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## 🕵️ Introduction

With tons of satellite images from Open Street Map and only a pc to work with , your mission, should you choose to accept is to identify whether a piece of land is a desert, forest or a water body.
Time to train a classifier with the features extracted and presented in a nice tabular form.

Understand with code! Here is getting started code for you.😄

## 💾 Dataset

• This dataset is crowdsourced from Open Street Map.
• This dataset is derived from geospatial data from two sources:
• Time-series of images captured by landsat satellites from the year 2014 to 2015.
• crowdsourced georeferenced polygons with land cover labels obtained from Open Street Map.
• The crowdsourced polygons cover only a small part of the image area.
• The main challenge with the dataset is that both the imagery and the crowdsourced data contain noise , this is due to cloud cover in the images and inaccurate labeling/digitizing of polygons.
• Each row has 29 attributes.
• 27 attributes describe the time series of NDVI values extracted from the satellite images acquired between January 2014 and July 2015 in reverse chronological order.
• Dates are given in the format yyyymmdd.
• Out of the 2 remaining attributes, one attribute denotes the Maximum NDVI (normalized difference vegetation index) value of the corresponding 27 given attributes.
• The last attribute gives the class of the land cover in the image. It may be of the following six types: ( forest-0,farm-1,impervious-2, grass-3, water-4,orchard-5)

## Files

Following files can be found in resources section:

• train.csv - (10545 samples)This csv file contains the attributes describing the land cover along with the class the land cover belongs to .
• test.csv - (300 samples)File that will be used for actual evaluation for the leaderboard score.

## 🚀 Submission

• Prepare a CSV containing header as class and predicted value as class 0(forest)/1(farm)/2(impervious)/3(grass)/4(water)/5(orchard) denoting the land cover.

 class label 0 forest 1 farm 2 impervious 3 grass 4 water

• Name of the above file should be submission.csv.
• Sample submission format available at sample_submission.csv in the resorces section.

Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$