ORIGINAL RESEARCH article
Front. Signal Process.
Sec. Image Processing
A graph generation pipeline for critical infrastructures based on heuristics, images and depth data
Provisionally accepted
Mike Diessner*
Yannick Tarant- Institute for the Protection of Terrestrial Infrastructures, German Aerospace Center, Sankt Augustin, Germany
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Virtual representations of physical critical infrastructures, such as water or energy plants, are used for simulations and digital twins to ensure resilience and continuity of their services. These models usually require 3D point clouds from laser scanners that are expensive to acquire and require specialist knowledge to use. In this article, we present a prototypical graph generation pipeline based on photogrammetry. The pipeline detects relevant objects and predicts their relation using RGB images and depth data generated by a stereo camera. This more cost-effective approach uses deep learning for object detection and instance segmentation of the objects, and employs user-defined heuristics or rules to infer their relations. Results of two hydraulic systems show that this strategy can produce graphs close to the ground truth. While this study focuses on hydraulic systems, the general process can be used to tailor the method to other types of infrastructures and applications. The user-defined rules create transparency qualifying the pipeline to be used in the high stakes decision-making that is required for critical infrastructures.
Keywords: Critical infrastructure, Depth Data, Digital Twin, Graph generation, Image data, Photogrammetry, Relational graph, scene understanding
Received: 05 Dec 2025; Accepted: 12 Feb 2026.
Copyright: © 2026 Diessner and Tarant. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mike Diessner
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.