A large-scale forest height remote sensing retrieval method includes: acquiring Ice, Cloud and land Elevation Satellite (ICESAT-2) tree height data, Landsat data, Shuttle Radar Topography Mission (SRTM) data, Worldclim data, forest type data and ecological zoning data within a target zone, and preprocessing the data; carrying out georeferencing on the processed data to generate a first data set; calculating spectral features, terrain features and climatic factor features of an image, and combining the calculated features with the ecological zoning data and the forest type data to obtain a second data set; extracting eigenvalues of a same geographical location from the second data set, and combining the extracted eigenvalues with the tree height data to generate training data; constructing a random forest model covering a large zone as an ecological zoning tree height retrieval model, and dividing the obtained training data into a training sample and a verification sample.

A few-shot urban remote sensing image information extraction method based on meta learning and attention includes building a few-shot urban remote sensing information pre-trained model. During a pre-training stage, pre-training network learning is performed for a few-shot set to fully learn feature information of existing samples and obtain initial feature parameters and a deep convolutional network backbone of the few-shot set; the few-shot urban remote sensing information pre-trained model is a network structure including a convolutional layer, a pooling layer and a fully-connected layer, and includes five sections of convolutional network where each section includes two or three convolutional layers, and an end of each section is connected to one maximum pooling layer to reduce a size of a picture; the number of convolutional kernels inside each section is same, and when closer to the fully-connected layer, the number of convolutional kernels is larger.

The present disclosure discloses a remote sensing-based extraction method for a type of a river channel, including: obtaining multi-source remote sensing data of a target area including a river channel; preprocessing the multi-source remote sensing data, and obtaining corresponding reflectance data; according to the reflectance data, analyzing a water index and a vegetation index of the target area; and according to the water index and the vegetation index, constructing first preset conditions for determining whether there is water in the river channel and second preset conditions for determining whether the river channel is a non-dry river channel to determine the type of the river channel. Types of river channels can be divided into four types: non-dry river channels, seasonal dry river channels, temporary water channels, and dry river channels.

An unmanned vehicle includes: at least one camera configured to obtain an image; and an image processor configured to: detect an object from the image; set a region of interest at the object in the image; and detect a target within the region of interest by detecting a change in pixel values from the region of interest, wherein the target is positioned behind the object.

Methods, systems, devices and computer software/program code products enable efficiently finding stereo correspondence between a feature or set of features in a first image or signal, and a search domain in a second image or signal.

A method of determining one or more shading conditions associated with a structure is provided. A method may include determining an azimuth of a reference roof edge relative to an orientation of a first image of a structure. The method may further include determining a relative azimuth of the reference roof edge from a lower hemisphere of a second, different image captured proximate the structure. In addition, the method may include determining one or more shading conditions associated with the structure based on the azimuth of the reference roof edge and the relative azimuth of the reference roof edge.

A method of determining one or more shading conditions associated with a structure is provided. A method may include determining an azimuth of a reference roof edge relative to an orientation of a first image of a structure. The method may further include determining a relative azimuth of the reference roof edge from a lower hemisphere of a second, different image captured proximate the structure. In addition, the method may include determining one or more shading conditions associated with the structure based on the azimuth of the reference roof edge and the relative azimuth of the reference roof edge.

A marine vessel includes a vessel body including a navigation light, an imager provided in a vicinity of or adjacent to the navigation light, and a light shield provided between the navigation light and the imager so as to block light from the navigation light to the imager.

A geographic area mapping system may enable collecting, from a set of mobile devices, radio frequency data, the radio frequency data comprising information about a set of network connections in the geographic area; collecting lidar data for the geographic area; generating a mapping between the collected radio frequency data and the collected lidar data for the geographic area; and providing a visualization of the mapped radio frequency data and lidar data for the geographic area.

A geographic area mapping system may enable collecting, from a set of mobile devices, radio frequency data, the radio frequency data comprising information about a set of network connections in the geographic area; collecting lidar data for the geographic area; generating a mapping between the collected radio frequency data and the collected lidar data for the geographic area; and providing a visualization of the mapped radio frequency data and lidar data for the geographic area.