TY - JOUR A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. A1 - Albizu, Natalia A1 - Schaffernicht, Erik A1 - Lilienthal, Achim J. T1 - GNN-DM: A Graph Neural Network Framework for Real-World Gas Distribution Mapping N2 - Gas distribution mapping (GDM) is essential for industrial safety and environmental monitoring, as it enables real-time hazard detection and air quality assessment. Traditional GDM methods, such as kernel-based techniques, struggle to reconstruct complex gas plume dynamics accurately. While deep learning has shown promise for GDM, two critical challenges hinder its practical use: the scarcity of available training data and the incompatibility of conventional architectures with irregular sensor layouts. To address these limitations, we propose GNN-DM, a graph neural network-based model for GDM that incorporates the relational structure of sensor networks to infer high-resolution maps from minimal, irregular inputs. The model is pretrained on synthetic gas dispersion data generated from measured wind data and fine-tuned on two industrial datasets collected on a ferry car deck and in a hot rolling mill. Compared with established GDM techniques, GNN-DM achieves higher accuracy on synthetic and real-world data, highlighting the potential of graph-based learning for practical gas mapping applications. KW - Environmental monitoring KW - Sensor networks KW - Transfer learning KW - Deep learning PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-647363 DO - https://doi.org/10.1109/JSEN.2025.3617158 SN - 1530-437X VL - 25 IS - 22 SP - 42171 EP - 42179 PB - Institute of Electrical and Electronics Engineers (IEEE) AN - OPUS4-64736 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -