AIcrowd | CRDSM | Challenges

🕵️ 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.