Tree and building detection in dense urban environments using automated processing of IKONOS image and LiDAR data

Abstract

The automated detection and reconstruction of artificial structures, larger than 10 m² in area using high resolution satellite images and Light Detection and Ranging (LiDAR) data through 3-dimensional shapes and/or 2-dimensional boundaries is described here. Additionally, it is demonstrated how individual tree crowns have been detected with more than 90% accuracy in very dense urban environments from very high-resolution images and range data. Pre-existing machine vision algorithms and techniques were modified and updated for this particular application to building detection within dense urban areas. All products from such procedures have not only been demonstrated with a significant areal coverage but have also been quantitatively assessed against manually obtained and third party mapping data. Accuracies of around 85% have been achieved for building detection and almost 95% for tree crown detection.

Acknowledgements

The authors would like to thank BNSC and Infoterra Ltd for supporting this research under the LINK RISKMAP programme as well as providing the Lidar dataset. We also would like to thank Nicke Coote of the Ordnance Survey for kindly providing the OS^® MasterMap^® data. All OS^® products shown here are Crown Copyright.