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Building segmentation using Convolutional Neural Networks for fused airborne lidar and image data
We develop a novel pipeline which uses Active Contour models and fused image-lidar data to achieve state-of-the-art accuracy in aerial building segmentation using CNNs.
6 June 2019
Manually labelling buildings for segmentation is a time-consuming task. We show that readily available GIS mapping data such as that from the Ordnance Survey (UK) can be used as training data. Further, we develop a novel pipeline which uses Active Contour models and fused image-lidar data to achieve state-of-the-art accuracy in aerial building segmentation.
People
- David Griffiths
- Prof. Jan Boehm