Abstract: An approach is provided for determining a feature correspondence between image views. The approach, for example, involves retrieving a top down image for an area of interest and determining a ground level camera pose path for the area of interest. The approach also involves selecting a portion of the top down image that corresponds to a geographic area within a distance threshold from the ground level camera pose path, and then processing the portion of the top down image to identify a semantic feature. The approach further involves determining a subset of camera poses of the ground level pose path that is within a sphere of visibility of the semantic feature, and retrieving one or more ground level images captured with the subset of camera poses. The approach further involves determining the feature correspondence of the semantic feature between the top down image and the one or more ground level images.

Abstract: An approach is provided for determining the quality of camera pose data. The approach, for example, involves identifying camera pose data for a camera used to capture an image. The approach further involves processing the image to determine a pixel location of features visible in the image, wherein each feature has a known physical location (e.g., obtained using survey techniques or equivalent). The approach further involves determining a camera physical location of the camera based on the camera pose data. The approach further involves determining an physical location of the image plane based on the camera pose data and/or camera parameters (e.g., intrinsic camera parameters). The approach further involves projecting a ray from the camera physical location thorough the image plane physical location corresponding to the pixel location determined for each feature, and computing a minimum distance between the projected ray and the known physical location of each feature.

Abstract: An approach is provided for detecting degraded ground paint in an image. The approach, for example, involves performing semantic segmentation on the image to determine one or more pixels of the image that are classified in a ground paint category. The approach also involves generating a binary image that contains the one or more pixels of the image that are classified in the ground paint category. The approach further involves generating a hole-filled binary image by filling in the binary image to generate one or more curvilinear structures from the one or more pixels. The approach further involves determining a difference between the image and the hole-filled binary image to identify one or more degraded ground paint pixels of the image and providing the one or more degraded ground paint pixels as an output.

Abstract: An approach is provided for automatically selecting the most appropriate sensor system for high-definition map feature accuracy and reliability specifications. The approach, for example, involves selecting at least one survey point that has a known physical location. The approach also involves initiating a plurality of passes to capture a plurality of images of the at least one survey point using a sensor system. For each pass, the approach further involves calculating an estimated location of the at least one survey point based on the plurality of images and calculating error data based on the estimated location and the known location. The approach also involves generating an error curve with respect to a number of the plurality of passes based on the error data for said each pass. The approach further involves providing an output indicating a target number of passes to meet an error specification based on the error curve.

Abstract: An approach is provided for automatically recommending ground control points or any other feature points for image correction (e.g., satellite image correction). The approach, for example, involves collecting a plurality of images depicting a geographic area of interest. The approach also involves processing the plurality of images to detect one or more candidate feature points (e.g., ground control points or other features detectable in the images). The approach further involves performing a feature correspondence of the one or more candidate feature points across the plurality of images. The approach also involves triangulating respective locations of the one or more candidate feature points based on the feature correspondence. The approach further involves filtering the one or more candidate feature points based on the respective locations. The approach further involves providing the filtered one or more candidate feature points as an output comprising one or more recommended feature points.

Abstract: An approach is provided for priority ranking of satellite images. The approach, for example, involves processing a plurality of images using a feature detector to determine a set of features on each image of the plurality of images. The approach also involves determining a count of feature correspondences between each pair of images of the plurality of images based on the set of features of said each image. The approach further involves computing a ranking of the plurality of images based on the count of features correspondences between said each pair of images. The approach further involves providing the ranking of the plurality of images as an output for selecting one or more images of the plurality of images for feature creation.

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 determining a feature correspondence based on camera geometry. The approach, for example, involves determining a first labeled or detected pixel location in a first image and a second labeled or detected pixel location in a second image. The approach also involves computing a first ray from a first camera position of the first image through the first labeled or detected pixel location. The approach further involves computing a second ray from a second camera position of the second image through the second labeled or detected pixel location. The approach further involves computing a closeness value of the first ray and the second ray. The approach further involves providing an output indicating the feature correspondence between the first labeled or detected pixel location and the second labeled or detected pixel location based on determining that the closeness value is within a threshold value.

Abstract: An approach is provided for automatically selecting the most appropriate sensor system for high-definition map feature accuracy and reliability specifications. The approach, for example, involves selecting at least one survey point that has a known physical location. The approach also involves initiating a plurality of passes to capture a plurality of images of the at least one survey point using a sensor system. For each pass, the approach further involves calculating an estimated location of the at least one survey point based on the plurality of images and calculating error data based on the estimated location and the known location. The approach also involves generating an error curve with respect to a number of the plurality of passes based on the error data for said each pass. The approach further involves providing an output indicating a target number of passes to meet an error specification based on the error curve.

Abstract: Systems and methods for correcting mapping information inaccuracies are provided. 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: An approach is provided for automatically recommending ground control points or any other feature points for image correction (e.g., satellite image correction). The approach, for example, involves collecting a plurality of images depicting a geographic area of interest. The approach also involves processing the plurality of images to detect one or more candidate feature points (e.g., ground control points or other features detectable in the images). The approach further involves performing a feature correspondence of the one or more candidate feature points across the plurality of images. The approach also involves triangulating respective locations of the one or more candidate feature points based on the feature correspondence. The approach further involves filtering the one or more candidate feature points based on the respective locations. The approach further involves providing the filtered one or more candidate feature points as an output comprising one or more recommended feature points.

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 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: An approach is provided for priority ranking of satellite images. The approach, for example, involves processing a plurality of images using a feature detector to determine a set of features on each image of the plurality of images. The approach also involves determining a count of feature correspondences between each pair of images of the plurality of images based on the set of features of said each image. The approach further involves computing a ranking of the plurality of images based on the count of features correspondences between said each pair of images. The approach further involves providing the ranking of the plurality of images as an output for selecting one or more images of the plurality of images for feature creation.

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: 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 determining the quality of camera pose data. The approach, for example, involves identifying camera pose data for a camera used to capture an image. The approach further involves processing the image to determine a pixel location of features visible in the image, wherein each feature has a known physical location (e.g., obtained using survey techniques or equivalent). The approach further involves determining a camera physical location of the camera based on the camera pose data. The approach further involves determining an physical location of the image plane based on the camera pose data and/or camera parameters (e.g., intrinsic camera parameters). The approach further involves projecting a ray from the camera physical location thorough the image plane physical location corresponding to the pixel location determined for each feature, and computing a minimum distance between the projected ray and the known physical location of each feature.

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 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: 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.