update

README.md

@@ -1,6 +1,9 @@
 Dataset for domain shift test in Sen1Floods11 training set, this dataset is manually curated for flood and cloud occlusions test set from WorldFloodsv2.
 Based on the paper **Cross-Resolution Domain Shift: From U-Net Encoder–Decoder to the Prithvi Hybrid Model** _Gunawan, A. A. S., Kam, R. M., & Andrew, H._ 
 
+Each samples varies in HxW pixel count, refering to `sliding_window_crop()`. Given an image tensor of shape `(C, H, W)`, the function scans across the spatial dimensions in steps of `stride=224`, extracting a `224×224` window at each position.
+
+**Edge handling** : When a window would extend beyond the image boundary, the endpoint is clamped to the image edge (`y_end = min(y + 224, H)`), and the start is *recalculated backwards* from that clamped end (`y_start = max(y_end - 224, 0)`). This means the last patch in any row or column overlaps with the second-to-last patch rather than being smaller, every patch is guaranteed to be exactly `224×224`.
 
 ---
 

process_patches.py

@@ -47,9 +47,6 @@ os.makedirs(output_root_s2, exist_ok=True)
 output_root_gt = "./datasets/Timor_Processed/GT"
 os.makedirs(output_root_gt, exist_ok=True)
 
-output_root_floodmask = "./datasets/Timor_Processed/Floodmask"
-os.makedirs(output_root_gt, exist_ok=True)
-
 def sliding_window_crop(image, window_size=PATCH_SIZE, stride=STRIDE):
     C, H, W = image.shape
     patches = []
@@ -149,8 +146,7 @@ def plot_label_patches(label_patches, cols=5, save_path=None):
     if save_path:
         grid_img.save(save_path)
 
-
-# Main processing loop
+ 
 for tif_path, satellite in files_s2:
     print(f"Processing {tif_path} ({satellite})...")
     img_tensor = read_tif_as_tensor(tif_path)
@@ -177,8 +173,7 @@ for tif_path, satellite in files_s2:
     #     plot_label_path = os.path.join(patch_output_dir, f"{base_name}_labels_grid.png")
     #     plot_label_patches(label_patches, save_path=plot_label_path)
 
-
-# Main processing loop
+ 
 for tif_path, satellite in files_gt:
     print(f"Processing {tif_path} ({satellite})...")
     img_tensor = read_tif_as_tensor(tif_path)