Abstract: Drone space is defined according to a building model and a buffer space. At least one three-dimensional geometry is identified from the building model. The buffer space is calculated from the three-dimensional geometry. Coordinates for a drone air space are defined based on the buffer space. At least one path segment may be identified based on the coordinates for the drone air space, and the coordinates for drone air space are stored in a map database in association with the at least one path segment.

Abstract: An approach involves receiving pixel-level labeling data for an image depicting at least a portion of a building facade. The pixel-level labeling data labels each of a plurality of pixels of the image as either window pixels or non-window pixels. The approach further involves generating a window pattern based on window size data, window spacing data, or a combination thereof extracted from the pixel-level labeling data. The approach further involves computing a confidence score for the window pattern based on at least one observed value of at least one characteristic of the window pattern or a deviation of the observed value from at least one expected value, and generating an abstract texture based on the window pattern, the confidence score, and/or other probabilistic metrics.

Abstract: An approach is provided for identifying objects present in mesh representation of a geo-location, generating accurate 3D models for the objects, and aligning the 3D models to their corresponding objects in an application. The approach comprises processing and/or facilitating a processing of textured three-dimensional mesh data in one or more regions of interest to cause, at least in part, a generation of at least one two-dimensional depth image representation. The approach further comprises causing, at least in part, a filtering of the textured three-dimensional mesh data in the one or more regions of interest to remove mesh data below at least one threshold height based, at least in part, on the at least one two-dimensional depth image representation.

Abstract: An approach is provided for generating a cleaned object model to represent an object in a mapping database. The approach includes receiving point cloud data depicting the object. The approach also includes processing the point cloud data to determine one or more surface points of the point cloud data. The one or more surface points represent one or more surfaces of the object. The approach further includes cutting a model of the object into one or more fragments using the one or more surface points. The one or more fragments include one or more object fragments and one or more non-object fragments. The approach further includes designating the one or more object fragments as the cleaned object model to represent the object in the mapping database.

Abstract: An approach is provided for generating an abstract texture for a building facade or model. The approach, for example, involves receiving pixel-level labeling data for an image depicting at least a portion of a building facade. The pixel-level labeling data labels each of a plurality of pixels of the image as either window pixels or non-window pixels. The approach further involves generating a window pattern based on window size data, window spacing data, or a combination thereof extracted from the pixel-level labeling data. The approach further involves computing a confidence score for the window pattern based on at least one observed value of at least one characteristic of the window pattern or a deviation of the observed value from at least one expected value, and generating an abstract texture based on the window pattern, the confidence score, and/or other probabilistic metrics.

Abstract: A method comprises obtaining data points, each comprised of a time stamp and measurement; dividing the data points into sequences of consecutive data points; limiting the maximum time between consecutive data points in the same sequence; limiting the maximum time between the earliest and latest data points in each sequence; calculating a polynomial of lowest transmission cost for each sequence; limiting the approximation error between the data points in a sequence and the associated polynomial; and transmitting, to a server, data based on the calculated polynomial.

Abstract: Drone space is defined according to a building model and a buffer space. At least one three-dimensional geometry is identified from the building model. The buffer space is calculated from the three-dimensional geometry. Coordinates for a drone air space are defined based on the buffer space. At least one path segment may be identified based on the coordinates for the drone air space, and the coordinates for drone air space are stored in a map database in association with the at least one path segment.

Abstract: An approach is provided for generating a cleaned object model to represent an object in a mapping database. The approach includes receiving point cloud data depicting the object. The approach also includes processing the point cloud data to determine one or more surface points of the point cloud data. The one or more surface points represent one or more surfaces of the object. The approach further includes cutting a model of the object into one or more fragments using the one or more surface points. The one or more fragments include one or more object fragments and one or more non-object fragments. The approach further includes designating the one or more object fragments as the cleaned object model to represent the object in the mapping database.

Abstract: Drone space is defined according to a building model and a buffer space. At least one three-dimensional geometry is identified from the building model. The buffer space is calculated from the three-dimensional geometry. Coordinates for a drone air space are defined based on the buffer space. At least one path segment may be identified based on the coordinates for the drone air space, and the coordinates for drone air space are stored in a map database in association with the at least one path segment.

Abstract: A method comprising receiving probe data indicative of a set of navigational signal measurements that is matched to a link segment, determining a set of measurement errors such that each measurement error of the set of measurement errors is a difference between a location indicated by the link segment and a location indicated by a navigational signal measurement of the set of navigational signal measurements, determining at least one statistical attribute of the set of measurement errors, and storing an indication of the statistical attribute in map information associated with the link segment is disclosed.

Abstract: An approach is provided for determining new point(s)-of-interest based, at least in part, on mobile device positioning, three dimensional location data, or a combination thereof. The approach involves processing and/or facilitating a processing of location information associated with a plurality of devices to determine one or more location points at which there are one or more concentrations of the plurality of devices. The approach also involves causing, at least in part, an accessing of three-dimensional data representing the one or more location points. The approach further involves processing and/or facilitating a processing of the three-dimensional data to determine one or more features that are indicative of a presence of one or more points of interest. The approach also involves determining one or more candidate points of interest based, at least in part, on the one or more features.

Abstract: An approach is provided for classifying objects that are present at a geo-location and providing an uncluttered presentation of images of some of the objects in an application such as a map application. The approach includes determining one or more regions of interest associated with at least one geo-location, wherein the one or more regions of interest are at least one textured three-dimensional representation of one or more objects that may be present at the at least one geo-location. The approach also includes processing and/or facilitating a processing of the at least one textured three-dimensional representation to determine at least one two-dimensional footprint and three-dimensional geometry information for the one or more objects.

Abstract: An approach is provided for identifying objects present in mesh representation of a geo-location, generating accurate 3D models for the objects, and aligning the 3D models to their corresponding objects in an application. The approach comprises processing and/or facilitating a processing of textured three-dimensional mesh data in one or more regions of interest to cause, at least in part, a generation of at least one two-dimensional depth image representation. The approach further comprises causing, at least in part, a filtering of the textured three-dimensional mesh data in the one or more regions of interest to remove mesh data below at least one threshold height based, at least in part, on the at least one two-dimensional depth image representation.

Abstract: A method comprising receiving probe data indicative of a set of navigational signal measurements that is matched to a link segment, determining a set of measurement errors such that each measurement error of the set of measurement errors is a difference between a location indicated by the link segment and a location indicated by a navigational signal measurement of the set of navigational signal measurements, determining at least one statistical attribute of the set of measurement errors, and storing an indication of the statistical attribute in map information associated with the link segment is disclosed.

Abstract: Drone space is defined according to a building model and a buffer space. At least one three-dimensional geometry is identified from the building model. The buffer space is calculated from the three-dimensional geometry. Coordinates for a drone air space are defined based on the buffer space. At least one path segment may be identified based on the coordinates for the drone air space, and the coordinates for drone air space are stored in a map database in association with the at least one path segment.

Abstract: An approach is provided for classifying objects that are present at a geo-location and providing an uncluttered presentation of images of some of the objects in an application such as a map application. The approach includes determining one or more regions of interest associated with at least one geo-location, wherein the one or more regions of interest are at least one textured three-dimensional representation of one or more objects that may be present at the at least one geo-location. The approach also includes processing and/or facilitating a processing of the at least one textured three-dimensional representation to determine at least one two-dimensional footprint and three-dimensional geometry information for the one or more objects.

Abstract: An approach is provided for determining new point(s)-of-interest based, at least in part, on mobile device positioning, three dimensional location data, or a combination thereof. The approach involves processing and/or facilitating a processing of location information associated with a plurality of devices to determine one or more location points at which there are one or more concentrations of the plurality of devices. The approach also involves causing, at least in part, an accessing of three-dimensional data representing the one or more location points. The approach further involves processing and/or facilitating a processing of the three-dimensional data to determine one or more features that are indicative of a presence of one or more points of interest. The approach also involves determining one or more candidate points of interest based, at least in part, on the one or more features.

Abstract: A method comprises obtaining data points, each comprised of a time stamp and measurement; dividing the data points into sequences of consecutive data points; limiting the maximum time between consecutive data points in the same sequence; limiting the maximum time between the earliest and latest data points in each sequence; calculating a polynomial of lowest transmission cost for each sequence; limiting the approximation error between the data points in a sequence and the associated polynomial; and transmitting, to a server, data based on the calculated polynomial.

Abstract: A computer model of a physical structure (or object) can be generated using context-based hypothesis testing. For a set of point data, a user selects a context specifying a geometric category corresponding to the structure shape. The user specifies at least one seed point from the set that lies on a surface of the structure of interest. Using the context and point data, the system loads points in a region near the seed point(s), and determines the dimensions and orientation of an initial surface component in the context that corresponds to those points. If the selected component is supported by the points, that component can be added to a computer model of the surface. The system can repeatedly find points near a possible extension of the surface model, using the context and current surface component(s) to generate hypotheses for extending the surface model to these points.