Abstract: A method for assigning a number of lanes and a direction of travel on a path includes receiving location data including a plurality of location points, projecting the plurality of location points on to an aggregation axis perpendicular to a centerline of the vehicle path, grouping the plurality of location points as projected onto the aggregation axis into one or more clusters, and determining the number of vehicle lanes of the vehicle path based on a count of the one or more clusters.

Abstract: An approach is provided for generating navigation data of a geographical location. The approach involves identifying a landmark located along a source road from a source image and segmenting the source image using a deep learning model to identify a segmentation mask. The approach also involves generating a template image based on the segmentation mask and a street image of the landmark, and matching the template image successively with a sequence of images of the landmark to determine a confidence score. The approach further involves, identifying a first image from the sequence of images with confidence score below a predetermined threshold, and selecting a second image with confidence score above the predetermined threshold from the sequence of images. The approach further involves calculating a visibility distance of the landmark based on the source image and the second image, and generating the navigation data based on the calculated visibility distance.

Abstract: An apparatus and method for lane feature detection from an image is performed according to predetermined path geometry. An image including at least one path is received. The image may be an aerial image. Map data, corresponding to the at least one path and defining the predetermined path geometry is selected. The image is modified according to the selected map data including the predetermined path geometry. A lane feature prediction model is generated or configured based on the modified image. A subsequent image is provided to the lane feature prediction model for a prediction of at least one lane feature.

Abstract: A method, apparatus and computer program product for detecting a start location and an end location of a bridge are described herein. In the context of a method, one or more sequences of images, at least one sequence of images corresponding with a side barrier portion of the bridge, may be identified from image data corresponding with a probable location of the bridge. Each side barrier portion may comprise a plurality of side barrier segments. The method may identify one or more characteristics of the plurality of side barrier segments. The method may determine the start location and the end location of the bridge based at least in part by identifying characteristic differences of the plurality of side barrier segments. The method may cause transmission of information corresponding with the start location and the end location of the bridge for updating a mapping or navigation system.

Abstract: A method, a system, and a computer program product may be provided for generating map data associated with one or more objects in a region. The method includes receiving point cloud data associated with the region. The method further includes generating a georeferenced top down raster image of the region, based on the point cloud data. The georeferenced top down raster image is indicative of a top surface of each of the one or more objects. The method further includes determining boundary data of the top surface of each of the one or more objects, based on the georeferenced top down raster image and generating the map data associated with the one or more objects, based on the boundary data of the top surface of each of the one or more objects.

Abstract: The invention provides a system, a method, and a computer programmable product for generating map data of a region. The method comprises obtaining image data of the region. The method further comprises determining, based on the image data, a plurality of road objects corresponding to at least one road segment associated with the region. The method further comprises generating at least one cluster of road objects corresponding to the at least one road segment, based on the plurality of road objects. The method further comprises generating the map data of the region based on the at least one cluster of road objects.

Abstract: A method, apparatus and computer program product for detecting a start location and an end location of a bridge are described herein. In the context of a method, one or more sequences of images, at least one sequence of images corresponding with a side barrier portion of the bridge, may be identified from image data corresponding with a probable location of the bridge. Each side barrier portion may comprise a plurality of side barrier segments. The method may identify one or more characteristics of the plurality of side barrier segments. The method may determine the start location and the end location of the bridge based at least in part by identifying characteristic differences of the plurality of side barrier segments. The method may cause transmission of information corresponding with the start location and the end location of the bridge for updating a mapping or navigation system.

Abstract: A method for assigning a number of lanes and a direction of travel on a path includes receiving location data including a plurality of location points, projecting the plurality of location points on to an aggregation axis perpendicular to a centerline of the vehicle path, grouping the plurality of location points as projected onto the aggregation axis into one or more clusters, and determining the number of vehicle lanes of the vehicle path based on a count of the one or more clusters.

Abstract: An apparatus and method for lane feature detection from an image is performed according to predetermined path geometry. An image including at least one path is received. The image may be an aerial image. Map data, corresponding to the at least one path and defining the predetermined path geometry is selected. The image is modified according to the selected map data including the predetermined path geometry. A lane feature prediction model is generated or configured based on the modified image. A subsequent image is provided to the lane feature prediction model for a prediction of at least one lane feature.

Abstract: An apparatus and method for lane feature detection from an image is performed according to predetermined path geometry. An image including at least one path is received. The image may be an aerial image. Map data, corresponding to the at least one path and defining the predetermined path geometry is selected. The image is modified according to the selected map data including the predetermined path geometry. A lane feature prediction model is generated or configured based on the modified image. A subsequent image is provided to the lane feature prediction model for a prediction of at least one lane feature.

Abstract: The invention provides a system, a method, and a computer programmable product for generating map data of a region. The method comprises obtaining image data of the region. The method further comprises determining, based on the image data, a plurality of road objects corresponding to at least one road segment associated with the region. The method further comprises generating at least one cluster of road objects corresponding to the at least one road segment, based on the plurality of road objects. The method further comprises generating the map data of the region based on the at least one cluster of road objects.

Abstract: A method for assigning a number of lanes and a direction of travel on a path includes receiving location data including a plurality of location points, projecting the plurality of location points on to an aggregation axis perpendicular to a centerline of the vehicle path, grouping the plurality of location points as projected onto the aggregation axis into one or more clusters, and determining the number of vehicle lanes of the vehicle path based on a count of the one or more clusters.

Abstract: A method, a system, and a computer program product may be provided for generating map data associated with one or more objects in a region. The method includes receiving point cloud data associated with the region. The method further includes generating a georeferenced top down raster image of the region, based on the point cloud data. The georeferenced top down raster image is indicative of a top surface of each of the one or more objects. The method further includes determining boundary data of the top surface of each of the one or more objects, based on the georeferenced top down raster image and generating the map data associated with the one or more objects, based on the boundary data of the top surface of each of the one or more objects.

Abstract: A method for assigning a number of lanes and a direction of travel on a path includes receiving location data including a plurality of location points, projecting the plurality of location points on to an aggregation axis perpendicular to a centerline of the vehicle path, grouping the plurality of location points as projected onto the aggregation axis into one or more clusters, and determining the number of vehicle lanes of the vehicle path based on a count of the one or more clusters.

Abstract: An apparatus and method for lane feature detection from an image is performed according to predetermined path geometry. An image including at least one path is received. The image may be an aerial image. Map data, corresponding to the at least one path and defining the predetermined path geometry is selected. The image is modified according to the selected map data including the predetermined path geometry. A lane feature prediction model is generated or configured based on the modified image. A subsequent image is provided to the lane feature prediction model for a prediction of at least one lane feature.

Abstract: An approach is provided for generating navigation data of a geographical location. The approach involves identifying a landmark located along a source road from a source image and segmenting the source image using a deep learning model to identify a segmentation mask. The approach also involves generating a template image based on the segmentation mask and a street image of the landmark, and matching the template image successively with a sequence of images of the landmark to determine a confidence score. The approach further involves, identifying a first image from the sequence of images with confidence score below a predetermined threshold, and selecting a second image with confidence score above the predetermined threshold from the sequence of images. The approach further involves calculating a visibility distance of the landmark based on the source image and the second image, and generating the navigation data based on the calculated visibility distance.