CadNet: a Deep Learning Model with Enhanced Line Connectivity for Cadastral Boundary Delineation
Abstrak
Cadastral mapping is a critical component of establishing a legal land administration system. Currently, an estimated 70% of the global population lacks access to formalized land rights through such systems, highlighting the urgency of accelerating the mapping of property rights. This study introduces an innovative methodology that integrates advanced deep learning techniques with remote sensing imagery to automate the extraction of cadastral boundaries. Our proposed model, CadNet, significantly outperforms baseline models. Moreover, CadNet is trained on a more extensive and diverse dataset than recent studies in this field. The results demonstrate a robust framework for advancing research in cadastral mapping leveraging deep learning and remote sensing technologies. Our codebase is available at https://github.com/jeroengrift/cadnet
Penulis (3)
Jeroen Grift
C. Persello
M. Koeva
Akses Cepat
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Cek di sumber asli →- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/IGARSS55030.2025.11242683
- Akses
- Open Access ✓