Automatic Cadastral Boundary Detection of Very High Resolution Images Using Mask R-CNN

kground 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-۵۰ 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 ۳ google map images with sizes ۴۹۶۳ × ۲۸۱۹, ۳۹۹۹ × ۳۹۹۹, and ۵۵۲۰ × ۳۷۷۶ pixels. And divide them to overlapping and non-overlapping ۴۰۰×۴۰۰ 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 ۹۵%, which also maintains a high precision of ۷۲%. This results in an F-score of ۸۲%.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 ۱۰ percent. The scores for a region in Iran containing many small farms is very good.

Keywords:

Remote Sensing , Mask R-CNN , Cadastral Mapping , Instance Segmentation