Application of natural language parsing for identifying non-surveyed boundaries towards enhanced systematic land titling: results from preliminary experiment

The need for the adoption of systematic land titling (SLT) in Nigeria cannot be overemphasised. Nonetheless, the problems of speed and cost of geospatial data acquisition, as well as identification of non-surveyed boundaries, remain unresolved, impeding the effectiveness of SLT for non-surveyed boundaries. The integration of language into Artificial Intelligence (AI) has allowed Natural Language Parsing (NLP) to effectively serve as a tool for communication between humans and computer systems. This study presents preliminary results of testing a prototype application that utilises NLP to convert textual descriptions into graphic sketches as a tool towards the production of a-priori sketches that can aid SLT in non-surveyed boundaries. The study determines that NLP alone cannot be used to achieve the required accuracy in geospatial data for SLT; however, the study concludes that NLP can be integrated alongside other ancillary information to enhance SLT in peri-urban regions

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02–0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation