Abstract: In example embodiments, techniques are provided for labeling elements of an infrastructure model with classes. The techniques may be implemented by a labeling tool that uses an ML model to create element selections and provides a cycle review mode to speed review within such selections. The labeling tool may further provide for two file loading and a number of visualization schemes to speed comparison of label files and prediction files.

Abstract: 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).

Abstract: In example embodiments, techniques are provided to automatically classify individual elements of an infrastructure model by training one or more machine learning algorithms on classified infrastructure models, producing a classification model that maps features to classification labels, and utilizing the classification model to classify the individual elements of the infrastructure model. The resulting classified elements may then be readily subject to analytics, for example, enabling the display of dashboards for monitoring project performance and the impact of design changes. Such techniques enable classification of elements of new infrastructure models or in updates to existing infrastructure models.

Abstract: 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).

Abstract: In example embodiments, techniques are provided to automatically classify individual elements of an infrastructure model by training one or more machine learning algorithms on classified infrastructure models, producing a classification model that maps features to classification labels, and utilizing the classification model to classify the individual elements of the infrastructure model. The resulting classified elements may then be readily subject to analytics, for example, enabling the display of dashboards for monitoring project performance and the impact of design changes. Such techniques enable classification of elements of new infrastructure models or in updates to existing infrastructure models.

Abstract: An optical time distributor includes: a master clock including: a master comb; a transfer comb; and a free-space optical terminal; and a remote clock in optical communication with the master clock via a free space link and including: a remote comb that produces: a remote clock coherent optical pulse train output; a remote coherent optical pulse train; a free-space optical terminal in optical communication: with the remote comb; and with the free-space optical terminal of the master clock via the free space link, and that: receives the remote coherent optical pulse train from the remote comb; receives the master optical signal from the free-space optical terminal of the master clock; produces the remote optical signal in response to receipt of the remote coherent optical pulse train; and communicates the remote optical signal to the free-space optical terminal of the master clock.

Abstract: An optical time distributor includes: a master clock including: a master comb; a transfer comb; and a free-space optical terminal; and a remote clock in optical communication with the master clock via a free space link and including: a remote comb that produces: a remote clock coherent optical pulse train output; a remote coherent optical pulse train; a free-space optical terminal in optical communication: with the remote comb; and with the free-space optical terminal of the master clock via the free space link, and that: receives the remote coherent optical pulse train from the remote comb; receives the master optical signal from the free-space optical terminal of the master clock; produces the remote optical signal in response to receipt of the remote coherent optical pulse train; and communicates the remote optical signal to the free-space optical terminal of the master clock.