{'fraction_non_alphanumeric': 0.022163120567375887, 'fraction_numerical': 0.005319148936170213, 'mean_word_length': 5.563953488372093, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Extracting Cadastral Boundaries from UAV Images Using Fully Convolutional Networks

The cadastre is the foundation of land management. However, it is estimated that 70% of the land rights in the world remain unregistered. Traditional approaches are costly and labor intensive, therefore, recently the use of remotely sensed images has been investigated. The delineation of cadastral boundaries from such data is challenging since not all boundaries are demarcated by visible physical objects. In this paper, we introduce a technique based on deep Fully Convolutional Networks (FCNs), which can automatically learn high-level spatial features from images, to extract cadastral boundaries. Our strategy combines FCN and a grouping algorithm using the Oriented Watershed Transform (OWT) to generate connected contours. We carried out an experimental analysis in a real case study in Busogo, Rwanda, using images acquired by Unmanned Aerial Vehicles (UAV) in 2018. Our investigation shows promising results in automatically extracting visible boundaries, which can contribute to the current mapping and updating practices in Rwanda.