Abstract: In some embodiments, the present disclosure relates to an integrated chip that includes an electrical interconnect structure, a thermal interconnect structure, and a thermal passivation layer over a substrate. The electrical interconnect structure includes interconnect vias and interconnect wires embedded within interconnect dielectric layers. The thermal interconnect structure is arranged beside the electrical interconnect structure and includes thermal vias, thermal wires, and/or thermal layers. Further, the thermal interconnect structure is embedded within the interconnect dielectric layers. The thermal passivation layer is arranged over a topmost one of the interconnect dielectric layers. The thermal interconnect structure has a higher thermal conductivity than the interconnect dielectric layers.

Abstract: Methods and apparatus for detecting plant disease in an area, the apparatus including a memory storing instructions and at least one processor configured to execute the instructions to perform operations including obtaining a red-green-blue (RGB) aerial image of the area and a multispectral aerial image of the area containing image data in red, green, blue, red edge, and near-infrared bands; detecting a plurality of plants in the area based on the RGB aerial image of the area; determining a plurality of vegetation indices respectively corresponding to the plurality of plants based on the multispectral aerial image of the area; and determining whether plant disease exists in the plurality of plants respectively based on the plurality of vegetation indices and a disease threshold.

Abstract: Methods and apparatus for detecting plant disease in an area, the apparatus including a memory storing instructions and at least one processor configured to execute the instructions to perform operations including: obtaining a plurality of multispectral aerial images corresponding to a plurality of plants, obtaining one or more template images of one or more sample plants, and determining whether plant disease exists in the plurality of plants based on the multispectral aerial images and the one or more template images.

Abstract: A method for image-guided agriculture includes receiving images; processing the images to generate reflectance maps respectively corresponding to spectral bands; synthesizing the reflectance maps to generate a multispectral image including vegetation index information of a target area; receiving crop information in regions of the target area; and assessing crop conditions for the regions based on the identified crop information and the vegetation index information.

Abstract: A method for image-guided agriculture includes receiving images; processing the images to generate reflectance maps respectively corresponding to spectral bands; synthesizing the reflectance maps to generate a multispectral image including vegetation index information of a target area; receiving crop information in regions of the target area; and assessing crop conditions for the regions based on the identified crop information and the vegetation index information.

Abstract: A method for image-guided agriculture includes capturing images based on one or more ground sampling distance values; processing the images to generate an orthophoto image of a target area; performing feature classification of the orthophoto image to identify corresponding crop information in regions of the target area; and assessing crop conditions in the regions based on one or more vegetation indices and the corresponding crop information in the regions.

Abstract: A method for image-guided agriculture includes receiving images; processing the images to generate reflectance maps respectively corresponding to spectral bands; synthesizing the reflectance maps to generate a multispectral image including vegetation index information of a target area; receiving crop information in regions of the target area; and assessing crop conditions for the regions based on the identified crop information and the vegetation index information.

Abstract: A method for image-guided agriculture includes capturing images based on one or more ground sampling distance values; processing the images to generate an orthophoto image of a target area; performing feature classification of the orthophoto image to identify corresponding crop information in regions of the target area; and assessing crop conditions in the regions based on one or more vegetation indices and the corresponding crop information in the regions.

Abstract: A method for image-guided agriculture includes receiving images; processing the images to generate reflectance maps respectively corresponding to spectral bands; synthesizing the reflectance maps to generate a multispectral image including vegetation index information of a target area; receiving crop information in regions of the target area; and assessing crop conditions for the regions based on the identified crop information and the vegetation index information.

Abstract: An unmanned aerial vehicle (UAV) includes one or more processors, and a memory storing instructions. When executed by the one or more processors, the instructions cause the UAV to perform operations including: recognizing a first gesture of a hand; responsive to a recognition of the first gesture, moving the unmanned aerial vehicle to hover above the hand; detecting a distance between the unmanned aerial vehicle and the hand; responsive to a determination that the distance falls in a range, monitoring the hand to recognize a second gesture of the hand; and responsive to a recognition of the second gesture, landing the unmanned aerial vehicle on the hand.

Abstract: The present application provides route planning methods and apparatuses for an unmanned aerial vehicle. An exemplary route planning method may include determining a quadrant angle in accordance with a starting point and an ending point. The route planning method may also include determining a flight shifting distance in accordance with the quadrant angle, a first interval between waypoints, and a second interval between routes. The route planning method may further include generating a plurality of waypoints and a plurality of routes in accordance with the quadrant angle, the flight shifting distance, the first interval between waypoints, and the second interval between routes. In addition, the route planning method include planning a flight path in accordance with the plurality of waypoints and the plurality of routes.

Abstract: An unmanned aerial vehicle (UAV) includes one or more processors, and a memory storing instructions. When executed by the one or more processors, the instructions cause the UAV to perform operations including: recognizing a first gesture of a hand; responsive to a recognition of the first gesture, moving the unmanned aerial vehicle to hover above the hand; detecting a distance between the unmanned aerial vehicle and the hand; responsive to a determination that the distance falls in a range, monitoring the hand to recognize a second gesture of the hand; and responsive to a recognition of the second gesture, landing the unmanned aerial vehicle on the hand.

Abstract: Methods and systems for detecting objects from aerial imagery are disclosed. The method includes obtaining an image of an area, obtaining a plurality of regional aerial images from the image of the area, classifying the plurality of regional aerial images as a first class or a second class by a classifier, wherein: the first class indicates a regional aerial image contains a target object, the second class indicates a regional aerial image does not contain a target object, and the classifier is trained by first and second training data, wherein the first training data include first training images containing target objects, and the second training data include second training images containing target objects obtained by adjusting at least one of brightness, contrast, color saturation, resolution, or a rotation angle of the first training images; and recognizing a target object in a regional aerial image in the first class.

Abstract: Methods and systems for detecting objects from aerial imagery are disclosed. The method includes obtaining an image of an area, obtaining a plurality of regional aerial images from the image of the area, classifying the plurality of regional aerial images as a first class or a second class by a classifier, wherein: the first class indicates a regional aerial image contains a target object, the second class indicates a regional aerial image does not contain a target object, and the classifier is trained by first and second training data, wherein the first training data include first training images containing target objects, and the second training data include second training images containing target objects obtained by adjusting at least one of brightness, contrast, color saturation, resolution, or a rotation angle of the first training images; and recognizing a target object in a regional aerial image in the first class.

Abstract: Methods and systems for detecting objects from aerial imagery are disclosed. According to certain embodiments, the method may include obtaining a Digital Surface Model (DSM) image of an area. The method may also include obtaining a DSM image of one or more target objects. The method may further include detecting the target object in the area based on the DSM images of the area and the one or more target objects. The method may further include recognizing the detected target objects by artificial intelligence. The method may further include acquiring the positions of the recognized target objects. The method may further include calculating the number of the recognized target objects.

Abstract: Methods and systems for detecting objects from aerial imagery are disclosed. According to certain embodiments, the method may include obtaining a Digital Surface Model (DSM) image of an area. The method may also include obtaining a DSM image of one or more target objects. The method may further include detecting the target object in the area based on the DSM images of the area and the one or more target objects. The method may further include recognizing the detected target objects by artificial intelligence. The method may further include acquiring the positions of the recognized target objects. The method may further include calculating the number of the recognized target objects.

Abstract: The present application provides route planning methods and apparatuses for an unmanned aerial vehicle. An exemplary route planning method may include determining a quadrant angle in accordance with a starting point and an ending point. The route planning method may also include determining a flight shifting distance in accordance with the quadrant angle, a first interval between waypoints, and a second interval between routes. The route planning method may further include generating a plurality of waypoints and a plurality of routes in accordance with the quadrant angle, the flight shifting distance, the first interval between waypoints, and the second interval between routes. In addition, the route planning method include planning a flight path in accordance with the plurality of waypoints and the plurality of routes.

Abstract: A positioning and orientation data analysis system for air vehicles includes an image sensor module, a positioning and orientation module, and a processor module. The image sensor module captures a map image of a region and has an internal direction parameter. The positioning and orientation module generates a positioning and orientation external direction parameter from external satellite positioning information and navigation coordinate information that it tracks. The processor module, which is electrically coupled to the image sensor module and the positioning and orientation module, generates an image external direction parameter by using aerial triangulation of the map image; and then, by comparing this parameter with the positioning and orientation external direction parameter, generates a boresight angle parameter and a lever arm parameter.