A method performed by a mobile device for handling identification of equipment. The mobile device records an image, in a recording direction at a first location, of the equipment. Upon recording the image, the mobile device further obtains one or more radiation indications for determining a direction of radiation from the equipment; and provides the obtained one or more radiation indications associated with the recorded image, to an internal identifying process at the mobile device and/or a network node for identifying the equipment.

A system and method for identifying a location using image recognition. STR listing images are analyzed and assigned an archetype. Optionally, STR listing images are analyzed with an object detection model and associated with archetypes. The STR dwelling unit type may be determined from the combination of STR image archetypes. A location for the STR listing may then be determined by comparing to images of dwelling units retrieved from databases.

A technique for assessing development condition of a property is provided that determines development condition for an individual property or properties of interest using image or other sensor data from one or more unmanned aerial vehicles taken over the development process. A property condition output may be generated to indicate a condition of the property or properties.

A method for labeling an image, an electronic device, and a storage medium are provided. The method includes the following operations. A remote sensing image is acquired. A local binary image respectively corresponding to at least one building in the remote sensing image and direction angle information of a contour pixel located on a building contour in the local binary image are determined based on the remote sensing image. The direction angle information includes information of an angle between a contour edge where the contour pixel is located and a preset reference direction. A labeled image labeled with a polygonal contour of the at least one building in the remote sensing image is generated based on the local binary image respectively corresponding to the at least one building and the direction angle information.

A method and a system for detecting wire or wire-like obstacles, which method and system are designed for an aircraft. The system for detecting wire or wire-like obstacles comprises a detection device, such as a video camera or a LIDAR device, a computer and a display device. The method includes a step of detecting at least one pylon in the surrounding environment of the aircraft via a detection device, a step of identifying a family of pylons to which each detected pylon corresponds, a step of characterizing at least one cable supported by the at least one detected pylon, and a step of determining a prohibited zone that can potentially contain each pylon and each cable and a safe zone not containing either a pylon or a cable. The prohibited zone and the safe zone may be displayed on the display device.

Aspects of the invention include includes detecting, using a first machine learning model, a first well pad at a first location based at least in part on a first set of data comprising spectral data describing a gas emission from the first location. Detecting an environmental event within a threshold distance of the well pad. Determining a probability of damage to the first well pad from the environmental event.

A system, a method and a computer program product are provided for determining building type of one or more buildings in a geographic region, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain a plurality of mobility features associated with the one or more buildings. The processor may be configured to determine, using a trained machine learning model, one or more transport modes for the one or more buildings, based on the plurality of mobility features. The processor may be further configured to determine, using the trained machine learning model, the building type of the one or more buildings based on the determined one or more transport modes.

In one example embodiment, a software application obtains a set of images that include an aerial cable and generates a 3D model from the set of images. The 3D model initially excludes a representation of the aerial cable. The software application processes each image of the set of images to extract pixels that potentially represent cables and determines a position in 3D space of the 3D model of a pair of attachment points for the aerial cable. The software application defines a vertical plane in 3D space of the 3D model based on the pair of cable attachment points. For each of one or more images of the set of images, the software application projects at least some of the pixels that potentially represent cables onto the vertical plane. The software application then calculates a curve representation (e.g., a catenary equation) for the aerial cable based on the pixels projected onto the vertical plane, and adds a cable model defined by the curve representation to the 3D model to represent the aerial cable.

The present disclosure is directed to a processing system with a virtualized graphics processor for highly parallel processing of graphics tasks as well as other computing tasks. The processing system includes a central processing unit (CPU) configured with a virtualization stack which includes a graphics processing unit (GPU) having hundreds to thousands of GPU cores virtualized into virtual machines (VMs). The GPU cores are loaded with low-level programming routines for graphics tasks. Different GPUs are loaded with different types of programming routines based on their respective dedicated graphics tasks. The cores are segmented into VMs based on the graphics task. By utilizing virtualized GPUs, highly parallel processing of graphics tasks can be achieved.

A computer vision-based system provides for load distribution estimation and load rating and vibration serviceability assessment of structures. The system integrates evaluates the structural load carrying capacity, the diagnosis and prognosis of performance and safety, and vibration serviceability. Cameras record images of a structure, and regions of interest are monitored in those images for their displacement and velocity as loading varies. Where the displacement determined exceeds a predetermined threshold, or where the acceleration determined exceeds predetermined limits, or where the distribution of displacements of parts of the structure deviates substantially from an estimated displacement distribution, an output indicating potential problems with the structure is output.