Crop loss estimation allows a user to monitor and estimate damage to the crops due to various natural events/factors. State of the art systems used for the crop loss estimation have the disadvantage that they do not convey to the users extent of damage. In addition to this, the existing methods do not take into account the recovery factor of the crops due to multiple factors and end up in overestimating the loss. The disclosure herein generally relates to crop monitoring, and, more particularly, to a method and system for crop loss estimation. In this method, crop loss is assessed based on real-time weather parameters and remote sensing data collected and processed, and crops are classified as being in one of a repairable damage class and a permanent damage class. The system also quantifies the crop loss, which allows the user to understand magnitude of the crop loss.

A device and method for remotely controlling an unmanned aerial vehicle based on reinforcement learning are disclosed. An embodiment provides a device for remotely controlling an unmanned aerial vehicle based on reinforcement learning, where the device includes a processor and a memory connected to the processor, and the memory includes program instructions that can be executed by the processor to determine an inclination direction corresponding to the hand pose of a user, the movement direction of the hand, and the angle in the inclination direction based on sensing data associated with the pose of the hand or the movement of the hand acquired by way of at least one sensor, and determine one of a movement direction, a movement speed, a mode change, a figural trajectory, and a scale of the figural trajectory of the unmanned aerial vehicle according to the determined inclination direction, movement direction, and angle.

The present disclosure provides a UAV video aesthetic quality evaluation method based on multi-modal deep learning, which establishes a UAV video aesthetic evaluation data set, analyzes the UAV video through a multi-modal neural network, extracts high-dimensional features, and concatenates the extracted features, thereby achieving aesthetic quality evaluation of the UAV video. There are four steps, step one to: establish a UAV video aesthetic evaluation data set, which is divided into positive samples and negative samples according to the video shooting quality; step two to: use SLAM technology to restore the UAV's flight trajectory and to reconstruct a sparse 3D structure of the scene; step three to: through a multi-modal neural network, extract features of the input UAV video on the image branch, motion branch, and structure branch respectively; and step four to: concatenate the features on multiple branches to obtain the final video aesthetic label and video scene type.

The present disclosure provides a UAV video aesthetic quality evaluation method based on multi-modal deep learning, which establishes a UAV video aesthetic evaluation data set, analyzes the UAV video through a multi-modal neural network, extracts high-dimensional features, and concatenates the extracted features, thereby achieving aesthetic quality evaluation of the UAV video. There are four steps, step one to: establish a UAV video aesthetic evaluation data set, which is divided into positive samples and negative samples according to the video shooting quality; step two to: use SLAM technology to restore the UAV's flight trajectory and to reconstruct a sparse 3D structure of the scene; step three to: through a multi-modal neural network, extract features of the input UAV video on the image branch, motion branch, and structure branch respectively; and step four to: concatenate the features on multiple branches to obtain the final video aesthetic label and video scene type.

The system, devices and methods herein enable autonomous and tele-operation of tele-operated robots for maintenance of a property around known and unknown obstacles. A method may include using an unmanned aerial vehicle for obtaining additional data relating to the property and obstacles within the property and plan a path around the obstacles using data from sensors on-board the tele-operated robot and the aerial image. A method may also provide optimization of total time needed for performing the property maintenance and the labor costs in situations where manual intervention is needed for navigating the tele-operated robot around obstacles on the property or for removing obstacles on the property.

The system, devices and methods herein enable autonomous and tele-operation of tele-operated robots for maintenance of a property around known and unknown obstacles. A method may include using an unmanned aerial vehicle for obtaining additional data relating to the property and obstacles within the property and plan a path around the obstacles using data from sensors on-board the tele-operated robot and the aerial image. A method may also provide optimization of total time needed for performing the property maintenance and the labor costs in situations where manual intervention is needed for navigating the tele-operated robot around obstacles on the property or for removing obstacles on the property.

Disclosed herein is an apparatus for the real-time monitoring of construction objects. The apparatus for the real-time monitoring of construction objects includes: a communication unit configured to receive image data acquired by photographing a construction site, and to transmit safety information to an external device; and a monitoring unit provided with a prediction model pre-trained using binary image sequences of construction objects at the construction site as training data, and configured to detect a plurality of construction objects from image frames included in image data received via the communication unit and convert the detected construction objects into binary images, to generate future frames by inputting the resulting binary images to the prediction model, and to derive proximity between the construction objects by comparing the generated future frames with the resulting binary images and generate the safety information.

Disclosed herein is an apparatus for the real-time monitoring of construction objects. The apparatus for the real-time monitoring of construction objects includes: a communication unit configured to receive image data acquired by photographing a construction site, and to transmit safety information to an external device; and a monitoring unit provided with a prediction model pre-trained using binary image sequences of construction objects at the construction site as training data, and configured to detect a plurality of construction objects from image frames included in image data received via the communication unit and convert the detected construction objects into binary images, to generate future frames by inputting the resulting binary images to the prediction model, and to derive proximity between the construction objects by comparing the generated future frames with the resulting binary images and generate the safety information.

The present disclosure relates to an information processing apparatus and method, an electronic device and computer readable medium. According to an embodiment, an information processing apparatus includes processing circuitry configured to compare environment data acquired by a user equipment with reference data; determine, based on the comparison, an adjustment for an acquisition manner of the environment data; and perform a control to notify the user equipment of indication information related to the adjustment.

The present disclosure relates to an information processing apparatus and method, an electronic device and computer readable medium. According to an embodiment, an information processing apparatus includes processing circuitry configured to compare environment data acquired by a user equipment with reference data; determine, based on the comparison, an adjustment for an acquisition manner of the environment data; and perform a control to notify the user equipment of indication information related to the adjustment.