A hardware system is configured for, and a method of, generating detail-rich gradient-based disparity maps in real-time using an automated gradient-based disparity map classification process that is scalable, can be used under different environment conditions with little to no restrictions, and whose level of precision can be adjusted in a scalable manner. Highly accurate cross-spectral stereo matching methods may be used for search and rescue operations and work at day time and night time using current and past visual and full infrared imaging to generate, classify, and identify scenes in real-time with minimum constraints. Such system and methods may be used to improve operations of existing search and rescue equipment.

Various embodiments of a cyber-physical system for providing cloud prediction for photovoltaic array control are disclosed herein.

Systems and methods are provided for analyzing messages generated by a plurality of computing devices associated with a plurality of users in a messaging system to generate training data to train a machine learning model to determine a probability that a media content item was generated inside an enclosed location or outside, receiving a media content item from a computing device, analyzing the media content item using the trained machine learning model to determine a probability that the media content item was generated inside an enclosed location or outside, determining, based on the probability generated by the trained machine learning model, that the media content item was generated inside an enclosed location, and determining an inside temperature associated with the venue based on messages generated by a plurality of computing devices in a messaging system comprising media content items and temperature information for the venue or a similar venue type.

Provided are a system and method for estimating a location of a forest fire. The system for estimating the location of the forest fire may include an artificial intelligence-based forest fire detection module that detects a forest fire from a captured image using an artificial intelligence model; a monitoring camera configured to monitor a predetermined area; a direction estimation module configured to estimate a direction of the forest fire on a map, using a forest fire detection image provided from the artificial intelligence-based forest fire detection module and data of the monitoring camera; and a location estimation module configured to estimate the location of the forest fire, using an altitude map table configured to provide altitude of a corresponding location from latitude and longitude of the monitoring camera, based on the estimated direction of the forest fire.

Automated detection of features and/or parameters within an ocean environment using image data. In an embodiment, captured image data is received from ocean-facing camera(s) that are positioned to capture a region of an ocean environment. Feature(s) are identified within the captured image data, and parameter(s) are measured based on the identified feature(s). Then, when a request for data is received from a user system, the requested data is generated based on the parameter(s) and sent to the user system.

The present invention relates to a method and system for facilitating access to recorded data. The system comprises an interface and a processing device. The interface is arranged to receive data and the processing device is arranged to separate the received data in data subsets, compress each data subset and assign an identifier to each compressed data subset, thereby creating data units each comprising a compressed data subset and an associated identifier, the processing device further being arranged to establish an index on the basis of the assigned identifiers.

A method for identifying a scene, comprising a computing device receiving a plurality of data points corresponding to a scene; the computing device determining one or more subsets of data points from the plurality of data points that are indicative of at least one sub-scene in said scene, said at least one sub-scene displayed on a display device that is part of said scene, wherein said at least one sub-scene does not represent said scene; the computing device categorizing said scene, disregarding said at least one sub-scene, wherein the categorizing includes interpreting said scene by a computer vision system such that said at least one sub-scene is not taken into account in the categorizing of said scene.

A farming machine is configured to identify and treat plants in a field. The farming machine includes one or more light sensors for measuring a characteristic of light. The one or more light sensors are coupled to the farming machine and are directed a substantially upwards orientation away from the plants. A control system adjusts settings of an image acquisition system based on a characteristic of light measured by the one or more light sensors. The image acquisition system captures an image of a plant using one or more image sensors coupled to the farming machine, the one or more image sensors directed in a substantially downwards orientation towards the plants. The control system identifies a plant in the image and actuates a treatment mechanism to treat the identified plant.

A system for monitoring and recording and processing an activity includes one or more cameras for automatically recording video of the activity. A remote media system is located at the location of the activity. A network media processor and services is communicatively coupled with the remote media system. The remote media system includes one or more AI enabled cameras. The AI enabled camera is configured to record the activity. The network media processor is configured to receive an activation request of the AI enabled camera and the validate the record request. The system may automatically administer a skill-based competition.

Described are methods for identifying the in-field positions of plant features on a plant by plant basis. These positions are determined based on images captured as a vehicle (e.g., tractor, sprayer, etc.) including one or more cameras travels through the field along a row of crops. The in-field positions of the plant features are useful for a variety of purposes including, for example, generating three-dimensional data models of plants growing in the field, assessing plant growth and phenotypic features, determining what kinds of treatments to apply including both where to apply the treatments and how much, determining whether to remove weeds or other undesirable plants, and so on.