Extracting Polygons of Visible Cadastral Boundaries Using Deep Learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Formal land registration systems are out of reach for most of the world’s population. Conventional mapping methods, such as high-precision ground surveys, are costly, making them inaccessible, especially in low- and middle-income countries. With the introduction of fit-for-purpose land administration, automatic feature extraction techniques have been actively investigated to accelerate the land rights mapping process. Therefore, in our research, we assessed the potential of deep learning to extract cadastral boundaries from very high-resolution images. Our study adopts a multitask learning strategy, which utilizes state of the art U-Net model for the segmentation task, whereas the frame field learning method provides structural information for the subsequent active contour model to produce regularized vector polygons. The experimental results show that the combined U-Net model and frame field information produced polygons with higher accuracy compared to a segmentation method on its own.
Original languageEnglish
Title of host publicationIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
Number of pages4
ISBN (Electronic)9798350320107
ISBN (Print)979-8-3503-3174-5
Publication statusPublished - 20 Oct 2023
Event43rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena Convention Center, Pasadena, United States
Duration: 16 Jul 202321 Jul 2023
Conference number: 43


Conference43rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Abbreviated titleIGARSS 2023
Country/TerritoryUnited States
Internet address


  • Deep learning
  • Surveys
  • Learning systems
  • Image segmentation
  • Sociology
  • Geoscience and remote sensing
  • Feature extraction
  • 2023 OA procedure


Dive into the research topics of 'Extracting Polygons of Visible Cadastral Boundaries Using Deep Learning'. Together they form a unique fingerprint.

Cite this