# GLORYS12 eddy detection — Data

来源：https://www.kaggle.com/competitions/glorys-12-eddy-detection/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description

In this section we will explain you the data and how you can use it. 
As described in Overview section, the main goal of the project is to build a model performing segmentation through supervised learning. We will study the Gulf Stream region. 
Data from various sources is available : classified eddies maps from PyEddyTracker tool run on NatRun product (virtual reality ~ real ocean); physical variables from NatRun OSSE 3NADIRS product (distorted reality ~ modeled ocean). Thus, we aim to train a model which detects eddies from the distorted reality, i.e. NatRun OSSE 3NADIRS product (as generally it is the only information about the ocean state available), given the ground-truth provided by a numerical (statistical) tool. 

You will work here with .nc files, this is a usual format for climatology and geophysical data storage. Each .nc file contains dimensions (coordinates, e.g. Latitude, Longitude, Time) and associated variables (velocities, temperature, salinity, sea surface height). A useful tool to work with such file format is xarray library. 

## Files

*   **eddies_train.nc** - the training set of eddies maps for the given region
*   **OSSE_U_V_SLA_SST_train.nc** - the training set of OSSE 3NADIRS physical variables for the given region
*   **OSSE_U_V_SLA_SST_test.nc** - the test set of OSSE 3NADIRS physical variables for the given region

The eddies maps are images of [Time, Latitude, Longitude] dimension. Each map (image) is regularly sampled, one cell corresponds to 1/12° (~100km). They contain the information on the eddy type per individual pixel (cell). We distinguish 3 classes (that should be predicted by your model) : 0 - no eddy detected, 1 - cyclonic eddy, 2 - anti-cyclonic eddy. An additional fourth class is given to take into account the coast (Gulf Stream being closely situated to the coast), we gave it a 999 identifier. Your model is not supposed to predict this class, however it should be able to process the ground correctly. 
Physical variables are also given as images of the same spatio-temporal resolution as eddies maps. Note that in physical variables maps the ground corresponds to NaN values. It is important to be careful with NaN values while training your model, take time to brainstorm with your team mates on how you can work with such values (pre-processing / mask / etc). You will find following variables in your .nc files : vozocrtxT - horizontal velocity component; vomecrtyT - vertical velocity component; sossheig - Sea Surface Height (SSH); votemper - Sea Surface Temperature (SST). You do not need to use all physical variables at a time. Take time to test and conclude on the influence of each variable on your model's performance (both in prediction ability and computational effectiveness). 


## Columns in your submission file
Your submission file should contains two columns : Id and Predicted. The Id column corresponds to each individual pixel of each spatio-temporal map. Each Id is a string: DayNumber_LatitudePixelNumber_LongitudePixelNumber. For this purpose, once eddies types are predicted (attention to the fourth ground's class), you should flatten your final predicted array of size [Time, Latitude, Longitude] to the new size [Time x Latitude x Longitude], fill the 'Predicted' column in the corresponding .csv file and add the corresponding Id column.  Please, refer to our team members for further details if necessary.