The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 565–570, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-565-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 565–570, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-565-2022
 
30 May 2022
30 May 2022

AUTOMATING THE UNDERGROUND CADASTRAL SURVEY: A PROCESSING CHAIN PROPOSAL

A. Carreaud1, F. Mariani2, and A. Gressin1 A. Carreaud et al.
  • 1University of Applied Sciences Western Switzerland (HES-SO / HEIG-VD), Insit Institute, 1400 Yverdon-les-Bains, Switzerland
  • 2Geneva Industrial Services, Switzerland

Keywords: Deep learning, Photogrammetry, Automatic 3D segmentation

Abstract. In order to ensure the proper functioning and evolution of underground networks (water, gas, etc.) over time, municipal services need to maintain accurate and up-to-date maps. Such maps are generally updated using traditional data acquisition methods (total station or GNSS), which are time-consuming, expensive, and require several teams of surveyors in the field. In this context, an important topic of research is the automation of the updating of the underground cadastre in order to save time, money, and human effort. In this paper, we present a new method that we developed ranging from the choice of the acquisition system, the tests carried out in the field to the detection of objects and the automatic segmentation in a 3D point cloud. We have chosen to use a convolutional neural network on images for the detection of objects that are part of the underground cadastre. As the next step, objects are projected to obtain a 3D point cloud segmented based on the object type. The vectorization step is still under development so that objects can be converted to vector format and therefore be used for updating the cadastre. The results based on excavation sites with well-represented objects in our training database are excellent, approaching 96% accuracy. However, the detection of rare objects is much less good and thus remains a topic for future research. Overall, the complete processing chain allowing to automate as much as possible the update of an underground cadastre is presented in this paper.