Extracting Polygons of Visible Cadastral Boundaries Using Deep Learning

Formal land registration systems are out of reach for most of the world’s population. Conventional mapping methods, such as high-precision ground surveys, are costly, making them inaccessible, especially in low- and middle-income countries. With the introduction of fit-for-purpose land administration, automatic feature extraction techniques have been actively investigated to accelerate the land rights mapping process. Therefore, in our research, we assessed the potential of deep learning to extract cadastral boundaries from very high-resolution images. Our study adopts a multitask learning strategy, which utilizes state of the art U-Net model for the segmentation task, whereas the frame field learning method provides structural information for the subsequent active contour model to produce regularized vector polygons. The experimental results show that the combined U-Net model and frame field information produced polygons with higher accuracy compared to a segmentation method on its own.