A method for situation awareness is provided. The method comprises: preparing a neural network trained by a learning set, wherein the learning set includes a plurality of maritime images and maritime information including object type information which includes a first type index for a vessel, a second type index for a water surface and a third type index for a ground surface, and distance level information which includes a first level index indicating that a distance is undefined, a second level index indicating a first distance range and a third level index indicating a second distance range greater than the first distance range; obtaining a target maritime image generated from a camera; and determining a distance of a target vessel based on the distance level index of the maritime information being outputted from the neural network which receives the target maritime image and having the first type index.

A hydrocarbon exploration system includes a plurality of computer nodes, each comprising a processor and memory coupled to the processor. The computer nodes are configured to implement a well identification system and a well location system. The well identification system is configured to analyze features of an image at a given set of geographic coordinates, and to determine, based on the features, whether a well is present at the geographic coordinates. The well location system is configured to, responsive to the well identification system determining that a well is not present at the geographic coordinates, render, at the geographic coordinates, a first indication of a potential incorrect location of the well in the image, and to render a second indication of a potential correct location of the well in the image.

A method to correct a digital image to reverse the effect of signal diffusion among pixels of the digital image. For a target pixel j of the digital image, a set of signal values and a set of signal amplitudes are received, each corresponding to a set of kernel pixels i surrounding and including the target pixel j. For each kernel pixel i, a weighting coefficient is computed based on the signal amplitude of that kernel pixel i and on the signal amplitude of the target pixel j. A linear combination of signal values corresponding to the set of kernel pixels i is computed, wherein the signal value for each pixel i is weighted by the weighting coefficient corresponding to that pixel i. The linear combination is stored in volatile memory of an electronic device as a corrected signal value for the target pixel j.

A method to correct a digital image to reverse the effect of signal diffusion among pixels of the digital image. For a target pixel j of the digital image, a set of signal values and a set of signal amplitudes are received, each corresponding to a set of kernel pixels i surrounding and including the target pixel j. For each kernel pixel i, a weighting coefficient is computed based on the signal amplitude of that kernel pixel i and on the signal amplitude of the target pixel j. A linear combination of signal values corresponding to the set of kernel pixels i is computed, wherein the signal value for each pixel i is weighted by the weighting coefficient corresponding to that pixel i. The linear combination is stored in volatile memory of an electronic device as a corrected signal value for the target pixel j.

Automated methods and systems are disclosed, including a method comprising: obtaining a first three-dimensional-data point cloud of a horizontal surface of an object of interest, the first three-dimensional-data point cloud having a first resolution and having a three-dimensional location associated with each point in the first three-dimensional-data point cloud; capturing one or more aerial image, at one or more oblique angle, depicting at least a vertical surface of the object of interest; analyzing the one or more aerial image with a computer system to determine three-dimensional locations of additional points on the object of interest; and updating the first three-dimensional-data point cloud with the three-dimensional locations of the additional points on the object of interest to create a second three-dimensional-data point cloud having a second resolution greater than the first resolution of the first three-dimensional-data point cloud.

Automated methods and systems are disclosed, including a method comprising: obtaining a first three-dimensional-data point cloud of a horizontal surface of an object of interest, the first three-dimensional-data point cloud having a first resolution and having a three-dimensional location associated with each point in the first three-dimensional-data point cloud; capturing one or more aerial image, at one or more oblique angle, depicting at least a vertical surface of the object of interest; analyzing the one or more aerial image with a computer system to determine three-dimensional locations of additional points on the object of interest; and updating the first three-dimensional-data point cloud with the three-dimensional locations of the additional points on the object of interest to create a second three-dimensional-data point cloud having a second resolution greater than the first resolution of the first three-dimensional-data point cloud.

A computer implemented method of creating a simulated realistic virtual model of a geographical area for training an autonomous driving system, comprising obtaining geographic map data of a geographical area, obtaining visual imagery data of the geographical area, classifying static objects identified in the visual imagery data to corresponding labels to designate labeled objects, superimposing the labeled objects over the geographic map data, generating a virtual 3D realistic model emulating the geographical area by synthesizing a corresponding visual texture for each of the labeled objects and injecting synthetic 3D imaging feed of the realistic model to imaging sensor(s) input(s) of the autonomous driving system controlling movement of an emulated vehicle in the realistic model where the synthetic 3D imaging feed is generated to depict the realistic model from a point of view of emulated imaging sensor(s) mounted on the emulated vehicle.

An approach is provided for determining a confidence level for map feature identification in a map application. The approach involves, for instance, presenting an image of a map feature in a user interface of a device. The image is initially presented with a content of the image obscured from view. The approach also involves progressively un-obscuring the image in the user interface until an input identifying the map feature is received from a user via the user interface. The approach further involves determining a percentage of the image that is visible at a time the input is received from the user. The approach further involves personalizing an application to the user based on the feature identification confidence level.

A method for an agricultural operation can include presenting a captured image of a field on a display of an electronic device. The image can be captured by an imager operably coupled with the electronic device. The method can further include detecting a geographic position of the electronic device, an imager direction of the imager, and a tilt orientation of the electronic device. The method also can include determining a field of view based on the geographic position, the tilt orientation, and the imager direction. Further, the method can include identifying whether one or more portions of the field within the field of view is a processed segment of the field or an unprocessed segment of the field. Lastly, the method can include visually augmenting the captured image with graphics based at least in part on the identification of the one or more portions of the field.

A method for an agricultural operation can include presenting a captured image of a field on a display of an electronic device. The image can be captured by an imager operably coupled with the electronic device. The method can further include detecting a geographic position of the electronic device, an imager direction of the imager, and a tilt orientation of the electronic device. The method also can include determining a field of view based on the geographic position, the tilt orientation, and the imager direction. Further, the method can include identifying whether one or more portions of the field within the field of view is a processed segment of the field or an unprocessed segment of the field. Lastly, the method can include visually augmenting the captured image with graphics based at least in part on the identification of the one or more portions of the field.