A system for an agricultural operation includes a first vehicle having an object sensor configured to capture data associated with one or more objects within the field and a location sensor configured to capture data associated with a location of each of the one or more objects. A computing system is communicatively coupled with the object sensor and the location sensor. The computing system is configured to identify at least one of the one or more objects as a weed, classify each of the identified weeds in a first set of weeds or a second set of weeds, and generate a weed map based on the classification of each of the first set of weeds and the second set of weeds.

A mapping system for multiview video displays presents an interactive map comprising a plurality of regularly distributed grid points indicating availability of multiview display data at those grid points. When selected, each of the grid points present a plurality of videos, images, and other data collected that are available for display.

A method for processing map data includes: determining a first region for which a map display image is to be drawn; determining multiple first reference points within the first region according to a second region corresponding to the first region; determining relative height data of the first region according to positions and real height data of all the first reference points; and determining a map display image of a position to be displayed according to the relative height data, in which the position to be displayed is within the first region.

Systems and methods to perform remote monitoring on a vehicle are described. One embodiment determines a state of a vehicle, where data associated with the vehicle is collected and logged. The data is transmitted to a data server. The data is processed, and vehicle information is extracted from the data. A state of the vehicle is determined based on the vehicle information.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

An approach is provided for maintaining map accuracy considering tectonic plate movements. The approach, for example, involves processing data to determine a tectonic plate on which a map feature of a geographic database is located. A stored position of the map feature is associated with a time epoch assigned to the stored position. The approach also involves determining a velocity of a movement of the tectonic plate. The approach further involves monitoring an estimated position of the map feature over time based on the stored position and the velocity of the movement of the tectonic plate. The approach further involves automatically flagging or updating the stored position of the map feature based on determining that the estimated position differs from the stored position by more than a distance threshold.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

This specification describes systems and methods for generating a mapping of a physical space from point cloud data for the physical space. The methods can include receiving the point cloud data for the physical space, filtering the point cloud data to, at least, remove sparse points from the point cloud data, aligning the point cloud data along x, y, and z dimensions that correspond to an orientation of the physical space, and classifying the points in the point cloud data as corresponding to one or more types of physical surfaces. The methods can also include identifying specific physical structures in the physical space based, at least in part, on classifications for the points in the point cloud data, and generating the mapping of the physical space to identify the specific physical structures and corresponding contours for the specific physical structures within the orientation of the physical space.

An agricultural vehicle having a chassis, wheels, a power unit, and a header. The header has a center section, at least one wing section extending laterally from the center section, and a wing section support. The center section is movable relative to the chassis, the wing section is movable relative to the center section, and the wing support is movable relative to the wing section. The combine has a control system that is configured to determine that the combine is configured to drive at a road-driving speed, and in response to such determination: operate a center section actuator to move the center section to a raised center section position, operate a wing section actuator to move the wing section to a raised wing section position, and operate a wing support actuator to move the wing support to a raised wing support position.

Methods and systems for determining information about an area that includes a polygon for controlling navigation of an autonomous vehicle are disclosed. The methods include defining a bounding box that encloses the area, and generating a KD-tree from the bounding box that partitions the polygon into a plurality of leaf nodes that each include at least some of a plurality of edges of the polygon. The methods also include assigning a reference point to each leaf node, creating a data representation of the area that comprises the KD-tree, and adding the data representation to map data comprising the area. A reference point is associated with a location within that leaf node, and information relating to whether the reference point lies outside or inside the at least one polygon.