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import numpy as np
import os
import pandas as pd
from pyproj import Transformer
import rasterio
from rasterio.transform import rowcol
import torch
from torch.utils.data import Dataset
from shapely.geometry import Polygon
from shapely import wkt
import xarray as xr
S2L1C_MEAN = [2607.345, 2393.068, 2320.225, 2373.963, 2562.536, 3110.071, 3392.832, 3321.154, 3583.77, 1838.712, 1021.753, 3205.112, 2545.798]
S2L1C_STD = [786.523, 849.702, 875.318, 1143.578, 1126.248, 1161.98, 1273.505, 1246.79, 1342.755, 576.795, 45.626, 1340.347, 1145.036]
S2L2A_MEAN = [1793.243, 1924.863, 2184.553, 2340.936, 2671.402, 3240.082, 3468.412, 3563.244, 3627.704, 3711.071, 3416.714, 2849.625]
S2L2A_STD = [1160.144, 1201.092, 1219.943, 1397.225, 1400.035, 1373.136, 1429.17, 1485.025, 1447.836, 1652.703, 1471.002, 1365.307]
S1GRD_MEAN = [-12.577, -20.265]
S1GRD_STD = [5.179, 5.872]
class ShipDataloader(Dataset):
def __init__(self,
data_path: str,
metadata_path: str = None,
transform = None,
concat: bool = True,
output_file_name: bool = False,
output_metadata: bool = False,
load_s2: bool = False,
shift_s2_channels: bool = True,
):
"""Dataset class for the SSL4EO downstream dataset.
Parameters
----------
data_path : str, path-like
Path to challenge data. Assumes that under data_path there is at least one folder with the name `s1`, optionally also `s2l1c` and `s2l2a` folders.
transform : torch.Compose
Transformations to apply to the data
concat : bool
Toggle concatenating the modalities along the channel dimension. Default is True.
output_file_name : bool
Toggle output of the file name.
output_metadata : bool
Toggle outputting metadata with bounding box, transform, ship location in pixel.
load_s2 : bool
Toggle loading of optional Sentinel-2 data.
shift_s2_channels : bool
Toggle shifting the S2 channels by 1000 to align to SSL4EO-S12 v1.1. Default is True, where the challenge data S2 channels are shifted upward 1000 to have the same range as SSL4EO-S12 v1.1. The background is that ESA decided. This argument is only used if load_s2=True.
from 2022-01-25 to shift the DN values of S2 by 1000 upward. SSL4EO-S12 v1.1 includes this shift, while the provided S2 data does not.
Returns
-------
dict
By default only includes field 'data', with either a torch.Tensor (if concat=True), or a dict with keys the modalities and torch.Tensors as values (if concat=False).
If output_file_name=True, dictionary includes field 'file_name' with a list of file names corrsponding to the loaded files.
If output_metadata=True, dictionary includes field 'metadata' with a disctionary contatining metadata for the samples.
"""
if output_metadata:
assert metadata_path is not None, f"metadata_path must be provided to output metadata."
self.data_path = data_path
self.metadata_path = metadata_path
self.transform = transform
self.dataset_name = 'bands'
self.concat = concat
self.output_file_name = output_file_name
self.output_metadata = output_metadata
self.load_s2 = load_s2
self.shift_s2_channels = shift_s2_channels
# Get S1 samples
self.samples = glob.glob(os.path.join(data_path, 's1', '*.tif'))
self.file_names = [os.path.basename(p).replace('.tif', '') for p in self.samples]
# Get optional metadata
if metadata_path is not None:
df = pd.read_csv(metadata_path)
# Filter samples and metadata to contain the same samples
df = df[df['file_name'].isin(self.file_names)]
meta_file_names = df['file_name'].unique().tolist()
self.samples = [s for s in self.samples if os.path.basename(s).replace('.tif', '') in meta_file_names]
# Update file name list
self.file_names = [os.path.basename(p).replace('.tif', '') for p in self.samples]
# Store metadata
self.metadata = df.set_index('file_name').to_dict(orient='index')
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
sample_path = self.samples[idx]
file_name = os.path.splitext(os.path.basename(sample_path))[0].replace('.tif', '')
data = {}
# Load S1 data
with rasterio.open(sample_path) as f:
data['s1'] = f.read()[None, None, ...]
raster_crs = f.crs
raster_transform = f.transform
# Optionally load s2 data
if self.load_s2:
# Load S2 data
s2_sample_paths = {m: os.path.join(self.data_path, m, file_name + '.zarr.zip') for m in ['s2l1c', 's2l2a']}
for modality, s2_sample_path in s2_sample_paths.items():
data[modality] = xr.open_zarr(s2_sample_path)[self.dataset_name].values
# Copy S1 data as many times as the season dimension of S2, dimension -4
data['s1'] = np.repeat(data['s1'], data['s2l1c'].shape[-4], axis=-4)
# Add shift to align S2 channels with SSL4EO-S12 v1.1
if self.shift_s2_channels and (modality in ['s2l1c', 's2l2a']):
data[modality] += 1000
n_bands_per_modality = {m: d.shape[-3] for m, d in data.items()}
start_ind_of_modality = {m: n for m, n in zip(data.keys(), [0] + np.cumsum(list(n_bands_per_modality.values())).tolist())}
modalities = list(data.keys())
# Concatenate data
data = np.concatenate(list(data.values()), axis=-3)
data = data.astype(np.float32)
data = torch.from_numpy(data)
# Transform
if self.transform is not None:
data = self.transform(data)
if not self.concat:
data = {m: data[..., start_ind_of_modality[m]: start_ind_of_modality[m] + n_bands_per_modality[m], :, :] for m in modalities}
output = {}
output['data'] = data
if self.output_file_name:
output['file_name'] = file_name
if self.output_metadata:
# Get metadata
metadata = self.metadata[file_name]
# Convert latitude, longitude WKT polygon bounding box
geometry = metadata['geometry']
pixel_coords = convert_wgs84_to_pixel_polygon(geometry, raster_crs, raster_transform)
# Add metadata
metadata['geometry_pixel'] = pixel_coords_to_wkt(pixel_coords)
metadata['crs'] = raster_crs
metadata['transform'] = raster_transform
output['metadata'] = metadata
return output
def collate_fn(batch):
if isinstance(batch, dict) or isinstance(batch, torch.Tensor):
# Single sample
return batch
elif isinstance(batch, list) and isinstance(batch[0], torch.Tensor):
# Concatenate tensors along sample dim
return torch.concat(batch, dim=0)
elif isinstance(batch, list) and isinstance(batch[0], dict):
data = [sample['data'] for sample in batch]
includes_file_names = False
if 'file_name' in batch[0]:
file_names = [sample['file_name'] for sample in batch]
includes_file_names = True
includes_metadata = False
if 'metadata' in batch[0]:
metadata = [sample['metadata'] for sample in batch]
includes_metadata = True
if isinstance(data[0], torch.Tensor):
data = torch.concat(data, dim=0)
elif isinstance(data[0], dict):
data = {
m: torch.concat([b[m] for b in data], dim=0)
for m in data[0].keys()
}
output = {'data': data}
if includes_file_names:
output['file_name'] = [fn for fn in file_names]
if includes_metadata:
output['metadata'] = [m for m in metadata]
return output
def transform_coords(coord_seq, transformer):
x, y = transformer.transform(coord_seq[0], coord_seq[1])
return (x, y)
def convert_wgs84_to_pixel_polygon(wkt_str, raster_crs, raster_transform):
poly_wgs84 = wkt.loads(wkt_str)
# Set up a transformer: WGS84 (EPSG:4326) → raster CRS
transformer = Transformer.from_crs(
"EPSG:4326",
raster_crs,
always_xy=True # ensure order (lon, lat) → (x, y)
)
exterior = [transform_coords(pt, transformer) for pt in poly_wgs84.exterior.coords]
# Transform interior rings (holes) if any
interiors = [
[_transform_coords(pt, transformer) for pt in ring.coords]
for ring in poly_wgs84.interiors
]
# Re‑build the polygon in raster CRS
geom_raster_crs = Polygon(exterior, interiors)
# To get the pixel coordinates for the whole polygon, transform each vertex.
pixel_coords = [
rowcol(raster_transform, *pt) # returns (row, col)
for pt in geom_raster_crs.exterior.coords
]
return pixel_coords
def pixel_coords_to_wkt(pixel_coords):
# Flip each pair to (col, row) so that x=col, y=row
pts = [(c, r) for r, c in pixel_coords]
# Close the ring if it isn't already
if pts[0] != pts[-1]:
pts.append(pts[0])
# Build the coordinate string
coord_str = ", ".join(f"{x} {y}" for x, y in pts)
# Return the WKT polygon string
return f"POLYGON(({coord_str}))"
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