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

fix_cache.py

@@ -0,0 +1,19 @@
+import joblib
+import geopandas as gpd
+import numpy as np
+
+cache_file = "dataset_cache/training_data_2d.joblib"
+data = joblib.load(cache_file)
+X = data['X']
+print("X shape:", len(X))
+
+gdf = gpd.read_file("train/ST_training_data_updated_1130points_new.shp")
+gdf = gdf.to_crs("EPSG:32648")
+print("gdf length:", len(gdf))
+
+if len(X) == len(gdf):
+    y = [(row['HT_code'] - 1) for idx, row in gdf.iterrows()]
+    joblib.dump({'X': X, 'y': y}, cache_file)
+    print("Fixed y in cache! Saved.")
+else:
+    print("Lengths do not match, cannot fix automatically.")