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

check_X.py

@@ -0,0 +1,18 @@
+import joblib
+import numpy as np
+
+cache_file = "dataset_cache/training_data_2d.joblib"
+data = joblib.load(cache_file)
+X = np.array(data['X'])
+y = np.array(data['y'])
+
+print("X shape:", X.shape)
+print("X mean:", np.mean(X))
+print("X std:", np.std(X))
+print("X min:", np.min(X))
+print("X max:", np.max(X))
+print("Any NaN:", np.isnan(X).any())
+
+for i in range(6):
+    print(f"Channel {i} mean: {np.mean(X[:, i, :, :]):.4f}, min: {np.min(X[:, i, :, :]):.4f}, max: {np.max(X[:, i, :, :]):.4f}")
+