image] = inputs.s1_inputs(williston.s1)
# create s2 inputs
s2s: List[ee.Image] = inputs.s2_inputs(williston.s2)
# create elevation inputs
elevation = inputs.nasa_dem()
filter = sfilters.gaussian_filter(3)
smoothed = filter(elevation)
slope = ee.Terrain.slope(smoothed)
# Create the inputs stack
stack = ee.Image.cat(*s1s, *s2s, smoothed, slope)
# sample the stack
training.sample(
stack=stack
)
# create the rf model
model = rf.RandomForestModel()
# train the model
trained = model.train(
training_data=training.samples,
predictors=stack.bandNames(),
classProperty=training.value
)
# classify the image
classified_img = stack.classify(trained).uint8()
# export image and samples to cloud
return sys.exit(0)
if __name__ == '__main__':
main()
```
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"description": "# CNWI-GEE\r\nCanadian National Wetland Inventory Google Earth Engie Random Forest Classifications. Provides high\r\nlevel access for doing standardized random forest wetland classifications.\r\n\r\n# Installation and Setup\r\nThis package is built to be used with the annaconda project. For best user experience use some \r\nrecent version of conda. These walk though will be using a miniconda3\r\n## Dependencies\r\n- Geopandas\r\n- google earth engine\r\n\r\n```sh\r\nconda create -n cnwi-gee python=3.10 -c conda-forge earthengine-api geopandas pandas\r\n```\r\n\r\n```sh\r\n# Step 2): activate new conda env and authenticate earth engine api\r\n$ conda activate cnwi-gee\r\n# authenticate earth engine api\r\n(cnwi-gee) $ earthengine authenticate\r\n```\r\n\r\n# Example Pipeline\r\n```python\r\nfrom dataclasses import dataclass\r\n\r\nimport ee\r\n\r\nfrom cnwi import inputs, rf, td, funcs, sfilters\r\nfrom cnwi import prebuilt\r\n\r\n\r\n\r\ndef main():\r\n # load dataset\r\n dataset = ee.FeatureCollection(\"users/ryangilberthamilton/BC/williston/fpca/willistonA_no_floodplain\")\r\n williston = prebuilt.WillistonA()\r\n \r\n # create a training object\r\n training = td.TrainingData(\r\n collection=dataset,\r\n label='cDesc'\r\n )\r\n \r\n # create s1 inputs\r\n s1s: List[ee.Image] = inputs.s1_inputs(williston.s1)\r\n \r\n # create s2 inputs\r\n s2s: List[ee.Image] = inputs.s2_inputs(williston.s2)\r\n \r\n # create elevation inputs\r\n elevation = inputs.nasa_dem()\r\n filter = sfilters.gaussian_filter(3)\r\n smoothed = filter(elevation)\r\n slope = ee.Terrain.slope(smoothed)\r\n \r\n # Create the inputs stack\r\n stack = ee.Image.cat(*s1s, *s2s, smoothed, slope)\r\n \r\n # sample the stack\r\n training.sample(\r\n stack=stack\r\n )\r\n \r\n # create the rf model\r\n model = rf.RandomForestModel()\r\n # train the model\r\n trained = model.train(\r\n training_data=training.samples,\r\n predictors=stack.bandNames(),\r\n classProperty=training.value\r\n )\r\n \r\n # classify the image\r\n classified_img = stack.classify(trained).uint8()\r\n \r\n # export image and samples to cloud\r\n return sys.exit(0)\r\n \r\n \r\nif __name__ == '__main__':\r\n main()\r\n```\r\n",
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