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competitions_details.md

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Competition details

For this challenge, you will have to predict the class of the deforested area, that is between:

  • Number 0: 'Plantation'
  • Number 1: 'Grassland/Shrubland'
  • Number 2: 'Smallholder Agriculture'

Dataset

The dataset will consist of the following variables of interest. They are both in the training.csv and testing.csv and can be divided as:

  • Features:

    • latitude: Where the photo latitude was taken.
    • latitude: Where the photo longitude was taken.
    • year: Year, in which the photo was taken.
    • example_path: Path where the sample image is located.

  • Labels:

    • label: In this column you will have the following categories:
      • 'Plantation':Encoded with number 0, Network of rectangular plantation blocks, connected by a well-defined road grid. In hilly areas the layout of the plantation may follow topographic features. In this group you can find: Oil Palm Plantation, Timber Plantation and Other large-scale plantations.
      • 'Grassland/Shrubland': Encoded with number 1, Large homogeneous areas with few or sparse shrubs or trees, and which are generally persistent. Distinguished by the absence of signs of agriculture, such as clearly defined field boundaries.
      • 'Smallholder Agriculture': Encoded with number 2, Small scale area, in which you can find deforestation covered by agriculture, mixed plantation or oil palm plantation.

Evaluation

The evaluation will be taken into consideration the following:

  • 100/1200:(DOC) Brief presentation explaining what you have done and how you have done it.
  • 700/1200:(OBJECTIVES) This will be obtained from the f1-score(macro) of the predictive model. Comparing the predictions your model has made about versus ground truth.
  • 400/1200:(QUALITY) Code quality and automation, complexity, maintainability, reliability, and security.

Delivery requirements:

Once completed, we have to submit the repository project, which must contain at least these 4 files:

  • main.py or main.ipynb: main script of the program
  • predictions.json: predictions in .json format (with target as the only field which will contain per each test element, index as key, its corresponding target as value )
  • presentation.pdf: 4 slides max. explaining the solution