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TODO.md

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Milestones

Prior to Winter Quarter (Dec. 9 - Jan. 1)

  • Find resources on random forests
  • Dive into data

Week 1 (Jan. 4-8)

  • Set up weekly meetings
  • Create weekly work schedule (i.e. timing of work sessions)
  • Address expected data preprocessing challenges
    • Hong Kong. Pick a city to start with.
    • *Using contest data * Get Landsat images from USGS Earth Explorer for that city on it's two dates
    • Using contest data. Clip image to cover city
    • Figure out what ENVI FLAASH is Using contest data
    • Only for curiosity sake, using contest data. Read about bilinear reampling with spatial data
  • Solidify plan of attack for data preprocessing.

Week 2 (Jan. 11-15)

  • Schedule Oral Exam
  • Migrated to week 3. Start writing background info/introduction
  • Go through 2+ random forest resources
  • Data preprocessing steps
    • Determine how you get raster data into R.
    • How can you get the raster data to be a data frame (& does it even make sense to do that?). It's seriously just as.data.frame(dat, xy=TRUE)
    • Determine how you choose to only use certain bands as input data? Can you just filter it? Yes
    • How do I know that the columns of my dataframe are actually what I'm looking for? Where are the bands? See next primary goal below (from meeting notes issue) for strategy
    • Migrated to week 4. How did they randomly and evenly divide the polygons? Test if time allows
  • Attempt to get from Tiffs to a data frame appropriate for feeding into random forest function.

Week 3 (Jan. 18-22)

  • Start references
  • Finish data preprocessing
    • Combine bands and get rid of ones not used in paper
    • Migrated to week 4. Figure out how to/how they randomly and evenly divide the polygons.
  • Go through 2+ random forest resources
  • What are the things/arguments that I control when I run that randomForest function?
  • Start writing background info/introduction

Week 4 (Jan 25-29)

  • Fit Different Classification Schemes
  • How did they randomly and evenly divide the polygons? Test.
  • Go through 2+ random forest resources
  • Start writing methods

Week 5 (Feb. 1-5)

  • Randomly and Evenly Divide the Polygons.
  • Fit Different Classification Schemes
  • At least set up accuracy metrics (add calculation formats into R somewhere)
  • Organize repo, especially docs section
  • Cite some of claims in introduction

Week 6 (Feb. 8-12)

  • Finish Methods Section
    • Description of data (actual volume of data involved + how this data relates to the model)
    • Explanation of how a typical decision tree is built
    • Explain Gini Impurity & Information Gain. What "best" means
  • Start preparing results figures
    • Table/Plot that shows the OA over the tuning parameters tried
    • Variable importance plot
    • One complete map of predictions
  • Which models look the best? Why?
  • Create F1 metric function

Week 7 (Feb. 15-19)

  • Write Results
  • Write conclusion
  • Final Evaluation of where things are at and setting what "finished" means by Thursday Meeting (18)

Week 8 (Feb. 22-26)

  • Finish writing by Thursday Meeting (25th)
  • Finish polishing figures

Week 9 (Mar. 1-5)

  • Prepare Presentation
  • Submit paper March 2

Week 10 (Mar. 8-12)

  • Oral Exam

Finals Week (Mar. 15-19)

  • All degree requirements submitted