Training method and device of landslide mass recognition model, electronic equipment and storage medium
A technology for identifying models and training methods, applied in the field of artificial intelligence, can solve the problems of lack of application of target shape features, difficulty in achieving accuracy, etc., and achieve the effect of strengthening spatial representation ability, strong practicability, and improving recognition accuracy.
Pending Publication Date: 2022-06-24
北京箩筐时空数据技术有限公司
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[0004] However, the semantic segmentation method based on convolutional neural network extracts data features by means of violent fitting, lacks the use of target shape features, and is difficult to achieve high accuracy
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[0035] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
[0036] The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.
[0037] In recent years, a variety of geological disasters have occurred in my country, which are highly concealed and destructive, and are difficult to reach for gro...
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The invention discloses a landslide mass recognition model training method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining original data which comprises a high-resolution image and DEM data; the original data is converted into graph structure data, and the graph structure data comprises a graph node matrix and a connection matrix; constructing and initializing a graph neural network model; and inputting the graph structure data into the graph neural network model for training to obtain a landslide mass recognition model. According to the method, the object-oriented thought is extended to remote sensing semantic segmentation in combination with research in the remote sensing field, meanwhile, the image convolution thought is adopted, the spatial representation capacity between the super-pixel blocks can be enhanced, the super-pixel segmentation of the remote sensing image is achieved, the pixel-oriented recognition problem is converted into the object-oriented recognition problem, and the recognition efficiency of the remote sensing image is improved. And the landslide mass identification precision is obviously improved, and the practicability is high.
Description
technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a training method, device, electronic device and storage medium for a landslide body identification model. Background technique [0002] In recent years, a variety of geological disasters have occurred in my country, which are highly concealed and destructive, and are difficult to reach for ground surveys. With the development of deep learning, especially convolutional neural networks, many scientists and researchers have implemented different techniques for producing landslide susceptibility maps representing the locations of possible landslides. Based on remote sensing optical image data, many scholars have used convolutional neural networks to identify landslide bodies in the Himalayas, Loess Landslide, Guizhou Old Town Landslide and other areas, and perform pixel-by-pixel landslide identification. [0003] In the prior art, the convolutional neural ...
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IPC IPC(8): G06V20/10G06N3/04G06N3/08G06V10/26G06V10/44G06V10/82
CPCG06N3/08G06N3/045
Inventor 钟宇峰
Owner 北京箩筐时空数据技术有限公司