@article{isprs-archives-XLVIII-2-W4-2024-65-2024,

title = {INCAD: 2D VECTOR DRAWINGS CREATION USING INSTANCE SEGMENTATION},

author = {T. Betsas and A. Georgopoulos and A. Doulamis},

url = {https://isprs-archives.copernicus.org/articles/XLVIII-2-W4-2024/65/2024/},

doi = {10.5194/isprs-archives-XLVIII-2-W4-2024-65-2024},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},

volume = {XLVIII-2/W4-2024},

pages = {65\textendash72},

abstract = {Orthoimages are a common product used as a base in CAD software for vectorization purposes. In fact, vectorization of orthoimages constitutes a tedious and labour-intensive process which should be supervised by experts e.g. architects, chemical engineers etc. On the one hand, deep learning algorithms are used extensively nowadays achieving high quality results. On the other hand, deep learning algorithms require a huge amount of manually annotated data to be trained on, which is a very difficult process especially at pixel level applications like semantic segmentation and instance segmentation. However, the transformation of 2D CAD drawings into a suitable deep learning dataset (CAD2DLD) is underexplored ignoring a large source of data, created by experts. In this effort, the InCAD algorithm is proposed, which aims to automatically create 2D vector drawings using the YOLOv8 instance segmentation algorithm which was trained on CAD2DLD data. Additionally, a methodology for transforming 2D CAD drawings into a suitable deep learning dataset for instance segmentation, is presented. Finally, the proposed workflow is evaluated on the creation of 2D vector drawings of stones of a fortification wall achieving promising results (78.34 mIoU).},

keywords = {2D instance segmentation, 2D vector drawings, Cultural heritage, orthophotos, YOLOv8},

pubstate = {published},

tppubtype = {article}

}