Abstract: In various examples, a method to manage map data includes storing a map of a geographic area using an immutable tree. The immutable tree comprises a plurality of nodes stored using a distributed hash table. The plurality of nodes include a plurality of map tiles. At least two map tiles of the plurality of map tiles cover different geographic subregions of the geographic area of the map. The method includes hosting one or more binary large objects (BLOBs) that correspond to the plurality of map tiles in an origin data plane. The method includes making the one or more BLOBs available for distribution to one or more client devices using a content delivery network (CDN).

Abstract: An approach is provided for selecting training observations for machine learning models. The approach involves determining a first distribution of a plurality of features observed in the training data set, and a second distribution of the plurality of features observed in the candidate pool of observations. The approach further involves selecting one or more observations in the candidate pool of observations for annotation based on the first distribution and the second distribution. The approach further involves adding the one or more observations to the training data set after annotation. The training data set is used for training the machine learning model.

Abstract: An approach is provided for dynamic adaptation of an in-vehicle feature detector. The approach involves embedding a feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof. The approach also involves deploying the feature detection model, the precomputed weights, or a combination thereof to adapt an in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof. The in-vehicle feature detector can then use the feature detection model, the precomputed weights, or a combination thereof to process sensor data collected while in the geographic area to detect one or more features.

Abstract: An approach is provided for selecting training observations for machine learning models. The approach involves determining a first distribution of a plurality of features observed in the training data set, and a second distribution of the plurality of features observed in the candidate pool of observations. The approach further involves selecting one or more observations in the candidate pool of observations for annotation based on the first distribution and the second distribution. The approach further involves adding the one or more observations to the training data set after annotation. The training data set is used for training the machine learning model.

Abstract: An approach is provided for dynamic adaptation of an in-vehicle feature detector. The approach involves embedding a feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof. The approach also involves deploying the feature detection model, the precomputed weights, or a combination thereof to adapt an in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof. The in-vehicle feature detector can then use the feature detection model, the precomputed weights, or a combination thereof to process sensor data collected while in the geographic area to detect one or more features.

Abstract: Embodiments herein provide for color conversion in production printing systems. The color conversion system includes a processor operable to receive a print job and convert the print job into a full sheetside bitmap comprised of an array of color pixels. The full sheetside bitmap is destined for color conversion via an ink limitation algorithm. The color conversion system also includes an image identification module operable to identify a portion of the print job for a level of color accuracy and to designate that portion of the print job for color conversion via a color accuracy algorithm. The processor is operable to convert a color value of the identified portion of the print job using the color accuracy algorithm. The processor also converts color values of the remainder of the print job using the ink limitation algorithm.

Abstract: An International Color Consortium (ICC) profile making tool includes a regression module to establish a dependency between a profile connection space (PCS) and a device color space, an optimization module to generate a first output profile based on an ink minimization mode and a second output profile based on a high accuracy mode and a smoothing module to prevent fluctuating transitions between colors occurring in the first output profile.