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 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 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.

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 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 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.

Abstract: An approach is provided for estimating a vanishing point or horizon in an image depicting one or more lanes of a roadway. The approach involves processing the image to construct one or more lane models of the one or more road lanes depicted in the image. The approach also involves extending the one or more road lanes through the image using the one or more lane models. The approach further involves determining a horizontal line in the image at which a maximum number of the one or more extended road lanes crosses over a minimum horizontal extent of the horizontal line. The approach further involves designating the horizontal line as the horizon of the image.

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.