Revising cadastral data on land boundaries using deep learning in image-based mapping

Fetai, Bujar (author), Grigillo, Dejan (author), Lisec, Anka (author)

One of the main concerns of land administration in developed countries is to keep the cadastral system up to date. The goal of this research was to develop an approach to detect visible land boundaries and revise existing cadastral data using deep learning. The convolutional neural network (CNN), based on a modified architecture, was trained using the Berkeley segmentation data set 500 (BSDS500) available online. This dataset is known for edge and boundary detection. The model was tested in two rural areas in Slovenia. The results were evaluated using recall, precision, and the F1 score—as a more appropriate method for unbalanced classes. In terms of detection quality, balanced recall and precision resulted in F1 scores of 0.60 and 0.54 for Ponova vas and Odranci, respectively. With lower recall (completeness), the model was able to predict the boundaries with a precision (correctness) of 0.71 and 0.61. When the cadastral data were revised, the low values were interpreted to mean that the lower the recall, the greater the need to update the existing cadastral data. In the case of Ponova vas, the recall value was less than 0.1, which means that the boundaries did not overlap. In Odranci, 21% of the predicted and cadastral boundaries overlapped. Since the direction of the lines was not a problem, the low recall value (0.21) was mainly due to overly fragmented plots. Overall, the automatic methods are faster (once the model is trained) but less accurate than the manual methods. For a rapid revision of existing cadastral boundaries, an automatic approach is certainly desirable for many national mapping and cadastral agencies, especially in developed countries.

• Language: English
• Keywords: geodesy, land, visible boundary, cadastre, maintenance, UAV, deep learning
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FGG - Faculty of Civil and Geodetic Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2022
• Number of pages: 17 str.
• Numbering: Vol. 11, iss. 5, art. 298
• PID: 20.500.12556/RUL-137057
• UDC: 528.4
• ISSN on article: 2220-9964
• DOI: 10.3390/ijgi11050298
• COBISS.SI-ID: 107071235
• Publication date in RUL: 31.05.2022

Record is a part of a journal

• Title: ISPRS international journal of geo-information
• Shortened title: ISPRS int. j. geo-inf.
• Publisher: MDPI
• ISSN: 2220-9964
• COBISS.SI-ID: 18678550

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: geodezija, zemljišče, vidna meja, kataster, vzdrževanje, UAV, globoko učenje