An urban remote sensing image scene classification method in consideration of spatial relationships is provided and includes following steps of: cutting a remote sensing image into sub-images in an even and non-overlapping manner; performing a visual information coding on each of the sub-images to obtain a feature image Fv; inputting the feature image Fv into a crossing transfer unit to obtain hierarchical spatial characteristics; performing convolution of dimensionality reduction on the hierarchical spatial characteristics to obtain dimensionality-reduced hierarchical spatial characteristics; and performing a softmax model based classification on the dimensionality-reduced hierarchical spatial characteristics to obtain a classification result. The method comprehensively considers the role of two kinds of spatial relationships being regional spatial relationship and long-range spatial relationship in classification, and designs three paths in a crossing transfer unit for relationships fusion, thereby obtaining a better urban remote sensing image scene classification result.

Provided are an illegal building identification method and apparatus, a device, and a storage medium, which relate to the field of cloud computing. The specific implementation scheme is: acquiring a target image and a reference image associated with the target image; extracting a target building feature of the target image and a reference building feature of the reference image, respectively; and determining, according to the target building feature and the reference building feature, an illegal building identification result of the target image.

Provided are a disaster information processing apparatus, an operation method of a disaster information processing apparatus, an operation program of a disaster information processing apparatus, and a disaster information processing system capable of grasping a damage situation at a disaster site in a short time without waste. A RW control unit receives a first aerial image obtained by capturing a first imaging range including an area by a first camera mounted on a first drone. A first damage situation analysis unit analyzes a first damage situation of a disaster in the first imaging range based on the first aerial image. A second imaging range determination unit determines a second imaging range of a second camera mounted on a second drone based on a first analysis result, and the second imaging range is relatively narrower than the first imaging range.

A method for automatically identifying global solar photovoltaic (PV) panels based on a cloud platform by using remote sensing. Optical images in a study area for a whole specific year are collected based on the cloud platform, and preprocessing is performed to obtain a surface reflectance image. Seven time-series images are derived and constructed based on spectral features of a solar PV panel: a solar PV panel index image, a water index image, a vegetation index image, a difference image between a first shortwave infrared band and a second shortwave infrared band, a difference image between the first shortwave infrared band and a near-infrared band, a blue band image, and a first shortwave infrared band image. Data in the seven time-series images are synthesized and reconstructed to obtain input data required by a model. A remote sensing theoretical model for automatically identifying a solar PV panel is constructed.

A method (400) of capturing and processing electroluminescence (EL) images (1910) of a PV array (40) is disclosed herein. In a described embodiment, the method 400 includes controlling the aerial vehicle (20) to fly along a flight path to capture EL images (1910) of corresponding PV array subsections (512b) of the PV array (40), deriving respective image quality parameters from at least some of the captured EL images, dynamically adjusting a flight speed of the aerial vehicle along the flight path, based on the respective image quality parameters for capturing the EL images (1910) of the PV array subsections (512b), extracting a plurality of frames (1500) of the PV array subsection (512b) from the EL images (1910); determining a reference frame having a highest image quality of the PV array subsection (512b) from among the extracted frames (2100); performing image alignment of the extracted frames (2100) to the reference frame to generate image aligned frames (2130), and processing the image aligned frames (2130) to produce an enhanced image (2140) of the PV array subsection (512b) having a higher resolution than the reference frame. A system, image processing device, and aerial vehicle for the method thereof are also disclosed.

In a computer-implemented method, one or more digital aerial images of a property of a current or potential policyholder may be received. The digital aerial image(s) may be processed to determine one or more features of the property, including one or more features of a tree. A predicted location of roots of the tree is determined based upon the tree feature(s). The property feature(s) is/are analyzed to determine a risk of damage to a structure located on the property, by analyzing at least the predicted location of roots of the tree to determine a risk of damage to a foundation of the structure. Based at least in part on this risk, a risk output is generated that includes an indication of whether action should be taken to mitigate the risk of damage and/or whether insurance coverage should be offered, and/or includes a measure of the risk of damage.

A method of inspecting a building construction site using a mobile robotic system includes a mobile platform and a sensor system mounted on the mobile platform and configured to generate one or more types of sensor data. The method includes: receiving object identification information identifying at least one building object to be inspected by the mobile robotic system in the building construction site; obtaining a robot navigation map covering the at least one building object based on a building information model for the building construction site; and determining at least one goal point in the robot navigation map for the at least one building object, each goal point being a position in the robot navigation map for the mobile robotic system to navigate autonomously to for inspecting corresponding one or more building objects of the at least one building object. A corresponding inspection system is also provided.

A method can include identifying a geolocation of an object in an image, the method comprising receiving data indicating a pixel coordinate of the image selected by a user, identifying a data point in a targetable three-dimensional (3D) data set corresponding to the selected pixel coordinate, and providing a 3D location of the identified data point.

Systems and methods for aggregating and integrating distributed grid element inputs are disclosed. A data platform is provided for a distribution power grid. The data platform provides a crowd-sourced gaming system for identifying grid elements and determining dynamic electric power topology. The data platform also provides an interactive interface for displaying a view of a certain area with identified grid elements. The data platform communicatively connects to the identified grid elements, collects data from the identified grid elements, and manages the distribution power grid.

A method and a system for generating a composite video feed for a geographical area are disclosed. A video of the geographical area, captured by a camera, of an aerial platform is received. The video includes metadata indicative of location information, which is used to identify the coordinates of the geographical area. An image that is adjacent to the geographical area is received from the geographical information system and is transformed according to the metadata. The coordinates of the geographical area are used to determine an area with the image. The video is embedded in the area by matching the area with the coordinates of the geographical area, where the edges of the video correspond to the boundaries of the area. A composite video feed, including the video embedded along with the image, is generated and a video player displays the composite video feed.