Integration of Rural Terrain Data for Optimal Path Finding

The research will look at methods to integrate a range of large-scale terrain data, and combine it to plan the fastest safe path back to a road from any point.

Many different data sources are available, such as terrain height, topography and flood risk, as well as user-generated hiking routes showing known paths. These are available in varying resolutions and accuracies for different regions. As well as this, other less tangible data sources can be considered, such as the ability and condition of the walking group. Initially, the maximal extent of data will be used to produce a terrain analysis which is as accurate as possible, i.e. the nearest thing we can have to a ground truth. Once a terrain map has been created, path/route planning algorithms will be used to find the shortest safe path over very large areas to a non-specific location. Following this, the impact of removing different data layers and the impact of adding noise can then be simulated to determine the most informative features when analysing terrain to create a navigable route. Furthermore, it should be possible for the results to be amended with the addition of live inputs such as weather conditions, or time of day.
Finally, as a practical outcome of the project I aim to use these findings to produce a navigational aide, it would be beneficial if the results could be accessed offline. This will require being able to store and query maps directly on a device, adding data storage and optimisation challenges to the project. Work will be required to decide how much pre-computation of the data can be performed, and for the live updates whether it is possible to run these calculations on the device, or whether all possible combinations of solutions should be stored on the device, and the relevant results displayed.