πŸ›  Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

πŸ•΅οΈ 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.πŸ˜„

πŸ’Ύ 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)


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.

πŸš€ 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.
  • 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 πŸš€ !!

πŸ–Š Evaluation Criteria

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

πŸ”— Links

πŸ“± Contact

πŸ“š References

  • Johnson, B. A., & Iizuka, K. (2016). Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines. Applied Geography, 67, 140-149.
  • https://latex.codecogs.com/
  • https://www.maketecheasier.com/what-is-openstreetmap/


Getting Started


01 john_f_wu 0.657
02 oumardiallo 0.653
03 adithyasunil26 0.637
04 jyotish 0.563
05 sainath_prasanna 0.517

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