穆扎地图矢量化的人机协作范式转变

高效地对手绘地籍图（如孟加拉的穆扎图）进行矢量化由于其复杂结构而是一个重大挑战。当前的手动数字化方法耗时且浪费人力资源。我们的研究提出了一种半自动化方法来 streamlining 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.