The data have been split into a test and training set. The training set contains 2497 agricultural fields and the test set contains 1074 fields. The crops growing on each field was verified in person and with drones in 2017.
We have provided the satellite images of the entire region across 11 time slices in one year, covering summer and winter months. You will use this satellite imagery to train a model to estimate the probability that each field falls into each crop type. There are 2 tiles with the satelite data. Files with the name JFP contain 10% of the fields and the other files contain 90% of the fields.
Use only the data provided here to train your model. Do NOT use Field_ID as a feature in your model.
There are 7 crop types present in these fields, plus vacant fields, and fields that have both vineyards and pecans intercropped in one field (this is its own classification). The crop IDs are as follows:
9 Vineyard & Pecan ("Intercrop")
Your task is to provide the probability that each field belongs to each of the above listed classes. For each unique field ID you should provide 9 probabilities with value between 0 and 1.
Your submission file should look like:
Field_ID Crop_ID_1 Crop_ID_2 Crop_ID_3 Crop_ID_4 ..... Crop_ID_9 <string> <number> <number> <number> <number> <number> 5 .034 .215 .567 .975 .123
The files you have for download here are:
If you need cloud computing support to process this data, please complete this form to get $500 USD in Azure credit for this competition. If you have questions about transfering the data to your workspace or setting up your workspace, let us know. We will also try to post some tips and guidance.
Have a look at these blog posts by Johnowhitaker.