🛠 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.
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.😄
- 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
27attributes describe the time series of NDVI values extracted from the satellite images acquired between
January 2014 and July 2015in reverse chronological order.
- Dates are given in the format
- 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
classof 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
10545samples)This csv file contains the attributes describing the land cover along with the class the land cover belongs to .
300samples)File that will be used for actual evaluation for the leaderboard score.
- Prepare a CSV containing header as
classand predicted value as class
5(orchard) denoting the land cover.
- Name of the above file should be
- Sample submission format available at
sample_submission.csvin 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,
- 💪 Challenge Page :https://www.aicrowd.com/challenges/crdsm
- 🗣️ Discussion Forum : https://www.aicrowd.com/challenges/crdsm/discussion
- 🏆 Leaderboard : https://www.aicrowd.com/challenges/crdsm/leaderboards
- 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.