Abstract: A direct current circuit breaker handcart and a direct current switch apparatus are provided. In the direct current circuit breaker handcart, the current carrying element is located on the support surface, and includes a main circuit breaker and an auxiliary circuit breaker electrically connected with each other; the commutation element is located on the support surface, is electrically connected with the current carrying element, and includes a commutation capacitor, a commutation inductor, a commutation switch assembly, and a commutation capacitor charger electrically connected with each other; the repulsive assembly is configured to control opening and breaking off of the main circuit breaker; the energy absorbing element is located on the support surface, and is electrically connected with the current carrying element and the commutation element, respectively.

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: 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 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: A method, apparatus and computer program product are provided for establishing correspondences between images using through generation of a translation between the different perspectives. Methods may include: receiving first sensor data from a first image sensor, where the first sensor data includes a first image of an environment captured from a first perspective; receiving second sensor data from a second image sensor of a second image of the environment captured from a second perspective; identifying image correspondence points between the first sensor data and the second sensor data; computing pairwise vectors between corresponding pairs of first projected points of the first sensor data and second projected points of the second sensor data; clustering the pairwise vectors according to magnitude and orientation; and generating a translation vector from clusters of pairwise vectors, where the translation vector represents a shift of the second image sensor data to correspond to ground truth data.

Abstract: A method, apparatus and computer program product are provided for identifying potential correspondence between image points and using clustering and filtering to generate consistent three-dimensional points of correspondence between two-dimensional images.

Abstract: The disclosure provides methods, apparatus, and products for attention guidance and labeling. In one aspect, a method comprises receiving metadata associated with a perspective image captured by an image capture device located at the image position and having the image pose, wherein the metadata comprises the image position and the image pose. The method comprises defining a field-of-view indicator having a first indicator position located at the image position and having the image pose; receiving ground control point (GCP) information identifying a GCP, wherein the GCP information comprises a GCP position; defining a GCP indicator having a second indicator position located at the GCP position; and causing display of an indicator layer comprising the field-of-view indicator and the GCP indicator in a second portion of an interactive user interface (IUI) of a labeling tool, wherein at least the perspective image is displayed in a first portion of the IUI.

Abstract: A method includes obtaining an object comprising a plurality of images, each image comprising at least one of: one or more tie points or one or more stray points. The method also includes determining an importance score of each tie point of the one or more tie points in the images. The method also includes determining an importance score of each stray point of the one or more stray points in the images. The method also includes determining an importance score of the object based on the importance score of the one or more tie points and the importance score of the one or more stray points. The method also includes providing the importance score of the object as an output for selection of the object for image labeling.

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: The disclosure provides methods, apparatus, and products for attention guidance and labeling. In one aspect, a method comprises receiving metadata associated with a first perspective image captured by a first image capture device located at a first image position and having a first image pose, wherein the metadata comprises the first image position and first image pose; defining a first field-of-view indicator; receiving metadata associated with a second perspective image captured by a second image capture device located at a second image position and having a second image pose, wherein the metadata comprises the second image position and second image pose; defining a second field-of-view indicator; and causing display of an indicator layer comprising the first and second field-of-view indicators in a second portion of an IUI of a labeling tool, wherein at least the first or second perspective image is displayed in a first portion of the IUI.

Abstract: The disclosure provides methods, apparatus, and products for attention guidance and labeling. In one aspect, a method comprises receiving metadata associated with a perspective image captured by an image capture device located at the image position and having the image pose, wherein the metadata comprises the image position and the image pose. The method comprises defining a field-of-view indicator having a first indicator position located at the image position and having the image pose; receiving ground control point (GCP) information identifying a GCP, wherein the GCP information comprises a GCP position; defining a GCP indicator having a second indicator position located at the GCP position; and causing display of an indicator layer comprising the field-of-view indicator and the GCP indicator in a second portion of an interactive user interface (IUI) of a labeling tool, wherein at least the perspective image is displayed in a first portion of the IUI.

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 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 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 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 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 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.