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utils.py
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utils.py
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import dask.array as da
import joblib
import xarray as xr
from dask_ml.wrappers import ParallelPostFit
from datacube.utils.geometry import assign_crs
from xarray import Dataset
from pystac_client import Client
from odc.stac import load
import os
from planetary_computer import sign_url
DEP_CATALOG = "https://stac.staging.digitalearthpacific.org"
MSPC_CATALOG = "https://planetarycomputer.microsoft.com/api/stac/v1/"
def load_data(
bbox: tuple[float],
chunks: dict = dict(x=2048, y=2048),
resolution: int = 10,
datetime: str = "2023",
) -> Dataset:
dep_client = Client.open("https://stac.staging.digitalearthpacific.org")
collection = "dep_s2_geomad"
items = list(
dep_client.search(
collections=[collection], bbox=bbox, datetime=datetime
).items()
)
bands = [
"B02",
"B03",
"B04",
"B05",
"B06",
"B07",
"B08",
"B8A",
"B11",
"B12",
"emad",
"bcmad",
"smad",
]
data = load(
items,
bbox=bbox,
measurements=bands,
resolution=resolution,
chunks=chunks,
).squeeze("time")
# Incorporate NDVI (Normalised Difference Vegetation Index) = (NIR-red)/(NIR+red)
data["ndvi"] = (data["B08"] - data["B04"]) / (data["B08"] + data["B04"])
# Incorporate MNDWI (Mean Normalised Difference Water Index) = (Green – SWIR) / (Green + SWIR)
data["mndwi"] = (data["B03"] - data["B12"]) / (data["B03"] + data["B12"])
# Incorporate EVI (Enhanced Vegetation Index) = 2.5NIR−RED(NIR+6RED−7.5BLUE)+1
data["evi"] = (2.5 * (data["B08"] - data["B04"])) * (
(data["B08"] + (6 * (data["B04"]) - (7.5 * (data["B02"]))))
) + 1
# Incorporate SAVI (Standard Vegetation Index) = (800nm−670nm) / (800nm+670nm+L(1+L)) # where L = 0.5
data["savi"] = (data["B07"] - data["B04"]) / (
data["B07"] + data["B04"] + 0.5 * (1 + 0.5)
)
# Incorporate BSI (Bare Soil Index) = ((B11 + B4) - (B8 + B2)) / ((B11 + B4) + (B8 + B2)) # https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/barren_soil/
data["bsi"] = ((data["B11"] + data["B04"]) - (data["B08"] + data["B02"])) / (
(data["B11"] + data["B04"]) + (data["B08"] + data["B02"])
)
# Incorporate NDMI (Normalised Difference Moisture Index) # https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/ndmi/
data["ndmi"] = ((data["B08"]) - (data["B11"])) / ((data["B08"]) + (data["B11"]))
# Incorporate NDBI (Normalised Difference Built-up Index) (B06 - B05) / (B06 + B05); # - built up ratio of vegetation to paved surface - let BU = (ndvi - ndbi) - https://custom-scripts.sentinel-hub.com/custom-scripts/landsat-8/built_up_index/
data["ndbi"] = ((data["B06"]) - (data["B05"])) / ((data["B06"]) + (data["B05"]))
# TODO: rotate key and grab it from an actual environment variable
os.environ["PC_SDK_SUBSCRIPTION_KEY"] = "84162f5502174b1b838239e74a44898d"
mspc_client = Client.open("https://planetarycomputer.microsoft.com/api/stac/v1/")
# Get a pystac client for the MSPC
items_dem = list(
mspc_client.search(collections=["cop-dem-glo-30"], bbox=bbox).items()
)
data_dem = load(
items_dem, chunks=chunks, groupby="solar_day", like=data, patch_url=sign_url
)
data_dem = (
data_dem.where(data_dem != -32768).rename({"data": "elevation"}).squeeze("time")
)
data_s2_dem = data.update(data_dem)
items_s1 = list(
dep_client.search(
collections=["dep_s1_mosaic"], bbox=bbox, datetime=datetime
).items()
)
data_s1 = load(
items_s1,
like=data,
chunks=chunks,
measurements=["mean_vv", "mean_vh"],
).squeeze("time")
data_s1["mean_vv_vh"] = (data_s1["mean_vv"]) / (data_s1["mean_vh"])
merged = data_s2_dem.update(data_s1)
return merged
def predict_xr(
model, input_xr, chunk_size=None, persist=False, proba=False, clean=False
):
"""
Predict using a scikit-learn model on an xarray dataset.
Shamelessly ripped from dea_tools
"""
# if input_xr isn't dask, coerce it
dask = True
if not bool(input_xr.chunks):
dask = False
input_xr = input_xr.chunk({"x": len(input_xr.x), "y": len(input_xr.y)})
# set chunk size if not supplied
if chunk_size is None:
chunk_size = int(input_xr.chunks["x"][0]) * int(input_xr.chunks["y"][0])
def _predict_func(model, input_xr, persist, proba, clean):
x, y, crs = input_xr.x, input_xr.y, input_xr.geobox.crs
input_data = []
for var_name in input_xr.data_vars:
input_data.append(input_xr[var_name])
input_data_flattened = []
for arr in input_data:
data = arr.data.flatten().rechunk(chunk_size)
input_data_flattened.append(data)
# reshape for prediction
input_data_flattened = da.array(input_data_flattened).transpose()
if clean is True:
input_data_flattened = da.where(
da.isfinite(input_data_flattened), input_data_flattened, 0
)
if (proba is True) & (persist is True):
# persisting data so we don't require loading all the data twice
input_data_flattened = input_data_flattened.persist()
# apply the classification
print("predicting...")
out_class = model.predict(input_data_flattened)
# Mask out NaN or Inf values in results
if clean is True:
out_class = da.where(da.isfinite(out_class), out_class, 0)
# Reshape when writing out
out_class = out_class.reshape(len(y), len(x))
# stack back into xarray
output_xr = xr.DataArray(out_class, coords={"x": x, "y": y}, dims=["y", "x"])
output_xr = output_xr.to_dataset(name="predictions")
if proba is True:
print(" probabilities...")
out_proba = model.predict_proba(input_data_flattened)
# convert to %
out_proba = da.max(out_proba, axis=1) * 100.0
if clean is True:
out_proba = da.where(da.isfinite(out_proba), out_proba, 0)
out_proba = out_proba.reshape(len(y), len(x))
out_proba = xr.DataArray(
out_proba, coords={"x": x, "y": y}, dims=["y", "x"]
)
output_xr["probabilities"] = out_proba
return assign_crs(output_xr, str(crs))
if dask is True:
# convert model to dask predict
model = ParallelPostFit(model)
with joblib.parallel_backend("dask"):
output_xr = _predict_func(model, input_xr, persist, proba, clean)
else:
output_xr = _predict_func(model, input_xr, persist, proba, clean).compute()
return output_xr