Datasets:

torchgeo
/

CropClimateX

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 299, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 353, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 304, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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CropClimateX

Repository: TBA
Paper: TBA

The database includes 15,500 small data cubes (i.e., minicubes), each with a spatial coverage of 12x12km, spanning 1527 counties in the US. The minicubes comprise data from multiple sensors (Sentinel-2, Landsat-8, MODIS), weather and extreme events (Daymet, heat/cold waves, and U.S. drought monitor maps), as well as soil and terrain features, making it suitable for various agricultural monitoring tasks. It integrates crop- and climate-related tasks within a single, cohesive dataset. In detail, the following data sources are provided:

Uses

The dataset allows various tasks, including yield prediction, phenology mapping, crop condition forecasting, extreme weather event detection/prediction, sensor fusion, pretraining on crop areas, and multi-task learning.

Dataset Structure

The data is stored in ZARR format in 12x12km minicubes, allowing compression, grouping, and automatically applying offsets and scaling when loading with Xarray. Xarray is recommended for reading the files to utilize the full functionality. Each modality is located in a different folder. The folder contains the grouped minicubes of each county. The following counties are covered by the dataset (a) all data except Sentinel-2 (b) only Sentinel-2:

Dataset Creation

The minicube locations were optimized using a genetic and a sliding grid algorithm (details are provided in the paper). The following data providers were used to gather the data: Planetary Computer, Google Earth Engine, SentinelHub, and Copernicus Data Space Ecosystem. The APIs were accessed with terragon.

Citation

If you use this dataset, please consider citing: TBA

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