Milestone #1: Data Investigation¶
My first step is to investigate my data options for this project. As discussed in my plan, I am considering Google Streetview data and LiDAR data. The Streetview data is my first choice but I realized that that data might be different from what I expect or have weird complications that make it difficult or impossible to do what I have in mind. I wanted to consider alternatives, and there's a lot that interests me about LiDAR data. Of course that data might be impossible to work with too. In any case, I needed to find out these things right away while it is still easy to change course on this project.
The summary of Google Streetview data is that it is easy to work with and close to what I expected. They provide a convenient API that is properly documented. Unfortunately, the depth data discussed in this blog post does not come from the API, and that information is compressed in a format I have not yet parsed. The author of that post does provide C++ code for doing so; I am optimistic that I will be able to translate that to Python and/or integrate their process into my code.
LiDAR data is also well documented but extremely complex. I've worked with complex data before and am confident I can manage this if I put in the time. My objection is that taking the project in that direction would take a good portion of the class. I would have less time to learn about the topics I want to be learning about.
Additionally, I feel the challenges I would face with the Google Streetview data is resonating with me in a way that the LiDAR data challenges are not.
My conclusion is that I will use the Google Streetview data for this project. Sometime after the semester is over I might spend more time with the LiDAR data and get some experience working with it. It would be a great choice for a future project.