The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B1-2020
06 Aug 2020
 | 06 Aug 2020


B. Fetai, M. Račič, J. Tekavec, and A. Lisec

Keywords: U-Net, visible boundary, detection, UAV, cadastral mapping

Abstract. Peoples land rights are secure if they are registered in a formal cadastral system. More than 70% of global land rights are not registered in any formal cadastral system. The contemporary efforts are on accelerating the cadastral mapping process as a basis of defining land rights boundaries. Proposed surveying techniques are indirect ones – delineation of visible parcel boundaries from remote sensing imagery. This research aims at automizing the procedure of visible boundary delineation from Unmanned Aerial Vehicle (UAV) imagery through deep learning. U-Net architecture was selected to train the model and predict visible boundaries. The model was trained on an available edge detection dataset, which was the closest to our domain problem. The model was tested on a tiled UAV images. The U-Net architecture was implemented in Keras and written in Python, running on top of the TensorFlow library. The training was done through Google Colaboratory. The evaluation metrics of the trained model indicated 0.95 overall accuracy. The average percentage of correctly detected visible boundaries was almost 80% for the tiled UAV images. This percentage is very satisfying since the model was trained on everyday imagery which is very different from UAV ones. The automatic boundary detection by using U-Net is applicable mostly for rural areas where the visibility of the boundaries is continuous. In cases where the boundaries are not visible, manual delineations are still required.