I was a volunteer for my city's water quality management program in high school. Testing local water bodies was difficult and expensive, so I wanted to develop a powerful and cost-effective alternative.
What it does
The graphical user interface takes in downloaded Landsat 8 imagery and allows the user to extract important information with a multi-step sequential interface.
How we built it
We used python and powerful libraries like numpy and opencv to process imagery and perform the necessary computations and transformations on the raw satellite images.
Challenges we ran into
Heavy logical errors because we were integrating so many different libraries in a relatively short period of time.
Accomplishments that we're proud of
Making a viable research tool for scientists to scan the Earth's surface from data going all the way back from the 1970's.
What we learned
Tkinter python library and numpy.
What's next for LandPy
More advanced analysis features such as advanced regressions to plot the future trend of water/land features in the future.