Every year there are thousands of aviation accidents along with hundreds of human deaths that happen around the world. While the data is sparse, it is well documented that many of these happen from emergency landings gone awry. While pilots can generally make great landings in clear daytime conditions, they are significantly handicapped when it comes to landing at night or amongst poor visibility conditions.
Due to the nature of this problem and some of the large scale advances in software technology we propose a solution that provides a significant improvement from the status quo. Using transfer learning on neural networks to classify satellite images along with terrain elevation data from the USGS we are able to recreate maps that can readily direct pilots to locations that are relatively flat and lack structures or vegetation. Using San Luis Obispo as our data set we confirmed that we could correctly classify at least 93\% of landable terrain and then identified areas within that area that could safely be used to land a plane. We then transfer this data into a 3D rendering program that allows us to visualize what is happening. In additional to visualizing where the landing paths are we also create a landing algorithm that demonstrates how the plane will maintain its current glide path and navigate to a successful landing while avoiding obstacles.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3767 |
| Date | 01 December 2020 |
| Creators | Alarid, Joseph |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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