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

    Joseph O. Odumosu Affiliation
    ; Victor C. Nnam Affiliation
    ; Olurotimi A. Kemiki Affiliation
    ; Abdulkadir Abubarkar Affiliation
    ; Michael A. Oyebanji Affiliation
    ; Sunday O. Babalola Affiliation


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.

Keyword : systematic land titling, non-surveyed boundaries, Natural Language Parsing, artificial intelligence, cadastral mapping

How to Cite
Odumosu, J. O., Nnam, V. C., Kemiki, O. A., Abubarkar, A., Oyebanji, M. A., & Babalola, S. O. (2023). Application of natural language parsing for identifying non-surveyed boundaries towards enhanced systematic land titling: results from preliminary experiment. Geodesy and Cartography, 49(4), 216–221.
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Dec 20, 2023
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