Recently, there has been a high demand for accelerating and improving the
detection of automatic cadastral mapping. As this problem is in its starting
point, there are many methods of computer vision and deep learning that have
not been considered yet. In this paper, we focus on deep learning and provide
three geometric post-processing methods that improve the quality of the work.
Our framework includes two parts, each of which consists of a few phases. Our
solution to this problem uses instance segmentation. In the first part, we use
Mask R-CNN with the backbone of pre-trained ResNet-50 on the ImageNet dataset.
In the second phase, 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 it pocket-based simplification algorithm. For evaluating the quality of
our solution, we use popular formulas in this field which are recall, precision
and F-score. The highest recall we gain is 95 percent which also maintains high
Precision of 72 percent. This resulted in an F-score of 82 percent.
Implementing instance segmentation using Mask R-CNN with some geometric
post-processes to its output gives us promising results for this field. Also,
results show that pocket-based simplification algorithms work better for
simplifying lines than Douglas-Puecker algorithm.

通过使用深度学习、几何后处理方法和基于实例分割的 Mask R-CNN 模型来改进自动宗地绘制检测质量，其中关键方法是引入基于口袋的简化算法，实验结果表明该方法优于 Douglas-Puecker 算法。