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 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: 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 selecting machine learning training observations. The approach, for example, involves providing data for presenting a user interface displaying a plurality of training images and specifying a feature to label in the plurality of training images. The feature is selected based on an image selection criterion. The approach also involves receiving a set of feature labels for the plurality of training images via the user interface based on crowd-sourced input data. The approach further involves training a machine learning based image selector to select a plurality of images, a plurality of patches of a larger image, or a combination thereof for labeling based on the set of feature labels and the plurality of training images.

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: 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 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: 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 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 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.

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 using one or more skip areas to label, train, and/or evaluate a machine learning model. The approach, for example, involves specifying the one or more skip areas with respect to an image. By way of example, a non-skip area of the image is a portion of the image that is not in the one or more skip areas. The approach also involves initiating a labeling of one or more features in the non-skip area of the image while excluding the one or more skip areas from the labeling to create a partially labeled image. The partially labeled image is then included in a training dataset for training a 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 providing quality assurance for training a feature prediction model. The approach involves training the feature prediction model to label one or more features by using a training data set comprising a plurality of data items with manually marked feature labels. The approach also involves processing the training data set using the trained feature prediction model to generate automatically marked feature labels for the plurality of data items. The approach further involves computing precision data indicating a respective precision between the manually marked feature labels and the automatically marked feature labels for each of the plurality of data items in the training data set. The approach further involves initiating a quality assurance procedure on said each of the plurality of data items based on a determination that the precision data does not satisfy a quality assurance criterion.

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 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 object detection. The approach involves receiving a feature map encoding high level features of object contours detected in an image divided into a plurality of grid cells, and further encoding start locations of each detected object contour. The approach also involves selecting a grid cell including a start location of an object contour. The approach further involves determining a precise location of the start location within the grid cell. The approach further involves determining a set of feature values from a set of proximate grid cells. The approach further involves processing the precise location and the set of feature values using a machine learning network to output a displacement vector to indicate a next coordinate of the object contour, and updating a cursor of the machine learning network based on the displacement vector.

Abstract: An approach is provided for parametric representation of lane lines. The approach involves segmenting an input image into grid cells. The approach also involves processing a portion of the input image in each grid cell to detect lane lines. The approach further involves, for each grid cell in which lane lines are detected, determining intercepts of the lane lines with edges of the grid cell, and slopes of the lane lines at the intercepts. The approach further involves generating a parametric representation of the lane lines for each grid cell. The parametric representation encodes the intercepts and slopes into a data structure for each grid cell. The approach further involves providing an output parametric representation for the input image that aggregates the parametric representations of each grid cell.

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 vertex-based evaluation of polygon similarity. The approach, for instance, involves processing, by a computer vision system, an image to generate a first set of vertices of a first polygon representing an object depicted in the image. The approach also involves for each vertex in the first set of vertices, determining a closest vertex in a second set of vertices of a second polygon, and determining a distance between said each vertex in the first set of vertices and the closest vertex in the second set of vertices. The approach further involves calculating a polygon similarity of the first polygon with respect to the second polygon based on a total of the distance determined for said each vertex in the first set of vertices normalized to a number of vertices in the first set of vertices.