Project Description
This project aims to identify the projection and location of a map input as an image. The algorithm processed input image using a series of image processing functions, then is converted into a vector using a neural network. Then, another network graph processes the vector to associate with one of the six inhabited continents.
Once the represented continent is identified, the vector-converted map follows a similar process to identify its projection. Variety of detectable projections vary by continent; as one may have guessed, North America supplies the most, and South America and Africa the least.
**Summary of Results and Conclusions**
Simply picking one out of six, the continent classifier boasts its accuracy at ~90%., successfully identifying hand-drawn, upside down maps, and sometimes making manlike mistakes:
Future Directions
SImilar procedures can be taken to further better the algorithm; for each continent, a classifier that identifies which part of the continent can be written, and so on. Additional projections can be added to be identified given that enough training set is provided.