feat: implement comprehensive land cover classification pipeline with model benchmarking and experiment logging

test_empty_points.py

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+import geopandas as gpd
+import planetary_computer
+import pystac_client
+import odc.stac
+import numpy as np
+from shapely.geometry import Point, shape
+from pyproj import Transformer
+
+bbox = [105.5, 9.2, 106.3, 10.0]
+time_range = "2023-01-01/2023-04-30"
+catalog = pystac_client.Client.open(
+    "https://planetarycomputer.microsoft.com/api/stac/v1",
+    modifier=planetary_computer.sign_inplace,
+)
+items = list(catalog.search(collections=["sentinel-2-l2a"], bbox=bbox, datetime=time_range, query={"eo:cloud_cover": {"lt": 30}}).items())
+items = sorted(items, key=lambda x: x.properties["eo:cloud_cover"])
+
+gdf = gpd.read_file("train/ST_training_data_updated_1130points_new.shp")
+gdf = gdf.to_crs("EPSG:32648")
+
+# Find a point that fails. Let's just test a few points.
+for idx, row in gdf.head(20).iterrows():
+    x_coord = row['geometry'].x
+    y_coord = row['geometry'].y
+    
+    transformer = Transformer.from_crs("epsg:32648", "epsg:4326", always_xy=True)
+    lon, lat = transformer.transform(x_coord, y_coord)
+    point = Point(lon, lat)
+    
+    filtered = []
+    for item in items:
+        if shape(item.geometry).contains(point):
+            filtered.append(item)
+            
+    filtered = [planetary_computer.sign(item) for item in filtered]
+    if not filtered:
+        print(f"Point {idx}: NO ITEMS CONTAINS POINT!")
+        continue
+        
+    ds = odc.stac.load(
+        filtered,
+        bands=["B02"],
+        x=(x_coord - 80, x_coord + 80),
+        y=(y_coord - 80, y_coord + 80),
+        crs="EPSG:32648",
+        resolution=10,
+        patch_url=planetary_computer.sign,
+        fail_on_error=False
+    ).compute()
+    
+    sums = ds["B02"].sum(dim=["x", "y"]).values
+    non_zero = (sums > 0).sum()
+    print(f"Point {idx}: {len(filtered)} items, {non_zero} non-zero time steps")