eurocropsml.dataset.config.EuroCropsDatasetPreprocessConfig¶
- class EuroCropsDatasetPreprocessConfig(*, download_url: str = 'https://zenodo.org/api/records/10629610/versions', raw_data_dir: Path, preprocess_dir: Path, band4_t1: float = 0.07, band4_t2: float = 0.25, band4_prob_threshold: float = 0.5, filter_clouds: bool = True, num_workers: int | None = None, excl_classes: list[int] = [], keep_classes: list[int] = [], satellite: Literal['S1', 'S2'] = 'S2', bands: list[str] | None = None, year: int = 2021)¶
Bases:
BaseModelConfiguration for downloading and preprocessing EuroCrops dataset.
- Parameters:
download_url – Zenodo URL do download dataset from.
raw_data_dir – Directory where the raw EuroCropsML data is stored (from data acquisiton).
preprocess_dir – Directory where the preprocessed data is stored.
band4_t1 – Lower threshold for band 4 for identifying cloudy pixels.
band4_t2 – Upper threshold for band 4 for identifying cloudy pixels.
band4_prob_threshold – Probability threshold for filtering clouds that decides whether an observation is defined cloudy or non-cloudy.
filter_clouds – Whether to filter clouds from Sentinel-2 time series.
num_workers – Number of workers used during multiprocessing.
excl_classes – Classes that should be excluded even before preprocessing.
keep_classes – Classes to keep for preprocessing. This comes in handy if for example only a couple of classes are relevant. In that case, it massively speeds up the pre- processing.
satellite – Preprocess Sentinel-1 or Sentinel-2.
bands – If this is None, the default bands stated in the global variables will be used. These are also the ones available in the ready-to-use EuroCropsML dataset. If during your own data acquisition not all bands or different bands were acquired, please define them here.
year – Year for which data are to be processed.
Methods
Interpret relative paths w.r.t.
Attributes
Configuration for the model, should be a dictionary conforming to
ConfigDict.