Bentley Systems, Inc.

Evan Rausch-Larouche

Karl-Alexandre Jahjah

Marc-André Gardner

Marc-André Lapointe

Simon Savary

Hugo Bergeron

Kaustubh Page

Raphaël Melancon

In example embodiments, techniques are provided for using a combination of multiple ML models and signal processing to extract links and connectivity from a schematic diagram in an image-only format. A first ML model (i.e. link segmenter) may produce a first set of predictions about the positions of link segments in the schematic diagram (e.g., in the form of a segmentation map). A second ML model (i.e. keypoint detector) may produce a second set of predictions about starting and stopping points of link segments in the schematic diagram (e.g., in the form of one or more heatmaps). A signal processing module may combine the first set of predictions and the second set of predictions to produce a description of links and connectivity they provide (e.g., combining the segmentation map with data from the one or more heatmaps). The results of the combining may be saved as a graph connectivity matrix.

Techniques for extracting links and connectivity from schematic diagrams

In example embodiments, techniques are provided to automatically identify misclassified elements of an infrastructure model using machine learning. In a first set of embodiments, supervised machine learning is used to train one or more classification models that use different types of data describing elements (e.g., a geometric classification model that uses geometry data, a natural language processing (NLP) classification model that uses textual data, and an omniscient (Omni) classification model that uses a combination of geometry and textual data; or a single classification model that uses geometry data, textual data, and a combination of geometry and textual data). Predictions from classification models (e.g., predictions from the geometric classification model, NLP classification model and the Omni classification model) are compared to identify misclassified elements, or a prediction of misclassified elements directly produced (e.g., from the single classification model). In a second set of embodiments, unsupervised machine learning is used to detect abnormal associations in data describing elements (e.g., geometric data and textual data) that indicate misclassifications. Identified misclassifications are displayed to a user for review and correction.

Automatic identification of misclassified elements of an infrastructure model

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Evan Rausch-Larouche