Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 May 2019 doi:10.20944/preprints201905.0342.v1 Peer-reviewed version available at Remote Sens. 2019, 11, 1662; doi:10.3390/rs11141662 1 Article 2 Comparing Human Versus Machine-Driven 3 Cadastral Boundary Feature Extraction 4 Emmanuel NYANDWI 1, *, Mila KOEVA 2, Divyani Kohli 2 and Rohan Bennett 3 5 1, * Department of Geography and Urban Planning, School of Architecture and Built Environment (SABE), 6 College of Science and Technology (CST), University of Rwanda;
[email protected] 7 2 Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede 7500 AE, 8 The Netherlands;
[email protected] (M.N.K);
[email protected] (D.K) 9 3 Department of Business Technology and Entrepreneurship of Swinburne Business School, Melbourne, 10 Australia, BA1231 Hawthorn campus;
[email protected] 11 Received: date; Accepted: date; Published: date 12 Abstract: The objective to fast-track the mapping and registration of large numbers of unrecorded 13 land rights globally, leads to the experimental application of Artificial Intelligence (AI) in the domain 14 of land administration, and specifically the application of automated visual cognition techniques for 15 cadastral mapping tasks. In this research, we applied and compared the ability of rule-based systems 16 within Object Based Image Analysis (OBIA), as opposed to human analysis, to extract visible 17 cadastral boundaries from Very high resolution (VHR) World View-2 image, in both rural and urban 18 settings. From our experiments, machine-based techniques were able to automatically delineate a 19 good proportion of rural parcels with explicit polygons where the correctness of the automatically 20 extracted boundaries was 47.4% against 74.24% for humans and the completeness of 45% for machine, 21 as against 70.4% for humans.