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

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

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.

• Jezik: Angleški jezik
• Ključne besede: geodesy, land, visible boundary, cadastre, maintenance, UAV, deep learning
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FGG - Fakulteta za gradbeništvo in geodezijo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2022
• Št. strani: 17 str.
• Številčenje: Vol. 11, iss. 5, art. 298
• PID: 20.500.12556/RUL-137057
• UDK: 528.4
• ISSN pri članku: 2220-9964
• DOI: 10.3390/ijgi11050298
• COBISS.SI-ID: 107071235
• Datum objave v RUL: 31.05.2022

Gradivo je del revije

• Naslov: ISPRS international journal of geo-information
• Skrajšan naslov: ISPRS int. j. geo-inf.
• Založnik: MDPI
• ISSN: 2220-9964
• COBISS.SI-ID: 18678550

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: geodezija, zemljišče, vidna meja, kataster, vzdrževanje, UAV, globoko učenje