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

test_spatial_filter.py

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+import geopandas as gpd
+import planetary_computer
+import pystac_client
+import odc.stac
+import numpy as np
+import time
+from shapely.geometry import Point, box, shape
+
+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())
+
+x = 561609
+y = 1024183
+
+start = time.time()
+# Filter items by spatial intersection
+from pyproj import Transformer
+# The items geometry are in EPSG:4326 (lon, lat)
+# Our x, y are in EPSG:32648
+transformer = Transformer.from_crs("epsg:32648", "epsg:4326", always_xy=True)
+lon, lat = transformer.transform(x, y)
+point = Point(lon, lat)
+
+filtered_items = []
+for item in items:
+    geom = shape(item.geometry)
+    if geom.contains(point):
+        filtered_items.append(item)
+
+filtered_items = sorted(filtered_items, key=lambda x: x.properties["eo:cloud_cover"])
+
+print("Original items:", len(items))
+print("Filtered items:", len(filtered_items))
+print("Time to filter:", time.time() - start)
+
+start = time.time()
+filtered_items = [planetary_computer.sign(item) for item in filtered_items]
+ds = odc.stac.load(filtered_items[:4], bands=["B02"], x=(x-80, x+80), y=(y-80, y+80), crs="EPSG:32648", resolution=10, patch_url=planetary_computer.sign, fail_on_error=False).compute()
+print("Time to load 4 items:", time.time() - start)
+print(ds["B02"].shape)