Techniques described herein may be used to identify anomalies in a joined region of two datasets. For example, a computer system may determine an elevation anomaly data point in based on an elevation criterion. The computer system may determine a set of buffer points surrounding the elevation anomaly data points. The computer system may user the set of buffer points to reduce the number of data points for adjusting. The computer system may determine a reduction factor based on the buffer points. The computer system may then apply the reduction factor to points in the buffer points and other points.

An apparatus for artificial intelligence (AI) bathymetry is disclosed. The apparatus includes a sonic unit attached to a boat, the sonic unit configured to generate a plurality of metric data as a function of a plurality of ultrasonic pulses and a plurality of return pulses. An image processing module is configured to generate a bathymetric image as a function of the plurality of metric data, identify, as a function of the bathymetric image, an underwater landmark, and register the bathymetric image to a map location as a function of the underwater landmark. A communication module is configured to transmit the registered bathymetric image to at least a remote device. An autonomous navigation module is configured to determine a heading for the boat as a function of a path datum and command boat control to navigate the boat as a function of the heading.

A field shape identification unit in a server control unit identifies, on the basis of position information about a work vehicle, the shape of a work field through which the work vehicle has traveled while working. A degree-of-overlap determination unit in the server control unit compares the shape of the work field identified by the field shape identification unit to the shape of a reference field and determines whether the degree of overlap between the work field and the reference field is greater than or equal to a prescribed degree. When the degree-of-overlap determination unit determines that the degree of overlap between the work field and the reference field is equal to or greater than a prescribed degree, an information management unit associates work-related information pertaining to the work in the work field with the reference field and manages the same.

A system and method of navigating a vehicle, the vehicle comprising a scanning device and a self-contained navigation system (SCNS) operatively connected to a computer, the method comprising: operating the scanning device for repeatedly executing a scanning operation, each operation includes scanning an area surrounding the vehicle, thereby generating respective scanning output data; operating the computer for generating, based on the scanning output data, a relative map representing at least a part of the area, the map having known dimensions and being relative to a position of the vehicle, wherein the map comprises cells, each cell classified to a class from at least two classes, comprising traversable and non-traversable, and characterized by dimensions equal or larger than an accumulated drift value of the SCNS; wherein non-traversable cells correspond to identified obstacles; receiving SCNS data and updating a position of the vehicle relative to the cells based on the SCNS data.

A map information system includes: an in-vehicle device that executes automated driving control of a vehicle; and an external device having external map information used for the automated driving control. The in-vehicle device includes: a memory device in which map information is stored; and a control device configured to execute the automated driving control based on the map information stored in the memory device. The control device is further configured to: determine whether or not a takeover occurs during the automated driving control; set an upload target area including the takeover occurrence position, in a case where the takeover occurs during the automated driving control; and upload the map information regarding the upload target area to the external device. The external device updates the external map information based on the map information uploaded from the in-vehicle device.

In order to achieve a more effective application of a crop efficiency product, a computer-implemented method is provided for applying a crop efficiency product to at least one crop in a field. The method comprises the steps of collecting remotely-sensed data of the field before an application of the crop efficiency product in the field, determining, based on the collected remotely-sensed data, at least one soil parameter at a plurality of locations in the field, generating, for each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter and a prediction model, wherein the prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter and associated yield responses for the at least one crop under the application of the crop efficiency product, deciding, for each of the plurality of locations in the field, whether to treat or not based on the predicted yield response, and outputting information indicative of the decision useable to activate at least one treatment device to comply with the decision.

This disclosure is generally directed to generating travel route information that is interpretable by a delivery robot for traversing a last mile of a delivery. In an example embodiment, an individual captures a video clip while moving along a travel route that is preferred by the individual for use by the delivery robot. The video clip is converted into a digital route map that the delivery robot uses to reach a package drop-off location on the property. In another example embodiment, an individual captures an image of a portion of the property and appends oral instructions to reach the package drop-off location. The image and the oral instructions are converted into a digital route map for use by the delivery robot. In yet another example embodiment, markers affixed to a surface of a traversable area on the property are used by the delivery robot to reach the package drop-off location.

A method of generating a scenic rating for segments of an electronic map involves obtaining probe data relating to the movement of a plurality of devices with respect to time in the area, and, for each one of a plurality of segments of the electronic map; identifying a set of positional data relating to the movement of devices along the navigable element represented by the segment, filtering the identified set of positional data relating to the movement of devices along the navigable element represented by the segment based on mode of transport to obtain one or more subset of the identified positional data relating to the movement of devices along the element represented by the segment which may be expected to relate to traversals of the navigable element for recreational purposes, using the or each obtained subset of the positional data to obtain one or more scenicity parameter which may be used in determining a scenic rating for the segment indicative of a scenicity of the navigable element represented by the segment, and using the one or more obtained scenicity parameter to determine a scenic rating for the segment.

Provided is a guide display device with which incorrect recognition of a place having small features as a ground surface having no features can be suppressed in generating and updating a three-dimensional map. A data processing unit (70): detects an edge (Eg) of a feature (E) on the basis of a result of subjecting image data captured by a camera (61) to image processing, and recognizes the feature (E) on the basis of the edge (Eg); and accumulates, from a predetermined number of frames from before the time at which the image data was captured, point group data (P) of a nearby range (Ma) of the edge (Eg) of the feature (E) that was recognized as a ground surface (F), and generates, on the basis of the accumulated point group data (P), a three-dimensional map (M) of the feature (E) the edge (Eg) of which has been recognized.

A soil modeling and mapping framework for use in precision agriculture analyzes remotely-sensed data pertaining to characteristics of one or more agricultural fields, and determines optimal sampling locations from information in remotely-sensed information, terrain derivatives and satellite imagery, to develop a customized sampling design for modeling soil properties in such agricultural fields that optimized for the particular landscape in such fields. The soil modeling and mapping framework then analyzes soil samples collected based on the customized sampling design in machine learning-based models that predict soil properties in sampled, semi-sampled, and unsampled target fields. The predicted soil properties are used to develop highly-accurate maps of soil properties such as fertility maps, which may further be used for defining and creating one or more management zones with recommendations for applying the right amount of nutrients at variable rates in the correct areas.