Automation of Spatial Framework Creation for Cadastral Mapping Using Drones and Deep Learning

Automatic detection of  land parcel visible boundaries and creation of basic spatial data necessary for cadastral mapping using very high resolution images obtained by drones and deep learning methods is an important task in terms of improving land administration systems, streamlining the process and reducing the costs required for their production. The data obtained through these methods can be successfully used as a spatial framework for Fit-For-Purpose land administration. In this article, the U-Net deep learning model was used to delineate the visible boundaries of land parcels from orthomosaics generated using aerial photographs taken by a drone over four villages in Tsageri municipality. The raster layer depicting the boundaries was processed using various algorithms, resulting in vector data that corresponds to parcel boundaries. This data can be used for the preparation new cadastral maps or for updating existing ones.