Abstract: Systems and methods for validating drive pose refinement are provided. In some aspects, a method includes receiving image data that depicts an area of interest, and receiving a plurality of virtual points generated using the image data. The method also includes selecting at least one drive in the area of interest that captures the plurality of virtual points, and generating a refined pose track for each of the at least one drive by applying a drive alignment process to drive data from the at least one drive using the plurality virtual points. The method further includes comparing the refined pose track to a control pose track generated using control repoints, and generating, based on the comparison, a report that validates the refined pose track.

Abstract: An approach is provided for an asymmetric evaluation of polygon similarity. The approach, for instance, involves receiving a first polygon representing an object depicted in an image. The approach also involves generating a transformation of the image comprising image elements whose values are based on a respective distance that each image element is from a nearest image element located on a first boundary of the first polygon. The approach further involves determining a subset of the plurality of image elements of the transformation that intersect with a second boundary of a second polygon. The approach further involves calculating a polygon similarity of the second polygon with respect the first polygon based on the values of the subset of image elements normalized to a length of the second boundary of the second polygon.

Abstract: Systems and methods for correcting mapping information inaccuracies are described. In some aspects, the method includes receiving terrestrial data captured in an area of interest, and detecting features in the terrestrial data identifying ground points in the area of interest. The method also includes correlating the ground points with ground control points in the area of interest to determine a correspondence, and computing an aggregate of positional differences between corresponding points. The method further includes generating a report indicating a quality of the terrestrial data captured in the area of interest based on the aggregate.

Abstract: A method is provided for generating and revising map geometry based on a received image and probe data. A method may include: receiving probe data from a first period of time, where the probe data from a first period of time is from a plurality of probes within a predefined geographic region; generating a first image of the predefined geographic region based on the probe data from the first period of time; receiving probe data from a second period of time different from the first period of time, where the probe data from the second period of time is from a plurality of probes within the predefined geographic region; generating a second image based on the probe data from the second period of time; comparing the first image to the second image; and generating a revised route geometry based on changes detected between the first image and the second image.

Abstract: An approach is provided for image labeling for cross view alignment. The approach, for example, involves determining camera pose data, camera trajectory data, or a combination thereof for a first image depicting an area from a first perspective view. The approach also involves processing the camera pose data, the camera trajectory data, or a combination thereof to generate meta data indicating a position, an orientation, or a combination thereof of the first perspective view of the area relative to a second image depicting the area from a second perspective view. The approach further involves providing data for presenting the meta data in a user interface as an overlay on the second perspective view.

Abstract: Systems and methods for verifying mapping information are provided. In some aspects, a method includes receiving control data acquired in an area of interest, the control data comprising a plurality of control points, and receiving mapping data associated with the area of interest, the mapping data comprising a plurality of mapping points that correspond to the plurality control points. The method also includes applying a localization algorithm to the control data to generate a control track, and applying the localization algorithm to the mapping data to generate a mapping track. The method further includes comparing the control track and the mapping track to determine a difference.

Abstract: An approach is provided for a redundant feature detection engine. The approach, for instance, involves segmenting an input image into a plurality of grid cells for processing by the redundant feature detection engine. The redundant feature detection engine includes a neural network. The approach also involves, for each of the plurality of grid cells, initiating a prediction of an object code by the redundant feature detection engine. The object code is a predicted feature that uniquely identifies an object depicted in the input image. The approach further involves aggregating the plurality of grid cells into one or more clusters based on the object code predicted for said each grid cell. The approach further involves predicting one or more features of the object corresponding to a respective cluster of the one or more clusters by merging one or more feature prediction outputs of said each grid cell in the respective cluster.

Abstract: An approach is provided for a location-aware evaluation of a machine learning model. The approach, for example, involves designating a geographic area for creating an evaluation dataset for the machine learning model. The approach also involves separating a plurality of observation data records into the evaluation dataset and a training dataset based on a comparison of a respective data collection location of each of the plurality of observation data records to the geographic area. The training dataset is then used to train the machine learning model, and the evaluation dataset is used to evaluate the trained machine learning model.

Abstract: An approach is provided for determining a polygon of a geographic database that overlaps a candidate polygon or candidate point. The geographic database represents stored polygons as respective polygon points with zero area. The approach involves determining proximate polygon points from among the respective polygon points with zero area that are within a distance threshold of the candidate polygon or the candidate point. The approach also involves retrieving one or more proximate polygons from the geographic database that correspond to the one or more proximate polygon points. The approach further involves determining an intersection between the one or more proximate polygons and the candidate polygon or the candidate point. The approach then involves selecting the polygon that overlaps the candidate polygon or the candidate point based on the determined intersection.

Abstract: An approach is provided for generating a super-resolution image as a higher resolution version of an input image. The approach, for example, involves determining a set of tasks to be performed on the input image to facilitate generating the super-resolution image. The approach also involves selecting a combination of loss functions, wherein each loss function of the combination of loss functions is respectively a task-specific neural network pre-trained to perform a corresponding one of the set of tasks. The approach also involves training the super resolution neural network using the combination of loss functions as one or more layers of the super resolution neural network. The approach also involves using the trained super resolution neural network to generate the super-resolution image as a higher resolution version of the input image.

Abstract: An approach is provided for predicting a pose error for a sensor system based on a trained machine learning model. The approach, for example, involves receiving images depicting a survey point with a known physical location. The approach also involves determining meta-data associated with the sensor system used to capture the images. The approach further involves generating a ray from the capture location through a pixel location of the survey point on an image plane of each image. The approach further involves calculating an error between the ray generated for the image and the known physical location. The approach further involves training a machine learning model to predict a pose error from image data captured using the sensor system based on the error in combination with features extracted from the image and the meta-data for the image. The approach further involves providing the trained machined learning as an output.

Abstract: An approach is provided for location correction using feature point correspondences between images or image sources. The approach, for example, involves processing a first image and a second image to identify a feature that is visible in both the first image and the second image. The first image has an image-to-ground correspondence between the feature in the first image and a geographic location. The approach also involves transferring the image-to-ground correspondence from the first image to the second image. The approach further involves using the transferred image-to-ground correspondence to reference the feature in the second image to the geographic location.

Abstract: Systems and methods for validating drive pose refinement are provided. In some aspects, a method includes receiving image data that depicts an area of interest, and receiving a plurality of virtual points generated using the image data. The method also includes selecting at least one drive in the area of interest that captures the plurality of virtual points, and generating a refined pose track for each of the at least one drive by applying a drive alignment process to drive data from the at least one drive using the plurality virtual points. The method further includes comparing the refined pose track to a control pose track generated using control repoints, and generating, based on the comparison, a report that validates the refined pose track.

Abstract: An approach is provided for determining a ground control point from image data using machine learning. The approach, for example, involves selecting an feature based determining that the feature meets one or more properties for classification as a machine learnable feature. The approach also involves retrieving a plurality of ground truth images depicting the feature. The plurality of ground truth images is labeled with known pixel location data of the feature as respectively depicted in each of the plurality of ground truth images. The approach further involves training a machine learning model using the plurality of ground truth images to identify predicted pixel location data of the ground control point as depicted in an input image.

Abstract: An approach is provided for determining quality assessment of cross view feature correspondence using bundle adjustment techniques. The approach, for example, involves retrieving a plurality of annotated images that are labeled with one or more feature correspondence labels. The approach also involves performing a bundle adjustment process on the plurality of annotated images to compute a three-dimensional (3D) location and a residual error of a feature corresponding to the one or more feature correspondence labels. The approach further involves flagging the one or more feature correspondence labels as potentially incorrect based on determining that the residual error is greater than an error threshold.

Abstract: Systems and methods for verifying mapping information are provided. In some aspects, a method includes receiving control data acquired in an area of interest, the control data comprising a plurality of control points, and receiving mapping data associated with the area of interest, the mapping data comprising a plurality of mapping points that correspond to the plurality control points. The method also includes applying a localization algorithm to the control data to generate a control track, and applying the localization algorithm to the mapping data to generate a mapping track. The method further includes comparing the control track and the mapping track to determine a difference.

Abstract: An approach is provided for feature point detection and representation. The approach, for example, involves processing (e.g., using a neural network or equivalent) image data associated with a grid cell of an image to determine a feature point corresponding to a position of a feature detected in the image data. The approach also involves encoding the position of the feature with respect to a coordinate system referenced to the grid cell. The output comprises one or more parameters indicating the encoded position, one or more attributes of the feature, or a combination thereof.

Abstract: Systems and methods for identifying data suitable for mapping are provided. In some aspects, the method includes receiving one or more images acquired in an area of interest, and selecting at least two ground control points within a field of view of the one or more images. The method also includes determining perceived locations for the at least two ground control points using the one or more images, and computing pairwise distances between the perceived locations and predetermined locations of the at least two ground control points. The method further includes comparing corresponding pairwise distances to identify differences therebetween, and determining a suitability of the one or more images for mapping based on the comparison.

Abstract: Systems and methods for identifying data suitable for mapping are provided. In some aspects, the method includes receiving one or more images acquired in an area of interest, and selecting at least two ground control points within a field of view of the one or more images. The method also includes determining perceived locations for the at least two ground control points using the one or more images, and computing pairwise distances between the perceived locations and predetermined locations of the at least two ground control points. The method further includes comparing corresponding pairwise distances to identify differences therebetween, and determining a suitability of the one or more images for mapping based on the comparison.

Abstract: An approach is provided for generating a polyline from line segments (e.g., line segments representing objects detected by a computer vision system). The approach involves selecting a line segment from a plurality of line segments. The approach also involves determining a neighboring line segment from among the plurality of line segments. The determined neighboring line segment has a closest distance to the line segment from among the plurality of line segments. The approach further involves merging the line segment and the neighboring line segment into a polyline based on determining that the closest distance is a mutual closest distance between line segment and the neighboring line segment.