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Urban expansion prediction method and system based on deep learning

An urban expansion and deep learning technology, applied in prediction, biological neural network model, instruments, etc., can solve the problem of not being able to accurately describe the area of ​​urban construction land, affecting the accuracy of urban expansion simulation, and achieve the effect of improving accuracy.

Active Publication Date: 2020-04-10
GUANGDONG LAB OF SOUTHERN OCEAN SCI & ENG GUANGZHOU +1
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Problems solved by technology

However, the switching threshold of the urban cellular automata model directly affects the accuracy of urban sprawl simulations
In addition, the cellular automata simulation based on the land use type is mainly based on the simulation of the pixel scale, which cannot accurately describe the area of ​​urban construction land in each pixel

Method used

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  • Urban expansion prediction method and system based on deep learning
  • Urban expansion prediction method and system based on deep learning
  • Urban expansion prediction method and system based on deep learning

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Embodiment Construction

[0069] In order to make the purpose, technical solution and advantages of the present application clearer, the embodiments of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0070] It should be clear that the described embodiments are only some of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the embodiments of the present application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the embodiments of the present application.

[0071] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms "a", "said" and "the" used in the embodiments of this application and the appended claims are also intended to include ...

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Abstract

The invention relates to an urban expansion prediction method and system based on deep learning. According to the invention, urban impervious surface coverage data is combined with remote sensing dataand human activity statistical data; and utilizing the LSTM-RNN model to obtain the urban impervious surface coverage of the to-be-measured time period, and constructing a standard deviation ellipseof the city according to the urban impervious surface coverage of the to-be-measured time period to obtain an urban expansion prediction graph of the to-be-measured time period. Compared with the prior art, the method has the advantages that the LSTM-RNN model is used for selecting multiple input variables such as the impermeable surface coverage, the remote sensing data and the human activity statistical data, and the output prediction value is updated according to the correlation of each variable, so that the urban impermeable surface coverage prediction precision in the to-be-tested time period is improved.

Description

technical field [0001] The present invention relates to the technical field of geographic information, in particular to a method and system for predicting urban expansion based on deep learning. Background technique [0002] my country's urbanization process is in an accelerated period, and urban water environment and ecological problems caused by rapid urbanization have become increasingly prominent. One of the characteristics of urban expansion is the increase in the area of ​​urban impervious surfaces. The urban impervious surface has become an indicator and an important driving force of urban environmental changes and human-land interaction, and is a key basis for urban ecological environment planning and protection. The area size, spatial layout and distribution of impervious surfaces are of great significance in the urbanization process and environmental quality assessment. Accurate estimation and extraction of impervious surface information can contribute to the cons...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06N3/04G06K9/00
CPCG06Q10/04G06Q50/26G06V20/176G06N3/044G06N3/045Y02A30/60
Inventor 许剑辉周成虎杨骥姜浩邓应彬
Owner GUANGDONG LAB OF SOUTHERN OCEAN SCI & ENG GUANGZHOU
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