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

TL;DR

This paper presents a deep learning framework using Mask R-CNN and geometric post-processing, including a novel pocket-based line simplification, to improve automatic cadastral boundary detection in high-resolution images, achieving high recall and precision.

Contribution

It introduces a combined approach of Mask R-CNN with geometric post-processing, including a new pocket-based line simplification method, for enhanced cadastral boundary detection.

Findings

Recall of 95% achieved in boundary detection

Precision maintained at 72%, resulting in an F-score of 82%

Pocket-based simplification outperforms Douglas-Peucker algorithm

Abstract

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.