Methods, systems, and media for image searching are described. Images comprising one query image and a plurality of candidate images are received. For each candidate image, a first model similarity measure from an output of a first model configured for scene classification to perceive scenes in the images is determined. Further, for each candidate image of the plurality of candidate images, a second model similarity measure from the output of a second model configured for attribute classification to perceive attributes in the images is determined. For each candidate image of the plurality of candidate images, a similarity agglomerate index of a weighted aggregate of the first model similarity measure and the second model similarity measure is computed. The plurality of candidate images based on the respective similarity agglomerate index of each candidate image are ranked and a first ranked candidate images corresponding to the searched images are generated.

Methods, systems, and media for image searching are described. Images comprising one query image and a plurality of candidate images are received. For each candidate image, a first model similarity measure from an output of a first model configured for scene classification to perceive scenes in the images is determined. Further, for each candidate image of the plurality of candidate images, a second model similarity measure from the output of a second model configured for attribute classification to perceive attributes in the images is determined. For each candidate image of the plurality of candidate images, a similarity agglomerate index of a weighted aggregate of the first model similarity measure and the second model similarity measure is computed. The plurality of candidate images based on the respective similarity agglomerate index of each candidate image are ranked and a first ranked candidate images corresponding to the searched images are generated.

The present invention is related to a system and method for scheduling and performing maintenance and/or repair on electrical equipment such as electrical panels, electrical outlets, electrical devices and the like. More particularly, the present invention relates to a system and method that allows a building supervisor/owner and/or an electrical maintenance provider to schedule what electrical equipment will be maintained and/or repaired and when that maintenance and/or repair will be conducted. The present invention will then provide a system and method to allow the electrical maintenance/repair provider to perform the maintenance and/or repair on the desired electrical equipment and provide immediate and up-to-date feedback to the system that the required maintenance and/or repair has been completed.

The present invention is related to a system and method for scheduling and performing maintenance and/or repair on electrical equipment such as electrical panels, electrical outlets, electrical devices and the like. More particularly, the present invention relates to a system and method that allows a building supervisor/owner and/or an electrical maintenance provider to schedule what electrical equipment will be maintained and/or repaired and when that maintenance and/or repair will be conducted. The present invention will then provide a system and method to allow the electrical maintenance/repair provider to perform the maintenance and/or repair on the desired electrical equipment and provide immediate and up-to-date feedback to the system that the required maintenance and/or repair has been completed.

Methods and devices related to determining image sensor settings using LiDAR are described. In an example, a method can include receiving, at a processing resource via a LiDAR sensor, first signaling indicative of location data, elevation data, and/or light energy intensity data associated with an object, receiving, at the processing resource via an image sensor, second signaling indicative of data representing an image of the object, generating, based at least in part on the first signaling, additional data representing a frame of reference for the object, transmitting to a user interface third signaling indicative of the data representing the frame of reference for the object and the data representing the image of the object, and displaying, at the user interface and based at least in part on the third signaling, another image that comprises a combination of the frame of reference and the data representing the image.

Methods and devices related to determining image sensor settings using LiDAR are described. In an example, a method can include receiving, at a processing resource via a LiDAR sensor, first signaling indicative of location data, elevation data, and/or light energy intensity data associated with an object, receiving, at the processing resource via an image sensor, second signaling indicative of data representing an image of the object, generating, based at least in part on the first signaling, additional data representing a frame of reference for the object, transmitting to a user interface third signaling indicative of the data representing the frame of reference for the object and the data representing the image of the object, and displaying, at the user interface and based at least in part on the third signaling, another image that comprises a combination of the frame of reference and the data representing the image.

A data storage device and method for efficient image searching are provided. In one embodiment, a data storage device is provided comprising a memory and a controller. The controller is configured to store a plurality of images and a plurality of keys in the memory, wherein each key of the plurality of keys is generated from a respective image of the plurality of images; receive, from a host, a key generated from a target image desired by the host; and return, to the host, an image from the stored plurality of images that is associated with a key that matches the key received from the host. Other embodiments are provided.

A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.