Many technologies and applications now necessitate an awareness of their geographical surroundings, typically employing an array of sensors to capture the environment. A key application is telecommunication network planning which benefits from the utilisation of RF propagation tools which incorporate representations of target environments typically sourced from high resolution aerial photography and/or LIDAR point clouds. However, the amount of data associated with LIDAR scanning can be very large, permutation invariant and clustered. Manually classifying this data, to maximise its utility in a propagation model, is not easily scaleable; being both labour intensive and time consuming. This paper describes a system which facilitates the automatic classification of point cloud data and its subsequent translation as wireframe meshes into a propagation model. Testing of automatically classified versus hand-labelled clutter results in comparable performance, with the average difference across all measurements of the automated approach outperforming hand-labelled data by circa 2.5 dB.
|Publication status||Published - 13 Oct 2021|
|Event||2021 4th International Conference on Artificial Intelligence for Industries (AI4I) - |
Duration: 20 Sep 2021 → 22 Sep 2021
|Conference||2021 4th International Conference on Artificial Intelligence for Industries (AI4I)|
|Period||20/09/21 → 22/09/21|
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Automated clutter classification of LIDAR data for Radio Frequency propagation modellingAuthor: Worsey, J. N., 2 Dec 2021
Supervisor: Bull, D. (Supervisor) & Armour, S. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)File