Explore raw data

[barisozmen]

Do an explorative analysis on jupyter notebook and put it to /notebooks/explore-raw-data

Notebook should iterate followings:

1. Explore raw training data from DOTA, and report followings:

   • Distribution of ground sample distances (gsd) of images
   • Distribution of image sources (GoogleEarth or others)
   • Frequency of each object type across all images
   • Number of objects per image
   • Co-occurrences and mutual exclusivity of object types across images
   • Are images augmented?

2. Show some image samples

3. Write an overall summary of explorative analysis. And add necessary information from DOTA paper in it.

[barisozmen]

Raw data exploration completed, and can be found in this notebook.

Main comments:

• Label classes are highly imbalanced: Number of objects by types range from 325 (ground-track-field) to 28,068 (ship)
• Co-occurences of object types are not surprising. High correlations are between ground-track-field/soccer-ball-field and helicopter/plane, because these tuples are tend to be in the same images. Again uninterestingly, planes and tennis-courts are negatively correlated, since it is unlikely to have a tennis court near an airport.