Abstract: A restraint verification platform may obtain video data video data of a passenger in a passenger seat. The restraint verification platform may analyze the video data using one or more machine learning models trained to detect passengers in passenger seats and passenger restraints of the passenger seats. The restraint verification platform may detect, using the one or more machine learning models and based on analyzing the video data, the passenger in the passenger seat and a passenger restraint of the passenger seat. The restraint verification platform may determine, based on analyzing the video data, that the passenger restraint is not properly securing the passenger. The restraint verification platform may perform an action based on determining that the passenger restraint is not properly securing the passenger.

Abstract: A guest assistance platform may analyze video data of an environment that includes a liquid substance. The video data is analyzed using a machine learning model trained to detect objects in the liquid substance. The guest assistance platform may detect an individual in the liquid substance based on analyzing the video data and may determine a measure of confidence associated with detecting the individual in the liquid substance. The guest assistance platform may determine one or more confidence factors associated with detecting the individual in the liquid substance. The guest assistance platform may modify the measure of confidence using the one or more confidence factors and may determine whether the modified measure of confidence satisfies a confidence threshold. The guest assistance platform may selectively cause assistance to be provided to the individual or continue analyzing the video data based on whether the modified measure of confidence satisfies the confidence threshold.

Abstract: A behavior detection platform may obtain video data of an environment including a guest and may determine a set of normal guest behaviors associated with the environment. The platform may analyze, using a first machine learning model, the video data to identify features of the guest and analyze, using a second machine learning model, the video data and the features to determine an actual guest behavior of the guest. The platform may predict, using the second machine learning model, a predicted guest behavior of the guest based on the actual guest behavior. The behavior detection platform may determine that at least one of the actual guest behavior or the predicted guest behavior is not included in the set of normal guest behaviors. The platform may cause assistance to be provided to the guest based on determining that the actual guest behavior or the predicted guest behavior are not included.

Abstract: A method for processing geospatial datasets corresponding to geospatial objects, the method having the steps of extracting geospatial attributes from the geospatial datasets, locating extracted geospatial attributes corresponding to a particular geospatial object at a particular point in time, and generating output indicative of the particular geospatial object at the particular point in time utilizing the located geospatial attributes.

Abstract: Methods for locating a feature on geospatial imagery and systems for performing those methods are disclosed. An accuracy level of each of a plurality of geospatial vector datasets available in a database can be determined. Each of the plurality of geospatial vector datasets corresponds to the same spatial region as the geospatial imagery. The geospatial vector dataset having the highest accuracy level may be selected. When the selected geospatial vector dataset and the geospatial imagery are misaligned, the selected geospatial vector dataset is aligned to the geospatial imagery. The location of the feature on the geospatial imagery is then determined based on the selected geospatial vector dataset and outputted via a display device.

Abstract: A method, computer program, and system for linking content to individual image features are provided. A section of an image is identified. A plurality of features associated with the section of the image is determined. Each of the plurality of features corresponds to at least one position within the section of the image. Content associated with the plurality of features is retrieved from a content repository. The content is linked to the plurality of features based on at least one rule. The content is then presented.

Abstract: Document relevance is determined with respect to a region of interest (ROI). A set of location references may be associated with a set of documents. The system selects location references associated with an ROI and then selects documents corresponding to the selected location references. The selected documents can be reported or processed further. A document-location reference index can be accessed when the present system is ‘online’ and processing a request for documents relevant to an ROI. The document-location reference index may be generated and updated while the present system is ‘offline’ and not processing a request for documents. The resulting relevant documents may be provided to a user in response to a document search associated with the ROI or along with an advertisement associated with the ROI.

Abstract: Automatic conflation systems and techniques which provide vector-imagery conflation and map-imagery conflation. Vector-imagery conflation is an efficient approach that exploits knowledge from multiple data sources to identify a set of accurate control points. Vector-imagery conflation provides automatic and accurate alignment of various vector datasets and imagery, and is appropriate for GIS applications, for example, requiring alignment of vector data and imagery over large geographical regions. Map-imagery conflation utilizes common vector datasets as “glue” to automatically integrate street maps with imagery. This approach provides automatic, accurate, and intelligent images that combine the visual appeal and accuracy of imagery with the detailed attribution information often contained in such diverse maps. Both conflation approaches are applicable for GIS applications requiring, for example, alignment of vector data, raster maps, and imagery.

Abstract: Automatic conflation systems and techniques which provide vector-imagery conflation and map-imagery conflation. Vector-imagery conflation is an efficient approach that exploits knowledge from multiple data sources to identify a set of accurate control points. Vector-imagery conflation provides automatic and accurate alignment of various vector datasets and imagery, and is appropriate for GIS applications, for example, requiring alignment of vector data and imagery over large geographical regions. Map-imagery conflation utilizes common vector datasets as “glue” to automatically integrate street maps with imagery. This approach provides automatic, accurate, and intelligent images that combine the visual appeal and accuracy of imagery with the detailed attribution information often contained in such diverse maps. Both conflation approaches are applicable for GIS applications requiring, for example, alignment of vector data, raster maps, and imagery.

Abstract: A method for processing geospatial datasets corresponding to geospatial objects, the method having the steps of extracting geospatial attributes from the geospatial datasets, locating extracted geospatial attributes corresponding to a particular geospatial object at a particular point in time, and generating output indicative of the particular geospatial object at the particular point in time utilizing the located geospatial attributes.

Abstract: Methods for locating a feature on geospatial imagery and systems for performing those methods are disclosed. An accuracy level of each of a plurality of geospatial vector datasets available in a database can be determined. Each of the plurality of geospatial vector datasets corresponds to the same spatial region as the geospatial imagery. The geospatial vector dataset having the highest accuracy level may be selected. When the selected geospatial vector dataset and the geospatial imagery are misaligned, the selected geospatial vector dataset is aligned to the geospatial imagery. The location of the feature on the geospatial imagery is then determined based on the selected geospatial vector dataset and outputted via a display device.

Abstract: Document relevance is determined with respect to a region of interest (ROI). A set of location references may be associated with a set of documents. The system selects location references associated with an ROI and then selects documents corresponding to the selected location references. The selected documents can be reported or processed further. A document-location reference index can be accessed when the present system is ‘online’ and processing a request for documents relevant to an ROI. The document-location reference index may be generated and updated while the present system is ‘offline’ and not processing a request for documents. The resulting relevant documents may be provided to a user in response to a document search associated with the ROI or along with an advertisement associated with the ROI.

Abstract: Methods for locating a feature on geospatial imagery and systems for performing those methods are disclosed. An accuracy level of each of a plurality of geospatial vector datasets available in a database can be determined. Each of the plurality of geospatial vector datasets corresponds to the same spatial region as the geospatial imagery. The geospatial vector dataset having the highest accuracy level may be selected. When the selected geospatial vector dataset and the geospatial imagery are misaligned, the selected geospatial vector dataset is aligned to the geospatial imagery. The location of the feature on the geospatial imagery is then determined based on the selected geospatial vector dataset and outputted via a display device.

Abstract: Automatic conflation systems and techniques which provide vector-imagery conflation and map-imagery conflation. Vector-imagery conflation is an efficient approach that exploits knowledge from multiple data sources to identify a set of accurate control points. Vector-imagery conflation provides automatic and accurate alignment of various vector datasets and imagery, and is appropriate for GIS applications, for example, requiring alignment of vector data and imagery over large geographical regions. Map-imagery conflation utilizes common vector datasets as “glue” to automatically integrate street maps with imagery. This approach provides automatic, accurate, and intelligent images that combine the visual appeal and accuracy of imagery with the detailed attribution information often contained in such diverse maps. Both conflation approaches are applicable for GIS applications requiring, for example, alignment of vector data, raster maps, and imagery.

Abstract: A method, computer program, and system for linking content to individual image features are provided. A section of an image is identified. A plurality of features associated with the section of the image is determined. Each of the plurality of features corresponds to at least one position within the section of the image. Content associated with the plurality of features is retrieved from a content repository. The content is linked to the plurality of features based on at least one rule. The content is then presented.

Abstract: Automatic conflation systems and techniques which provide vector-imagery conflation and map-imagery conflation. Vector-imagery conflation is an efficient approach that exploits knowledge from multiple data sources to identify a set of accurate control points. Vector-imagery conflation provides automatic and accurate alignment of various vector datasets and imagery, and is appropriate for GIS applications, for example, requiring alignment of vector data and imagery over large geographical regions. Map-imagery conflation utilizes common vector datasets as “glue” to automatically integrate street maps with imagery. This approach provides automatic, accurate, and intelligent images that combine the visual appeal and accuracy of imagery with the detailed attribution information often contained in such diverse maps. Both conflation approaches are applicable for GIS applications requiring, for example, alignment of vector data, raster maps, and imagery.

Abstract: This invention is novel structure of an over-current protection device and manufacturing method thereof. The over-current protection device is formed with a main body with a lead frame and a ceramic fiber lead wound by a metal wire exteriorly, by coating the exterior of the whole lead with a thermally-insulating material, and then cladding the lead with a flame retardation material having an electrical insulation characteristic.

Abstract: An insulated structure of a chip array component and fabrication method of the same, the element is fabricated by enclosing its main body with a dense layer of high surface insulation resistance material, and then exposing the portions of the main body where terminal electrodes are to be formed by removing the dense layer of high surface insulation resistance material by employing sand blasting, laser trimming, grinding, or etching process.

Abstract: An insulated structure of a chip array component and fabrication method of the same, the element is fabricated by enclosing its main body with a dense layer of high surface insulation resistance material, and then exposing the portions of the main body where terminal electrodes are to be formed by removing the dense layer of high surface insulation resistance material by employing sand blasting, laser trimming, grinding, or etching process.