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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

Abstract

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. https://doi.org/10.3846/gac.2023.18111
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Dec 20, 2023
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Atilola, O. (2013, May 6–10). Systematic land titling in Nigeria: Geoinformation challenges. In FIG Working Week 2013 (pp. 1–13), Abuja, Nigeria. https://doi.org/10.1177/1365480212474731

Banire, M. A. (2006). Land management in Nigeria: Towards a new legal framework. Ecowatch Publication Lagos.

Crommelinck, S., Bennett, R., Gerke, M., Nex, F., Yang, M. Y., & Vosselman, G. (2016). Review of automatic feature extraction from high-resolution optical sensor data for UAV-based cadastral mapping. Remote Sensing, 8(8), 689. https://doi.org/10.3390/rs8080689

Crommelinck, S., Koeva, M., Yang, M. Y., & Vosselman, G. (2019a). Robust object extraction from remote sensing data. arXiv:1904.12586. https://arxiv.org/abs/1904.12586

Crommelinck, S., Koeva, M., Yang, M. Y., & Vosselman, G. (2019b). Application of deep learning for delineation of visible cadastral boundaries from remote sensing imagery. Remote Sensing 11(21), 2505. https://doi.org/10.3390/rs11212505

García-Pedrero, A., Gonzalo-Martín, C., & Lillo-Saavedra, M. (2017). A machine learning approach for agricultural parcel delineation through agglomerative segmentation. International Journal of Remote Sensing, 38(7), 1809–1819. https://doi.org/10.1080/01431161.2016.1278312

Jazayeri, I., Rajabifard, A., & Kalantari, M. (2014). Geometric and semantic evaluation of 3D data sourcing methods for land and property information. Land Use Policy, 36, 219–230. https://doi.org/10.1016/j.landusepol.2013.08.004

Kibble, R. (2013). Introduction to Natural Language processing: Undergraduate study in computing and related programmes. University of London, Department of Computing, Goldsmiths.

Manyoky, M., Theiler, P., Steudler, D., & Eisenbeiss, H. (2012). Unmanned aerial vehicles in cadastral applications. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38, 57–62. https://doi.org/10.5194/isprsarchives-XXXVIII-1-C22-57-2011

Oluwadare, C. O., & Kufoniyi, O. (2017, March). Comparative study of sporadic and systematic methods of land titling and registration in Ondo State, Nigeria. In Environmental Design and Management International Conference (EDMIC): Responsive Built Environment (pp. 9–13). The Faculty of Environmental Design and Management, Obafemi Awolowo University, Ile-Ife.

Otubu, A. (2018). The land use act and land administration in 21st century Nigeria: Need for reforms. Journal of Sustainable Development, Law and Policy, 9(1), 80–108. https://doi.org/10.4314/jsdlp.v9i1.5

Owusu, C., Lan, Y., Zheng, M., Tang, W., & Delmelle, E. (2018). Geocoding fundamentals and associated challenges. In H. A. Karimi & B. Karimi. (Eds.), Geospatial data science techniques and applications. CRC Press. https://doi.org/10.1201/b22052

Persello, C., Tolpekin, V. A., Bergado, J. R., & de By, R. A. (2019). Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping. Remote Sensing of Environment, 231, 111253. https://doi.org/10.1016/j.rse.2019.111253

Pont-Tuset, J., Arbeláez, P., Barron, J. T., Marques, F., & Malik, J. (2017). Multiscale combinatorial grouping for image segmentation and object proposal generation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(1), 128–140. https://doi.org/10.1109/TPAMI.2016.2537320

Presidential Technical Committee on Land Reform. (2013). Draft regulations on land use act, 2013. Abuja, FCT Nigeria.

Sohail, S. (2020). Geospatial natural language processing. https://medium.com/geoai/geospatial-natural-language-processing-ee3fc6ea6939