Santehanser, TimoTimoSantehanserSchönfelder, PhillipPhillipSchönfelder2024-10-212024-10-212024-09-1835. Forum Bauinformatik, fbi 2024: 317-324https://hdl.handle.net/11420/49623Site plans play a crucial role in construction projects, providing detailed layouts of structures and infrastructure components. Extracting specific information, such as sewage system details including shafts and pipeline routes, from these plans is essential for accurate cost estimation. However, it remains a labor-intensive task, especially for pixel-based drawings lacking machineinterpretable vector geometries and machine-readable text elements. In this study, we conceptualize an automated method to streamline this process, leveraging object detection and optical character recognition (OCR) techniques. The proposed approach involves three main steps: (1) locating the legend region in which the shaft symbol is specified using a specialized OCR method, (2) identifying the relevant shaft symbol from the plan’s legend, and (3) detecting shaft locations within site plans using state-of-the-art object detection algorithms. Developing this method aims to significantly increase efficiency in construction cost estimation by automating the tedious task of extracting and analyzing site plan data. The preliminary results included in this study demonstrate candidate techniques for symbol processing in site plans.enhttps://creativecommons.org/licenses/by/4.0/automationcomputer visiondeep learningdrawing analysissite planTechnology::624: Civil Engineering, Environmental EngineeringTechnology::690: Building, ConstructionComputer Science, Information and General Works::006: Special computer methodsConference Paper10.15480/882.1353010.15480/882.13530Conference Paper