用半自动方法高效转换孟加拉手绘地籍图，提升矢量化效率。
A Paradigm Shift in Mouza Map Vectorization: A Human-Machine Collaboration Approach
- 分步处理边界与编号，结合CNN和光滑算法实现矢量化。
- 在真实地图上验证，效率显著优于传统人工方式。
- 适合需要高精度地图数字化的政府或测绘机构使用。
由于结构复杂，手绘地籍图（如孟加拉国的Mouza地图）的高效矢量化面临挑战。当前的人工数字化方法耗时且费力。本研究提出一种半自动化方法，通过分离地块边界与地块编号，并分别进行矢量化处理，显著节省时间和人力。采用卷积神经网络（CNN）模型对地图进行预处理与编号识别，基于自建标注数据集训练；同时引入基于多种矢量图特征观察的平滑算法。尽管需人工介入以确保精度，实验结果表明该方法在多个地图样本上均优于现有流程，定量与定性评估及用户研究均证实其有效性。
原文摘要 · Abstract (English)
Efficient vectorization of hand-drawn cadastral maps, such as Mouza maps in Bangladesh, poses a significant challenge due to their complex structures. Current manual digitization methods are time-consuming and labor-intensive. Our study proposes a semi-automated approach to streamline the digitization process, saving both time and human resources. Our methodology focuses on separating the plot boundaries and plot identifiers and applying our digitization methodology to convert both of them into vectorized format. To accomplish full vectorization, Convolutional Neural Network (CNN) models are utilized for pre-processing and plot number detection along with our smoothing algorithms based on the diversity of vector maps. The CNN models are trained with our own labeled dataset, generated from the maps, and smoothing algorithms are introduced from the various observations of the map's vector formats. Further human intervention remains essential for precision. We have evaluated our methods on several maps and provided both quantitative and qualitative results with user study. The result demonstrates that our methodology outperforms the existing map digitization processes significantly.
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