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diff --git a/winter_dataset/sen12ms_cr_dataLoader.py b/winter_dataset/sen12ms_cr_dataLoader.py
new file mode 100644
index 0000000..8c06d32
--- /dev/null
+++ b/winter_dataset/sen12ms_cr_dataLoader.py
@@ -0,0 +1,286 @@
+"""
+ Generic data loading routines for the SEN12MS-CR dataset of corresponding Sentinel 1,
+ Sentinel 2 and cloudy Sentinel 2 data.
+
+ The SEN12MS-CR class is meant to provide a set of helper routines for loading individual
+ image patches as well as triplets of patches from the dataset. These routines can easily
+ be wrapped or extended for use with many deep learning frameworks or as standalone helper
+ methods. For an example use case please see the "main" routine at the end of this file.
+
+ NOTE: Some folder/file existence and validity checks are implemented but it is
+ by no means complete.
+
+ Authors: Patrick Ebel (patrick.ebel@tum.de), Lloyd Hughes (lloyd.hughes@tum.de),
+ based on the exemplary data loader code of https://mediatum.ub.tum.de/1474000, with minimal modifications applied.
+"""
+
+import os
+import rasterio
+
+import numpy as np
+
+from enum import Enum
+from glob import glob
+
+
+class S1Bands(Enum):
+ VV = 1
+ VH = 2
+ ALL = [VV, VH]
+ NONE = []
+
+
+class S2Bands(Enum):
+ B01 = aerosol = 1
+ B02 = blue = 2
+ B03 = green = 3
+ B04 = red = 4
+ B05 = re1 = 5
+ B06 = re2 = 6
+ B07 = re3 = 7
+ B08 = nir1 = 8
+ B08A = nir2 = 9
+ B09 = vapor = 10
+ B10 = cirrus = 11
+ B11 = swir1 = 12
+ B12 = swir2 = 13
+ ALL = [B01, B02, B03, B04, B05, B06, B07, B08, B08A, B09, B10, B11, B12]
+ RGB = [B04, B03, B02]
+ NONE = []
+
+
+class Seasons(Enum):
+ SPRING = "ROIs1158_spring"
+ SUMMER = "ROIs1868_summer"
+ FALL = "ROIs1970_fall"
+ WINTER = "ROIs2017_winter"
+ ALL = [SPRING, SUMMER, FALL, WINTER]
+
+
+class Sensor(Enum):
+ s1 = "s1"
+ s2 = "s2"
+ s2cloudy = "s2cloudy"
+
+# Note: The order in which you request the bands is the same order they will be returned in.
+
+
+class SEN12MSCRDataset:
+ def __init__(self, base_dir):
+ self.base_dir = base_dir
+
+ if not os.path.exists(self.base_dir):
+ raise Exception(
+ "The specified base_dir for SEN12MS-CR dataset does not exist")
+
+ """
+ Returns a list of scene ids for a specific season.
+ """
+
+ def get_scene_ids(self, season):
+ season = Seasons(season).value
+
+ # Check if season folder exists directly
+ path = os.path.join(self.base_dir, season)
+
+ # If season folder doesn't exist, try with _s1 suffix (alternative structure)
+ if not os.path.exists(path):
+ path = os.path.join(self.base_dir, season + "_s1")
+
+ if not os.path.exists(path):
+ raise NameError("Could not find season {} in base directory {}".format(
+ season, self.base_dir))
+
+ # add all dirs except "s2_cloudy" (which messes with subsequent string splits)
+ scene_list = [os.path.basename(s)
+ for s in glob(os.path.join(path, "*")) if "s2_cloudy" not in s]
+ scene_list = [int(s.split("_")[1]) for s in scene_list]
+ return set(scene_list)
+
+ """
+ Returns a list of patch ids for a specific scene within a specific season
+ """
+
+ def get_patch_ids(self, season, scene_id):
+ season = Seasons(season).value
+ path = os.path.join(self.base_dir, season, f"s1_{scene_id}")
+
+ # If path doesn't exist, try with _s1 suffix
+ if not os.path.exists(path):
+ path = os.path.join(self.base_dir, season + "_s1", f"s1_{scene_id}")
+
+ if not os.path.exists(path):
+ raise NameError(
+ "Could not find scene {} within season {}".format(scene_id, season))
+
+ patch_ids = [os.path.splitext(os.path.basename(p))[0]
+ for p in glob(os.path.join(path, "*"))]
+ patch_ids = [int(p.rsplit("_", 1)[1].split("p")[1]) for p in patch_ids]
+
+ return patch_ids
+
+ """
+ Return a dict of scene ids and their corresponding patch ids.
+ key => scene_ids, value => list of patch_ids
+ """
+
+ def get_season_ids(self, season):
+ season = Seasons(season).value
+ ids = {}
+ scene_ids = self.get_scene_ids(season)
+
+ for sid in scene_ids:
+ ids[sid] = self.get_patch_ids(season, sid)
+
+ return ids
+
+ """
+ Returns raster data and image bounds for the defined bands of a specific patch
+ This method only loads a sinlge patch from a single sensor as defined by the bands specified
+ """
+
+ def get_patch(self, season, scene_id, patch_id, bands, is_cloudy=False):
+ season = Seasons(season).value
+ sensor = None
+
+ if isinstance(bands, (list, tuple)):
+ b = bands[0]
+ else:
+ b = bands
+
+ if isinstance(b, S1Bands):
+ sensor = Sensor.s1.value
+ bandEnum = S1Bands
+ elif isinstance(b, S2Bands):
+ sensor = Sensor.s2.value if not is_cloudy else "s2_cloudy"
+ bandEnum = S2Bands
+ else:
+ raise Exception("Invalid bands specified")
+
+ if isinstance(bands, (list, tuple)):
+ bands = [b.value for b in bands]
+ else:
+ bands = bands.value
+
+ scene = "{}_{}".format("s2_cloudy" if is_cloudy else sensor.replace("_cloudy", ""), scene_id)
+ filename = "{}_{}_p{}.tif".format(season, scene, patch_id)
+
+ # Try standard structure first: base_dir/season/scene/filename
+ patch_path = os.path.join(self.base_dir, season, scene, filename)
+
+ # If not found, try alternative structure: base_dir/season_sensor/scene/filename
+ if not os.path.exists(patch_path):
+ patch_path = os.path.join(self.base_dir, season + "_" + sensor, scene, filename)
+
+ with rasterio.open(patch_path) as patch:
+ data = patch.read(bands)
+ bounds = patch.bounds
+
+ if len(data.shape) == 2:
+ data = np.expand_dims(data, axis=0)
+
+ return data, bounds
+
+ """
+ Returns a triplet of patches. S1, S2 and cloudy S2 as well as the geo-bounds of the patch
+ """
+
+ def get_s1s2s2cloudy_triplet(self, season, scene_id, patch_id, s1_bands=S1Bands.ALL, s2_bands=S2Bands.ALL, s2cloudy_bands=S2Bands.ALL):
+ s1, bounds = self.get_patch(season, scene_id, patch_id, s1_bands, is_cloudy=False)
+ s2, _ = self.get_patch(season, scene_id, patch_id, s2_bands, is_cloudy=False)
+ s2cloudy, _ = self.get_patch(season, scene_id, patch_id, s2cloudy_bands, is_cloudy=True)
+
+ return s1, s2, s2cloudy, bounds
+
+ """
+ Returns a triplet of numpy arrays with dimensions D, B, W, H where D is the number of patches specified
+ using scene_ids and patch_ids and B is the number of bands for S1, S2 or cloudy S2
+ """
+
+ def get_triplets(self, season, scene_ids=None, patch_ids=None, s1_bands=S1Bands.ALL, s2_bands=S2Bands.ALL, s2cloudy_bands=S2Bands.ALL):
+ season = Seasons(season)
+ scene_list = []
+ patch_list = []
+ bounds = []
+ s1_data = []
+ s2_data = []
+ s2cloudy_data = []
+
+ # This is due to the fact that not all patch ids are available in all scenes
+ # And not all scenes exist in all seasons
+ if isinstance(scene_ids, list) and isinstance(patch_ids, list):
+ raise Exception("Only scene_ids or patch_ids can be a list, not both.")
+
+ if scene_ids is None:
+ scene_list = self.get_scene_ids(season)
+ else:
+ try:
+ scene_list.extend(scene_ids)
+ except TypeError:
+ scene_list.append(scene_ids)
+
+ if patch_ids is not None:
+ try:
+ patch_list.extend(patch_ids)
+ except TypeError:
+ patch_list.append(patch_ids)
+
+ for sid in scene_list:
+ if patch_ids is None:
+ patch_list = self.get_patch_ids(season, sid)
+
+ for pid in patch_list:
+ s1, s2, s2cloudy, bound = self.get_s1s2s2cloudy_triplet(
+ season, sid, pid, s1_bands, s2_bands, s2cloudy_bands)
+ s1_data.append(s1)
+ s2_data.append(s2)
+ s2cloudy_data.append(s2cloudy)
+ bounds.append(bound)
+
+ return np.stack(s1_data, axis=0), np.stack(s2_data, axis=0), np.stack(s2cloudy_data, axis=0), bounds
+
+
+if __name__ == "__main__":
+ import time
+ # Load the dataset specifying the base directory
+ sen12mscr = SEN12MSCRDataset(".")
+
+ spring_ids = sen12mscr.get_season_ids(Seasons.SPRING)
+ cnt_patches = sum([len(pids) for pids in spring_ids.values()])
+ print("Spring: {} scenes with a total of {} patches".format(
+ len(spring_ids), cnt_patches))
+
+ start = time.time()
+ # Load the RGB bands of the first S2 patch in scene 8
+ SCENE_ID = 8
+ s2_rgb_patch, bounds = sen12mscr.get_patch(Seasons.SPRING, SCENE_ID,
+ spring_ids[SCENE_ID][0], bands=S2Bands.RGB)
+ print("Time Taken {}s".format(time.time() - start))
+
+ print("S2 RGB: {} Bounds: {}".format(s2_rgb_patch.shape, bounds))
+
+ print("\n")
+
+ # Load a triplet of patches from the first three scenes of Spring - all S1 bands, NDVI S2 bands, and NDVI S2 cloudy bands
+ i = 0
+ start = time.time()
+ for scene_id, patch_ids in spring_ids.items():
+ if i >= 3:
+ break
+
+ s1, s2, s2cloudy, bounds = sen12mscr.get_s1s2s2cloudy_triplet(Seasons.SPRING, scene_id, patch_ids[0], s1_bands=S1Bands.ALL,
+ s2_bands=[S2Bands.red, S2Bands.nir1], s2cloudy_bands=[S2Bands.red, S2Bands.nir1])
+ print(
+ f"Scene: {scene_id}, S1: {s1.shape}, S2: {s2.shape}, cloudy S2: {s2cloudy.shape}, Bounds: {bounds}")
+ i += 1
+
+ print("Time Taken {}s".format(time.time() - start))
+ print("\n")
+
+ start = time.time()
+ # Load all bands of all patches in a specified scene (scene 106)
+ s1, s2, s2cloudy, _ = sen12mscr.get_triplets(Seasons.SPRING, 106, s1_bands=S1Bands.ALL,
+ s2_bands=S2Bands.ALL, s2cloudy_bands=S2Bands.ALL)
+
+ print(f"Scene: 106, S1: {s1.shape}, S2: {s2.shape}, cloudy S2: {s2cloudy.shape}")
+ print("Time Taken {}s".format(time.time() - start))