Rahimpour, N., Azadbakht, A., Tahmasbi, M., Farahani, H., Kheradpishe, S.R., Javaheri, A.. (1403). Automatic Cadastral Boundary Detection of Very High Resolution Images Using Mask R-CNN. فناوری آموزش, 12(2), 525-534. doi: 10.22061/jecei.2024.10650.727
N. Rahimpour; A. Azadbakht; M. Tahmasbi; H. Farahani; S.R. Kheradpishe; A. Javaheri. "Automatic Cadastral Boundary Detection of Very High Resolution Images Using Mask R-CNN". فناوری آموزش, 12, 2, 1403, 525-534. doi: 10.22061/jecei.2024.10650.727
Rahimpour, N., Azadbakht, A., Tahmasbi, M., Farahani, H., Kheradpishe, S.R., Javaheri, A.. (1403). 'Automatic Cadastral Boundary Detection of Very High Resolution Images Using Mask R-CNN', فناوری آموزش, 12(2), pp. 525-534. doi: 10.22061/jecei.2024.10650.727
Rahimpour, N., Azadbakht, A., Tahmasbi, M., Farahani, H., Kheradpishe, S.R., Javaheri, A.. Automatic Cadastral Boundary Detection of Very High Resolution Images Using Mask R-CNN. فناوری آموزش, 1403; 12(2): 525-534. doi: 10.22061/jecei.2024.10650.727
Department of computer and data sciences, Faculty of mathematical sciences , Shahid Beheshti University, Tehran, Iran.
تاریخ دریافت: 15 اسفند 1402،
تاریخ بازنگری: 13 تیر 1403،
تاریخ پذیرش: 25 تیر 1403
چکیده
Background and Objectives: Cadastral boundary detection deals with locating the boundary of the ownership and use of land. Recently, there has been high demand for accelerating and improving the automatic detection of cadastral mapping. As this problem is in its starting point, there are few researches using deep learning algorithms. Methods: In this paper, we develop an algorithm with a Mask R-CNN core followed with geometric post-processing methods that improve the quality of the output. Many researches use classification or semantic segmentation but our algorithm employs instance segmentation. Our algorithm includes two parts, each of which consists of a few phases. In the first part, we use Mask R-CNN with the backbone of a pre-trained ResNet-50 on the ImageNet dataset. In the second part, we apply three geometric post-processing methods to the output of the first part to get better overall output. Here, we also use computational geometry to introduce a new method for simplifying lines which we call pocket-based simplification algorithm. Results: We used 3 google map images with sizes 4963 × 2819, 3999 × 3999, and 5520 × 3776 pixels. And divide them to overlapping and non-overlapping 400×400 patches used for training the algorithm. Then we tested it on a google map image from Famenin region in Iran. To evaluate the performance of our algorithm, we use popular metrics Recall, Precision, and F-score. The highest Recall is 95%, which also maintains a high precision of 72%. This results in an F-score of 82%. Conclusion: The idea of semantic segmentation to derive boundary of regions, is new. We used Mask R-CNN as the core of our algorithm, that is known as a very suitable tools for semantic segmentation. Our algorithm performs geometric post-process improves the f-score by almost 10 percent. The scores for a region in Iran containing many small farms is very good.
