Radiant Earth Spot the Crop XL Challenge

Based on time-series of Sentinel-1 and Sentinel-2 satellite images can you classify crop types in South Africa?
The dataset for this competition contains a time-series of satellite imagery and labels for crop type that have been collected through aerial and ground survey. Labels are derived from the survey conducted by the Western Cape Department of Agriculture. Satellite data including multispectral Sentinel-2 and synthetic aperture radar (SAR) Sentinel-1 are then matched with corresponding labels.
In this competition you are asked to use BOTH Sentinel-2 and Sentinel-1 time-series data. You cannot use only one of the input satellite data, and your model needs to input both of the time-series. The time-series is provided every 5 days for Sentinel-2 and every 12 days for Sentinel-1, but you do not need to use the observations from all days. You are allowed to select specific dates, or apply any pre-processing and feature extraction to the time-series data before input to your model. Note that you would need to provide your full feature-extraction and training scripts if you win in the competition.
Data for this competition is hosted on Radiant MLHub - the open-access repository for geospatial training data. You can access the data by creating a free account on Radiant MLHub. Go to Radiant MLHub Dashboard and use the Sign Up option if you don’t have an account.
You can download the data using Radiant MLHub Python Client (see the example notebooks) or simply by going to the Radiant MLHub Registry page. These links will be available once the competition starts.
You can use the following starter notebooks to learn more about the data and how to access them:
Radiant Earth Spot the Crop Challenge Tutorials
Variable definitions
The label chips contain the mapping of pixel to crop type label. The following pixel values correspond to the following crop types.
0 - No Data
1 - Lucerne/Medics
2 - Planted pastures (perennial)
3 - Fallow
4 - Wine grapes
5 - Weeds
6 - Small grain grazing
7 - Wheat
8 - Canola
9 - Rooibos
Each label chip also contains a mapping of pixel to field ID. The value of the pixel corresponds to the field ID which the pixel belongs to. These field IDs are the same as the field IDs present in the field_info_(test/train) csv files.
Files available for download:
ref_south_africa_crops_competition_v1_train_labels.tar.gz
common
documentation.pdf
field_info_train.csv
Contains the target. This is the dataset that you will use to train your model.
labels.json
Contains the mapping of raster value to crop type
ref_south_africa_crops_competition_v1_train_labels{CHIP_ID}
field_ids.tif
labels.tif
stac.json
ref_south_africa_crops_competition_v1_train_source_s1.tar.gz
ref_south_africa_crops_competition_v1_train_source_s1_{CHIP_ID}{DATE}
VV.tif
VH.tif
stac.json
ref_south_africa_crops_competition_v1_train_source_s2.tar.gz
ref_south_africa_crops_competition_v1_train_source_s2{CHIP_ID}{DATE}
B01.tif
B02.tif
B03.tif
B04.tif
B05.tif
B06.tif
B07.tif
B08.tif
B8A.tif
B09.tif
B11.tif
B12.tif
CLM.tif - cloud mask
stac.json
ref_south_africa_crops_competition_v1_test_labels.tar.gz
_common
documentation.pdf
sample_submission.csv
Shows the submission format for this competition, with the ‘Field ID’ column mirroring that of field_info_test.csv and the ‘Crop’ column containing your predictions. The order of the rows does not matter, but the names of the Field ID must be correct.
field_info_test.csv
Resembles field_info_train.csv but without the target-related columns. This is the dataset on which you will apply your model to.
labels.json
Contains the mapping of raster value to crop type
ref_south_africa_crops_competition_v1_test_labels{CHIP_ID}
field_ids.tif
stac.json
ref_south_africa_crops_competition_v1_test_source_s1.tar.gz
ref_south_africa_crops_competition_v1_test_source_s1_{CHIP_ID}{DATE}
VV.tif
VH.tif
stac.json
ref_south_africa_crops_competition_v1_test_source_s2.tar.gz
ref_south_africa_crops_competition_v1_test_source_s2{CHIP_ID}_{DATE}
B01.tif
B02.tif
B03.tif
B04.tif
B05.tif
B06.tif
B07.tif
B08.tif
B8A.tif
B09.tif
B11.tif
B12.tif
CLM.tif - cloud mask
stac.json