Abstract: In one embodiment, a method includes receiving an image associated with an object in an environment, the image being captured by sensors associated with a vehicle, generating a feature representation of the image, determining a potential ground control point associated with the object based on the feature representation of the image, determining a predetermined location reading based on the potential ground control point, calculating a differential relative to the predetermined location reading based on the potential ground control point, and determining a location of the vehicle based on the differential and the predetermined location reading based on the potential ground control point.

Abstract: The disclosed computer-implemented method may include (i) identifying, by a server computer system, a provider computing device for use in capturing street-level image data, where the provider computing device controls a camera positioned to capture street-level imagery outside the vehicle, (ii) determining, by the server computer system, a configuration that controls use of the provider computing device to provide street-level image data captured by the camera to the server computer system, (iii) sending, by the server computer system, the configuration to the computing device, and (iv) receiving, from the computing device, street-level image data captured by the computing device using the camera responsive to the configuration. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include (i) identifying, by a server computer system, a provider computing device for use in capturing street-level image data, where the provider computing device controls a camera positioned to capture street-level imagery outside the vehicle, (ii) determining, by the server computer system, a configuration that controls use of the provider computing device to provide street-level image data captured by the camera to the server computer system, (iii) sending, by the server computer system, the configuration to the computing device, and (iv) receiving, from the computing device, street-level image data captured by the computing device using the camera responsive to the configuration. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A disclosed method may include receiving geographic coordinates of a location at which two parties are to rendezvous, generating a human-understandable geospatial descriptor for the request location, and sending the descriptor to respective devices of the two parties for presentation to the two parties. Generating the human-understandable geospatial descriptor may include identifying a human-visible feature in the vicinity of the request location that is labeled within available map data, selecting, based on a descriptor generation model, a reference expression relative to the identified feature, and applying a grammar-based constructor to the label and the selected reference expression to form the human-understandable geospatial descriptor. The model may be tuned using machine learning. The two parties may include a ride requestor and a ride provider in a ridesharing service. The identified feature may be a point of interest, landmark, street name, intersection, marker, or structure.

Abstract: In one embodiment, a method includes receiving an image associated with an object in an environment, the image being captured by sensors associated with a vehicle, generating a feature representation of the image, determining a potential ground control point associated with the object based on the feature representation of the image, determining a predetermined location reading based on the potential ground control point, calculating a differential relative to the predetermined location reading based on the potential ground control point, and determining a location of the vehicle based on the differential and the predetermined location reading based on the potential ground control point.

Abstract: The disclosed computer-implemented method may include (i) identifying, by a server computer system, a provider computing device for use in capturing street-level image data, where the provider computing device controls a camera positioned to capture street-level imagery outside the vehicle, (ii) determining, by the server computer system, a configuration that controls use of the provider computing device to provide street-level image data captured by the camera to the server computer system, (iii) sending, by the server computer system, the configuration to the computing device, and (iv) receiving, from the computing device, street-level image data captured by the computing device using the camera responsive to the configuration. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In one embodiment, a method includes training an image localization model for determining an adjusted location reading for a vehicle based on its location reading and the associated image. The method further includes updating a location associated with the vehicle using the adjusted location reading. The training of the image localization model includes generating feature representations of a number of training images, and learning a relationship between the feature representations and differentials between a number of raw location readings and their respective reference location readings.

Abstract: The disclosed computer-implemented method may include (i) identifying, by a server computer system, a provider computing device for use in capturing street-level image data, where the provider computing device controls a camera positioned to capture street-level imagery outside the vehicle, (ii) determining, by the server computer system, a configuration that controls use of the provider computing device to provide street-level image data captured by the camera to the server computer system, (iii) sending, by the server computer system, the configuration to the computing device, and (iv) receiving, from the computing device, street-level image data captured by the computing device using the camera responsive to the configuration. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In one embodiment, one or more computer systems of a social-networking system retrieve a user profile for a user associated with a media device. The one or more computer systems of a social-networking system receive media consumption. The one or more computer systems of a social-networking system correlates the user profile and the media consumption data to determine device-based media consumption data associated with content being consumed on the media device. The one or more computer systems of a social-networking system store data that associates the user profile with the device-based media consumption data.

Abstract: The disclosed computer-implemented method may include (i) identifying, by a server computer system, a provider computing device for use in capturing street-level image data, where the provider computing device controls a camera positioned to capture street-level imagery outside the vehicle, (ii) determining, by the server computer system, a configuration that controls use of the provider computing device to provide street-level image data captured by the camera to the server computer system, (iii) sending, by the server computer system, the configuration to the computing device, and (iv) receiving, from the computing device, street-level image data captured by the computing device using the camera responsive to the configuration. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In one embodiment, a method includes training an image localization model for determining an adjusted location reading for a vehicle based on its location reading and the associated image. The method further includes updating a location associated with the vehicle using the adjusted location reading. The training of the image localization model includes generating feature representations of a number of training images, and learning a relationship between the feature representations and differentials between a number of raw location readings and their respective reference location readings.

Abstract: The disclosed computer-implemented method may include (i) identifying, by a server computer system, a provider computing device for use in capturing street-level image data, where the provider computing device controls a camera positioned to capture street-level imagery outside the vehicle, (ii) determining, by the server computer system, a configuration that controls use of the provider computing device to provide street-level image data captured by the camera to the server computer system, (iii) sending, by the server computer system, the configuration to the computing device, and (iv) receiving, from the computing device, street-level image data captured by the computing device using the camera responsive to the configuration. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In one embodiment, one or more computer systems of a social-networking system retrieve a user profile for a user associated with a media device. The one or more computer systems of a social-networking system receive media consumption. The one or more computer systems of a social-networking system correlates the user profile and the media consumption data to determine device-based media consumption data associated with content being consumed on the media device. The one or more computer systems of a social-networking system store data that associates the user profile with the device-based media consumption data.

Abstract: A system and method for ranking additional videos based on experiment data. The system includes an additional video request retrieval unit to receive a request for a plurality of additional videos, and a number of the plurality of additional videos based on a video being served; an additional video database to retrieve the plurality of additional videos based on the video, and to retrieve a relevance ranking for each of the plurality of additional videos, a monetization ranking for each of the plurality of additional videos, and a likelihood ranking for each of the plurality of additional videos, the likelihood ranking being sourced from the experimental data; a weighting/ranking unit to assign a score for each of the plurality of videos based on the retrieved rankings from the additional video database; and an additional video selection unit to select the number of the plurality of videos based the score.