thêm chức năng train trên odc predict trên planetary
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Victor Phan
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"""
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Load Sentinel data without ODC Database
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Sử dụng Microsoft Planetary Computer STAC API
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Usage:
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from load_data_no_odc import load_sentinel2_stac, load_sentinel1_stac
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"""
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import xarray as xr
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import numpy as np
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import pandas as pd
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from pystac_client import Client
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import planetary_computer
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import odc.stac
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from datetime import datetime
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def load_sentinel2_stac(
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bbox, # (lon_min, lat_min, lon_max, lat_max)
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date_range, # ("2022-09-01", "2023-10-01")
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bands=None,
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resolution=10,
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chunks={'time': 1, 'x': 2048, 'y': 2048}
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):
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"""
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Load Sentinel-2 L2A data from Microsoft Planetary Computer
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Args:
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bbox: Bounding box (lon_min, lat_min, lon_max, lat_max)
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date_range: Tuple of start and end dates
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bands: List of bands to load (default: ['red', 'nir', 'scl'])
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resolution: Spatial resolution in meters
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chunks: Dask chunk sizes
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Returns:
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xarray.Dataset with Sentinel-2 data
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"""
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if bands is None:
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bands = ['red', 'nir', 'blue', 'green', 'nir08', 'swir16', 'swir22', 'SCL']
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print(f"🔍 Searching Sentinel-2 data...")
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print(f" Bbox: {bbox}")
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print(f" Date: {date_range[0]} to {date_range[1]}")
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print(f" Bands: {bands}")
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# Connect to Planetary Computer STAC
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catalog = Client.open(
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"https://planetarycomputer.microsoft.com/api/stac/v1",
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modifier=planetary_computer.sign_inplace,
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)
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# Search for Sentinel-2 items
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search = catalog.search(
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collections=["sentinel-2-l2a"],
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bbox=bbox,
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datetime=f"{date_range[0]}/{date_range[1]}",
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)
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items = list(search.items())
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print(f"✅ Found {len(items)} Sentinel-2 scenes")
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if len(items) == 0:
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print("⚠ No data found for the given parameters")
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return None
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# Load data using odc-stac
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print("📥 Loading data...")
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try:
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data = odc.stac.load(
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items,
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bands=bands,
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bbox=bbox,
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resolution=resolution,
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chunks=chunks,
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groupby="solar_day",
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)
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print(f"✅ Loaded Sentinel-2 data: {data.dims}")
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print(f" Shape: {dict(data.dims)}")
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print(f" Bands: {list(data.data_vars)}")
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return data
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except Exception as e:
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print(f"❌ Error loading data: {e}")
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import traceback
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traceback.print_exc()
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return None
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def load_sentinel1_stac(
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bbox, # (lon_min, lat_min, lon_max, lat_max)
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date_range, # ("2022-09-01", "2023-10-01")
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bands=None,
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resolution=10,
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chunks={'time': 1, 'x': 2048, 'y': 2048}
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):
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"""
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Load Sentinel-1 RTC data from Microsoft Planetary Computer
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Args:
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bbox: Bounding box (lon_min, lat_min, lon_max, lat_max)
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date_range: Tuple of start and end dates
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bands: List of bands to load (default: ['vv', 'vh'])
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resolution: Spatial resolution in meters
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chunks: Dask chunk sizes
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Returns:
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xarray.Dataset with Sentinel-1 data
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"""
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if bands is None:
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bands = ['vv', 'vh']
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print(f"🔍 Searching Sentinel-1 data...")
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print(f" Bbox: {bbox}")
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print(f" Date: {date_range[0]} to {date_range[1]}")
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print(f" Bands: {bands}")
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# Connect to Planetary Computer STAC
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catalog = Client.open(
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"https://planetarycomputer.microsoft.com/api/stac/v1",
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modifier=planetary_computer.sign_inplace,
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)
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# Search for Sentinel-1 RTC items
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search = catalog.search(
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collections=["sentinel-1-rtc"],
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bbox=bbox,
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datetime=f"{date_range[0]}/{date_range[1]}",
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)
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items = list(search.items())
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print(f"✅ Found {len(items)} Sentinel-1 scenes")
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if len(items) == 0:
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print("⚠ No data found for the given parameters")
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return None
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# Load data using odc-stac
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print("📥 Loading data...")
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try:
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data = odc.stac.load(
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items,
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bands=bands,
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bbox=bbox,
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resolution=resolution,
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chunks=chunks,
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groupby="solar_day",
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)
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# Rename bands to uppercase for consistency
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if 'vv' in data.data_vars:
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data = data.rename({'vv': 'VV', 'vh': 'VH'})
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print(f"✅ Loaded Sentinel-1 data: {data.dims}")
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print(f" Shape: {dict(data.dims)}")
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print(f" Bands: {list(data.data_vars)}")
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return data
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except Exception as e:
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print(f"❌ Error loading data: {e}")
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import traceback
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traceback.print_exc()
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return None
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def mask_clean_s2(data, scl_band='SCL'):
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"""
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Apply cloud mask to Sentinel-2 data using SCL band
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Good pixels: 2=dark, 4=vegetation, 5=not-vegetated, 6=water
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"""
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print("🧹 Applying cloud mask...")
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if scl_band not in data.data_vars:
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print(f"⚠ Warning: {scl_band} band not found, skipping cloud mask")
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return data
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# Good pixel values
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good_pixels = [2, 4, 5, 6]
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# Create mask
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mask = xr.zeros_like(data[scl_band], dtype=bool)
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for pixel_val in good_pixels:
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mask = mask | (data[scl_band] == pixel_val)
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# Apply mask to all bands except SCL
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data_vars = [v for v in data.data_vars if v != scl_band]
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result = data[data_vars].where(mask)
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print(f"✅ Cloud mask applied")
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return result
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def calculate_ndvi(data, nir_band='nir08', red_band='red'):
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"""
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Calculate NDVI from Sentinel-2 data
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"""
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print(f"📊 Calculating NDVI using {nir_band} and {red_band}...")
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if nir_band not in data.data_vars or red_band not in data.data_vars:
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print(f"⚠ Warning: Required bands not found")
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print(f" Available bands: {list(data.data_vars)}")
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return data
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nir = data[nir_band]
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red = data[red_band]
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ndvi = (nir - red) / (nir + red)
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ndvi = ndvi.rename('NDVI')
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# Add NDVI to dataset
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result = xr.merge([data, ndvi])
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print(f"✅ NDVI calculated")
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return result
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def fill_nan_temporal(data, dim='time'):
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"""
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Fill NaN values using forward/backward fill along time dimension
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"""
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print(f"🔧 Filling NaN values using temporal interpolation...")
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filled = data.bfill(dim=dim).ffill(dim=dim)
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print(f"✅ NaN values filled")
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return filled
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def resample_monthly(data, method='mean'):
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"""
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Resample data to monthly frequency
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"""
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print(f"📅 Resampling to monthly using {method}...")
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if method == 'mean':
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result = data.resample(time='1MS').mean()
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elif method == 'median':
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result = data.resample(time='1MS').median()
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else:
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raise ValueError(f"Unknown method: {method}")
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print(f"✅ Resampled to monthly: {result.dims}")
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return result
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# ══════════════════════════════════════════════════════════════════════════════
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# CONVENIENCE WRAPPER
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# ══════════════════════════════════════════════════════════════════════════════
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def load_and_process_s2(bbox, date_range, apply_cloud_mask=True, calculate_indices=True):
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"""
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Load and process Sentinel-2 data in one step
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"""
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# Load data
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data = load_sentinel2_stac(bbox, date_range)
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if data is None:
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return None
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# Apply cloud mask
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if apply_cloud_mask:
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data = mask_clean_s2(data)
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# Calculate NDVI
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if calculate_indices:
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data = calculate_ndvi(data)
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# Fill NaN
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data = fill_nan_temporal(data)
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# Resample to monthly
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data_monthly = resample_monthly(data)
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return data_monthly
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def load_and_process_s1(bbox, date_range):
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"""
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Load and process Sentinel-1 data in one step
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"""
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# Load data
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data = load_sentinel1_stac(bbox, date_range)
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if data is None:
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return None
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# Resample to monthly
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data_monthly = resample_monthly(data)
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return data_monthly
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# Print module info
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print("=" * 70)
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print("📦 Data Loading Module (No ODC Database Required)")
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print("=" * 70)
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print("\n💡 Usage:")
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print(" from load_data_no_odc import load_and_process_s2, load_and_process_s1")
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print("\n bbox = (lon_min, lat_min, lon_max, lat_max)")
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print(" date_range = ('2022-09-01', '2023-10-01')")
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print("\n data_s2 = load_and_process_s2(bbox, date_range)")
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print(" data_s1 = load_and_process_s1(bbox, date_range)")
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print("=" * 70)
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print()
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