# Research on Land Utilization Spatial Classification Planning Method Based on Multiocular Vision

Authors: Zhifei Zhang; Shenmin Wang
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

With the development of China’s social economy as well as the accelerating urbanization construction and the expanding scale of cities, the integration of land use and urban land classification based on land use spatial planning has become an important task for the sustainable development of China at present. Land use spatial classification planning is the basic basis for all kinds of development and protection construction activities, and government land use spatial planning at all levels plays an important role in implementing major national, provincial, and municipal strategies and promoting the rational and effective use of land use space. By briefly describing the spatial classification of land use and analyzing the idea of systematic integration of land use, this paper provides guidance and reference for exploring the construction of urban land use classification under land use spatial planning, aiming to improve the classification system of land use spatial planning. A neural network-based land use classification algorithm is proposed for the problems of few labeled samples of remote sensing images with high spatial resolution and feature deformation due to sensor height changes in land use spatial classification planning. By multiscale adaptive fusion of multiple convolutional layer features, the impact of feature deformation on classification accuracy is reduced. To further improve the classification accuracy, the depth features extracted from the pretraining network are used to pretrain the multiscale feature fusion part and the fully connected layer, and the whole network is fine-tuned using the augmented dataset. The experimental results show that the adaptive fusion method improves the fusion effect and effectively improves the accuracy of land use spatial classification planning.


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## Body

## 1. Introduction

The current urbanization process provides great convenience for urban production and life, but the contradiction between urban and rural planning and land management has become more and more prominent. At this stage, to better promote the coordinated and unified development of the two, it is necessary not only to strengthen the coordination and cooperation among all relevant departments but also to pay more attention to the quality of urban and rural planning and construction and land management work in the process of urbanization construction. Reasonable planning is beneficial to the development of cities, but at the present stage, there are certain problems in the process of planning for both. Based on this, this paper explores the problems and optimization measures of planning and land planning in cities analyzed under the land use space system. The land spatial planning study is shown in Figure1.Figure 1
Land spatial planning study.Land planning and management land planning and management, as an important basis for urban development and construction, should fully meet the needs of urban economic development and classify land on this basis. Land resources have a leading role in coordinating economic development, population development, and environmental development in cities [1–3]. During the promotion of urban construction, rational use of land and sustainable development of land resources are of great significance to improve the quality of urban construction, meet the needs of urban construction, and achieve urban economic development. Under the wave of urbanization construction in China, the overall land use planning is an important guarantee to realize the smooth implementation of various national reforms. Urban land use, by coordinating the relationship between land rent and use, makes reasonable arrangements for industrial land, residential land, and other types of land to meet people’s needs. In this paper, we analyze in depth from several angles to improve the efficiency of comprehensive land use and solve the contradiction between not being constrained by land use and meeting the demand for land use, and the result obtained from the overall land use planning is to realize the coordination of environmental, economic, and social benefits [4–7]. The planning and utilization of urban land resources is no longer a matter of allocating resources at present, but also a matter of coordinating with current urban construction and citizens’ working and living standards. Therefore, the planning and utilization of urban land resources should be based on the two perspectives of resource allocation and land resource utilization efficiency.As the current urbanization construction process in China is accelerating, the traditional humanistic and natural environment has been affected and even seriously damaged. The study of China’s urban modernization construction process and the study of rural greening construction process to achieve green development and healthy development has become urgent [8–10]. Building green urban environment is based on optimizing urban space, planning, and designing urban environment, putting forward solutions to save material resources, hydraulic resources, and land resources in the city to achieve the purpose of environmental protection, accelerating land planning and construction, and implementing optimization of land resources, aiming to provide a harmonious living environment for urban residents and meet the needs of harmony between human and nature.The essence of land use spatial classification planning is the optimal allocation of land use quantitative structure and spatial structure. One of the difficulties in the preparation of spatial land use classification planning is how to implement the quantitative structure of land use to a specific geographical space to achieve the optimal allocation of land resources [11, 12]. The configuration of land use quantity structure is usually done by overlaying the current land use map with land suitability evaluation map, topographic map, town planning map, basic farmland protection zone map, nature protection zone map or other special land use planning map, etc., and then using the technical index of suitability to determine the land use of each land unit according to the overlapping situation. Although this overlay method is simple and easy to implement, the workload is large and is influenced and limited by the planner’s own knowledge, experience, and preferences.The development principle urban land use classification should focus on the functionality of land use in the context of continuous marketization and changing needs and should make full use of the actual needs of social development to reasonably increase new land use types, such as the future security type land, whose property rights attributes and operation methods are quite different from those of commercial housing, so different land use control methods should be implemented. The main purpose of urban land classification is to serve the spatial planning of the land, so the construction of the classification system needs to meet the feasibility of land classification. Therefore, it is necessary to subdivide the types of urban land space in the current situation survey and fully consider the operability of the practice [13–15].However, there are few tools in GIS to solve the problem of land use configuration with multiple conflicting objectives, so it is especially urgent to find a method that can objectively and quantitatively optimize the configuration of land use spatial structure. How to match the quantitative structure of land use to specific land units based on the results of land suitability evaluation is a multiobjective integer planning problem with land units as the decision variables. To realize the path exploration of multiocular vision system in large-scale scenes and to provide timely feedback of the surrounding environment information, it becomes the urgent key to build an efficient, low-cost, and visualized GIS for spatial classification planning of land use. In recent years, with the development of multivision computer vision technology and related hardware processes, many classical land use spatial classification planning methods for GIS have emerged and been applied in various fields, especially in terms of accuracy and real-time construction of 3D land use spatial classification planning in complex geographic environments. In the past, there were GIS land use spatial classification planning methods based on visual dense point clouds, but there were problems of lengthy computation and low accuracy. Nowadays, there are different types of sensors for 3D mapping, such as LIDAR and RGBD cameras. Among them, the LIDAR-based geographic mapping method obtains higher accuracy of spatial land use classification planning and is closer to the real environment, but the reconstructed effect of this method lacks texture and only reflects 3D spatial information, and the cost is higher; the reconstructed texture of RGBD camera-based method is clearer, but it is not suitable for geographically complex the large-scale mapping. In contrast, the land use spatial classification planning method of GIS based on multivision stereo matching can obtain 3D information from 2D images by simulating human binoculars and using the principle of stereo vision to adapt to complex geographic environments and has the advantages of automatic, online, noncontact detection, high flexibility, low cost, and clear texture, which can be used to build 3D land use spatial classification planning for geographically large-scale environments [16–18].Although the method of land use spatial classification planning based on multivision GIS has many advantages, there are still problems such as long computation, low real time, and mismatching of depth values due to complex scene information when applied to geographic large-scale map building. To address the shortcomings and drawbacks of the construction of land use spatial classification planning based on multivisual vision, this paper optimizes and improves on the basis of the original stereo matching algorithm: firstly, the original image is grayscale preprocessed and stereo corrected, and the parallax map is obtained based on the traditional matching algorithm, and the singular distortion points are detected by using left-right consistency; secondly, bilinear interpolation and median filtering are used for repair optimization; then, the method can optimize the depth calculation link in the construction of land use spatial classification planning and improve the operation speed, anti-interference, and accuracy of the system.In recent years, China has elevated the optimization of land use spatial development pattern and the strengthening of ecological civilization construction to the national strategy. Scientific and reliable land use spatial land base data is the basis and foundation of land use spatial planning, and unified land use classification is the prerequisite for data acquisition, and land classification should be merged according to different land use characteristics, and its purpose is the key to distinguish different classification standards. However, the existing research focuses on the division of land use space into three types of space from multiple functional perspectives and multiple scales, and the research on data sources and data conversion for spatial function division is still insufficient and lacks current data support; it focuses on the functional division of land use space under a single classification system or a certain perspective and does not make full use of the existing spatial land use classification data and does not construct effective guidelines for convergence of multiple classification systems and identification methods for multisource data fusion.The main contributions of this paper are as follows: through comprehensive comparative analysis method and GIS spatial overlay analysis method, based on the current situation of land use spatial utilization and spatial dominant functions and on the basis of comparative analysis of the differences and discrepancies of the existing national land classification systems, we reconstruct the land use spatial land classification system for land use spatial planning, develop guidelines for the articulation of land use spatial land classification with the existing multiple national land classification systems, and explore the integration of national land. This paper provides basic data support for multiclass spatial classification, pattern optimization, and land use spatial planning. This paper addresses the problem that it is difficult to effectively match the land use quantity structure to specific land units based on the results of land suitability evaluation by conventional superposition methods and other land use allocation methods and proposes an optimal allocation method of land use spatial structure based on eye-catching vision to effectively solve the core problems of macro structure adjustment and land use zoning in land use spatial classification planning.


## 2. Related Work

### 2.1. Spatial Land Use Classification Planning

Land classification is an important basis for spatial land use planning and a scientific basis for optimizing the layout of spatial land use structure. Urban land use classification is an important means to promote the rationality of land use spatial planning and improve the value of land use, so how to systematically integrate land use and urban land use classification under land use spatial planning has become one of the main issues of current social development.Land use spatial planning classification is to reasonably divide land into different categories according to the differences of land itself and certain rules, so that the similar attributes possessed by individual units of land in the region can be centrally summarized for better construction of corresponding functional areas. Therefore, the current land classification in China mainly includes land natural classification, land use classification, land evaluation classification, and land function classification, which provides support for land use. The natural land classification is based on the natural properties of the land, such as climate, soil, hydrology, and geomorphology, and is based on the natural laws of land classification [19, 20]. Land evaluation classification is to classify land according to the similarity and difference of evaluation indexes, such as land quality, production potential, and suitability, which is an important basis for land use planning; land function classification is to classify land according to the services or products it provides, which is essentially to classify the resource attributes of land, such as production land, living land, and ecological land, which provides a great basis and guidance for land use.The idea of systematically integrating land use under land use spatial planning and giving full play to the support and guarantee function of land use spatial planning can optimize the urban and rural patterns in the region through reasonable land use spatial planning and promote the formation of scientific production, living, and ecological space. Moreover, it is necessary to adhere to ecological protection and protection of basic farmland, control the urban-rural development boundary, etc., fully implement the management level, promote the effective control of the total amount of land for construction and urban development intensity, and promote the balanced construction and development of infrastructure and public service facilities. On the other hand, the systematic integration of land use also requires further implementation of national land improvement and supervision of comprehensive management and ecological restoration of urban and rural land. Finally, it is necessary to actively guide the balanced development of regions through the implementation of differentiated policies to guarantee the orderly implementation of spatial planning measures for land use.At present, countries have different standards for land classification, such as the multilevel ecological classification system in the United States, the ecological land classification system in Canada, and the land classification system in the Netherlands. And the way of land classification in China is multiple standards in parallel, showing diversified characteristics. From the perspective of land use function, the classification system of “three-living land” is constructed. We have explored a three-level comprehensive functional land use zoning system at the provincial level by integrating various types of zoning. We analyze the existing land use spatial planning land classification system and propose a land use classification system based on the problems of the existing classification system. The idea of land use division is based on the industrial structure of land use. The natural ecological spatial classification system at the city and county level is established based on the combination of land use and cover types and human activity images [21–23]. The refinement of spatial functions of land use is mainly based on three perspectives of land use, ecosystem, and landscape value, and a classification system corresponding to the functions is established.The functional classification of the land use perspective is mainly based on the economic use of land. The sustainable development perspective classifies landscape values into five major categories and constructs a county-level classification system for the three spatial land uses by systematically classifying ecological landscapes. Inspired by the EU spatial classification, a study on the division of spatial land in Hunan Province was conducted. Based on the national standard rules of land use classification, the classification of “three spatial areas” was established. The Lorenz curve and Gini coefficient method were used to study the distribution of the three spatial areas. Based on the principle of multifunctionality of land use, the classification of “three living spaces” was constructed. In general, the existing studies have explored the spatial classification system of land use and the classification of “three living spaces” in a more systematic way, which is important to support the preparation of spatial land use planning.To establish a scientific land use evaluation system and index system and management mechanism to integrate land use based on land use spatial planning system, it is necessary to improve the evaluation standards of resources, environmental carrying capacity, and spatial suitability of development to provide basic conditions for land planning layout. At the same time, we should design based on land use spatial planning, land use as the center, construction of complete infrastructure, coordination of regional development and urban-rural integration, and reasonable layout of land use planning guidance and constraint management. It is also necessary to strengthen the strength of resource protection, and the ability to control spatial utilization to ensure that land resources can be used to maximum effect. Moreover, this process needs a relatively perfect land use spatial planning standard system and land use control mechanism as a guarantee to fully guarantee the rigidity and flexibility of land use spatial planning and realize the efficient use of land.


### 2.2. Multiocular Vision Technology

With the rapid development of science and technology, human spatial ways and techniques of land use have been increasing. Especially with the support of aerospace technology, remote sensing imaging technology based on high-altitude overhead perspective and even outer space has come into being. Remote sensing technology perceives the characteristics of target objects and analyzes them through the propagation and reception of electromagnetic waves. In the 1970s, the world’s first remote sensing satellite was successfully launched to open a new era of remote sensing detection technology. The exploration of technology using satellites as remote sensing platforms has been favored by governments of various countries. Among the early starters, the U.S. government and some of its commercial companies have implemented multiple remote sensing satellite launch programs simultaneously in recent decades, with each series of satellite launch programs containing multiple remote sensing satellites to form a satellite constellation [24–27].At the same time, Canada is also implementing a constellation program in which satellites will carry sophisticated LIDAR systems. When the constellation is fully launched, it will reduce the interval between revisits to Canada to less than 24 hours. In addition, the earliest started in 1974 during the Cold War with the Soviet Union’s return-type remote sensing satellite for Earth observation. Looking back at the development of remote sensing technology in China, which is also strongly supported by the government, China’s aerospace technology has also made remarkable progress since the 1980s; from the meteorological satellites in 1988 to the environmental disaster mitigation series of satellites in 2008 and the high-definition series of satellites since 2013, the development of the aerospace industry has been fruitful. By the end of April 2018, the number of remote sensing satellites worldwide increased to 684. With the deployment of so many satellite-based and airborne remote sensing platforms, countries around the world have a huge amount of remote sensing data to be processed urgently every day. Especially for optical remote sensing data, the processing and understanding of the data has been far from keeping up with the acquisition of data. The high-resolution, large-format optical remote sensing images acquired by satellites and land types often cover a large and complex amount of ground material information. Obviously, it is no longer practical to discriminate and identify these massive data in real time through manual efforts [28]. As a result, how to use computer technology to understand and interpret optical remote sensing images has become the focus and difficult problem of current research.Multiocular stereo vision is to simulate the human visual system to construct the real world. Stereo matching, as the key to 3D reconstruction and noncontact ranging, is one of the core elements of multiocular vision. Multiocular vision flowchart is shown in Figure2. It has the advantages of simple implementation, low cost, and ranging under noncontact conditions by acquiring depth through two-dimensional images and can be used in robot guidance systems for navigation judgment and target pickup, in industrial automation control systems for parts installation, quality inspection, and environmental inspection, and in security monitoring systems for human flow detection and hazard alarm [29–31]. The stereo matching algorithm is divided into region matching, feature matching, phase matching, and energy matching according to the matching primitives and methods. Area matching is influenced by image affine and radiometric distortion, difficult to choose the constraint window, and easy to produce false matches at depth discontinuities; feature matching is insensitive to image geometric transformation and has strong anti-interference and low complexity; the defect is that the parallax results are sparse and need to go through the process of interpolation and fitting; phase matching algorithm is very sensitive to rotational transformation and unstable singularities; energy matching is used to construct global energy. The energy matching algorithm obtains the parallax by constructing the global energy function, which cannot be used for large deviation images and has too much complexity.Figure 2
Multiocular vision flowchart.Currently, the semiglobal stereo matching of remote sensing images combined with accelerated robust features uses feature points to guide path aggregation; the stereo matching algorithm based on AD Census features enhances the robustness to local noisy pixels; a SURF-BRISK algorithm combining Hamming distance and affine transformation to match the localization method is proposed; the multiocular stereo matching algorithm with optimized oblique planes improves the computational speed. The feature-based dense stereo matching first uses linear contrast spreading to highlight the important texture of the image, extracts feature information with accelerated segmentation detection features, relies on fast approximate nearest neighbor search library andK-nearest neighbor algorithm to achieve feature matching after accelerated robust feature description, and then uses random sampling consistency algorithm to remove false matches; the obtained exact matching point pairs are used as seed pairs based on parallax continuity. The parallax is calculated by using the simplified zero-mean normalized interrelationship number as the similarity measure, obtaining the minimum surrogate value point pair and eliminating some of the false matches by thresholding, using the polar line constraint to reduce the search complexity, and using the two-way matching strategy to improve the matching accuracy: subpixel fitting of the parallax value to obtain the subpixel parallax to improve the parallax accuracy and make the parallax. The subpixel fitting of parallax values to obtain subpixel parallaxes improves the parallax accuracy and smooths the transition in the continuous region. The algorithm solves the shortcomings of feature matching, reduces the time complexity and noise interference, and improves the accuracy and precision, especially for the weak texture and depth discontinuity.


## 2.1. Spatial Land Use Classification Planning

Land classification is an important basis for spatial land use planning and a scientific basis for optimizing the layout of spatial land use structure. Urban land use classification is an important means to promote the rationality of land use spatial planning and improve the value of land use, so how to systematically integrate land use and urban land use classification under land use spatial planning has become one of the main issues of current social development.Land use spatial planning classification is to reasonably divide land into different categories according to the differences of land itself and certain rules, so that the similar attributes possessed by individual units of land in the region can be centrally summarized for better construction of corresponding functional areas. Therefore, the current land classification in China mainly includes land natural classification, land use classification, land evaluation classification, and land function classification, which provides support for land use. The natural land classification is based on the natural properties of the land, such as climate, soil, hydrology, and geomorphology, and is based on the natural laws of land classification [19, 20]. Land evaluation classification is to classify land according to the similarity and difference of evaluation indexes, such as land quality, production potential, and suitability, which is an important basis for land use planning; land function classification is to classify land according to the services or products it provides, which is essentially to classify the resource attributes of land, such as production land, living land, and ecological land, which provides a great basis and guidance for land use.The idea of systematically integrating land use under land use spatial planning and giving full play to the support and guarantee function of land use spatial planning can optimize the urban and rural patterns in the region through reasonable land use spatial planning and promote the formation of scientific production, living, and ecological space. Moreover, it is necessary to adhere to ecological protection and protection of basic farmland, control the urban-rural development boundary, etc., fully implement the management level, promote the effective control of the total amount of land for construction and urban development intensity, and promote the balanced construction and development of infrastructure and public service facilities. On the other hand, the systematic integration of land use also requires further implementation of national land improvement and supervision of comprehensive management and ecological restoration of urban and rural land. Finally, it is necessary to actively guide the balanced development of regions through the implementation of differentiated policies to guarantee the orderly implementation of spatial planning measures for land use.At present, countries have different standards for land classification, such as the multilevel ecological classification system in the United States, the ecological land classification system in Canada, and the land classification system in the Netherlands. And the way of land classification in China is multiple standards in parallel, showing diversified characteristics. From the perspective of land use function, the classification system of “three-living land” is constructed. We have explored a three-level comprehensive functional land use zoning system at the provincial level by integrating various types of zoning. We analyze the existing land use spatial planning land classification system and propose a land use classification system based on the problems of the existing classification system. The idea of land use division is based on the industrial structure of land use. The natural ecological spatial classification system at the city and county level is established based on the combination of land use and cover types and human activity images [21–23]. The refinement of spatial functions of land use is mainly based on three perspectives of land use, ecosystem, and landscape value, and a classification system corresponding to the functions is established.The functional classification of the land use perspective is mainly based on the economic use of land. The sustainable development perspective classifies landscape values into five major categories and constructs a county-level classification system for the three spatial land uses by systematically classifying ecological landscapes. Inspired by the EU spatial classification, a study on the division of spatial land in Hunan Province was conducted. Based on the national standard rules of land use classification, the classification of “three spatial areas” was established. The Lorenz curve and Gini coefficient method were used to study the distribution of the three spatial areas. Based on the principle of multifunctionality of land use, the classification of “three living spaces” was constructed. In general, the existing studies have explored the spatial classification system of land use and the classification of “three living spaces” in a more systematic way, which is important to support the preparation of spatial land use planning.To establish a scientific land use evaluation system and index system and management mechanism to integrate land use based on land use spatial planning system, it is necessary to improve the evaluation standards of resources, environmental carrying capacity, and spatial suitability of development to provide basic conditions for land planning layout. At the same time, we should design based on land use spatial planning, land use as the center, construction of complete infrastructure, coordination of regional development and urban-rural integration, and reasonable layout of land use planning guidance and constraint management. It is also necessary to strengthen the strength of resource protection, and the ability to control spatial utilization to ensure that land resources can be used to maximum effect. Moreover, this process needs a relatively perfect land use spatial planning standard system and land use control mechanism as a guarantee to fully guarantee the rigidity and flexibility of land use spatial planning and realize the efficient use of land.


## 2.2. Multiocular Vision Technology

With the rapid development of science and technology, human spatial ways and techniques of land use have been increasing. Especially with the support of aerospace technology, remote sensing imaging technology based on high-altitude overhead perspective and even outer space has come into being. Remote sensing technology perceives the characteristics of target objects and analyzes them through the propagation and reception of electromagnetic waves. In the 1970s, the world’s first remote sensing satellite was successfully launched to open a new era of remote sensing detection technology. The exploration of technology using satellites as remote sensing platforms has been favored by governments of various countries. Among the early starters, the U.S. government and some of its commercial companies have implemented multiple remote sensing satellite launch programs simultaneously in recent decades, with each series of satellite launch programs containing multiple remote sensing satellites to form a satellite constellation [24–27].At the same time, Canada is also implementing a constellation program in which satellites will carry sophisticated LIDAR systems. When the constellation is fully launched, it will reduce the interval between revisits to Canada to less than 24 hours. In addition, the earliest started in 1974 during the Cold War with the Soviet Union’s return-type remote sensing satellite for Earth observation. Looking back at the development of remote sensing technology in China, which is also strongly supported by the government, China’s aerospace technology has also made remarkable progress since the 1980s; from the meteorological satellites in 1988 to the environmental disaster mitigation series of satellites in 2008 and the high-definition series of satellites since 2013, the development of the aerospace industry has been fruitful. By the end of April 2018, the number of remote sensing satellites worldwide increased to 684. With the deployment of so many satellite-based and airborne remote sensing platforms, countries around the world have a huge amount of remote sensing data to be processed urgently every day. Especially for optical remote sensing data, the processing and understanding of the data has been far from keeping up with the acquisition of data. The high-resolution, large-format optical remote sensing images acquired by satellites and land types often cover a large and complex amount of ground material information. Obviously, it is no longer practical to discriminate and identify these massive data in real time through manual efforts [28]. As a result, how to use computer technology to understand and interpret optical remote sensing images has become the focus and difficult problem of current research.Multiocular stereo vision is to simulate the human visual system to construct the real world. Stereo matching, as the key to 3D reconstruction and noncontact ranging, is one of the core elements of multiocular vision. Multiocular vision flowchart is shown in Figure2. It has the advantages of simple implementation, low cost, and ranging under noncontact conditions by acquiring depth through two-dimensional images and can be used in robot guidance systems for navigation judgment and target pickup, in industrial automation control systems for parts installation, quality inspection, and environmental inspection, and in security monitoring systems for human flow detection and hazard alarm [29–31]. The stereo matching algorithm is divided into region matching, feature matching, phase matching, and energy matching according to the matching primitives and methods. Area matching is influenced by image affine and radiometric distortion, difficult to choose the constraint window, and easy to produce false matches at depth discontinuities; feature matching is insensitive to image geometric transformation and has strong anti-interference and low complexity; the defect is that the parallax results are sparse and need to go through the process of interpolation and fitting; phase matching algorithm is very sensitive to rotational transformation and unstable singularities; energy matching is used to construct global energy. The energy matching algorithm obtains the parallax by constructing the global energy function, which cannot be used for large deviation images and has too much complexity.Figure 2
Multiocular vision flowchart.Currently, the semiglobal stereo matching of remote sensing images combined with accelerated robust features uses feature points to guide path aggregation; the stereo matching algorithm based on AD Census features enhances the robustness to local noisy pixels; a SURF-BRISK algorithm combining Hamming distance and affine transformation to match the localization method is proposed; the multiocular stereo matching algorithm with optimized oblique planes improves the computational speed. The feature-based dense stereo matching first uses linear contrast spreading to highlight the important texture of the image, extracts feature information with accelerated segmentation detection features, relies on fast approximate nearest neighbor search library andK-nearest neighbor algorithm to achieve feature matching after accelerated robust feature description, and then uses random sampling consistency algorithm to remove false matches; the obtained exact matching point pairs are used as seed pairs based on parallax continuity. The parallax is calculated by using the simplified zero-mean normalized interrelationship number as the similarity measure, obtaining the minimum surrogate value point pair and eliminating some of the false matches by thresholding, using the polar line constraint to reduce the search complexity, and using the two-way matching strategy to improve the matching accuracy: subpixel fitting of the parallax value to obtain the subpixel parallax to improve the parallax accuracy and make the parallax. The subpixel fitting of parallax values to obtain subpixel parallaxes improves the parallax accuracy and smooths the transition in the continuous region. The algorithm solves the shortcomings of feature matching, reduces the time complexity and noise interference, and improves the accuracy and precision, especially for the weak texture and depth discontinuity.


## 3. Methods

### 3.1. Model Architecture

The traditional CenterNet network adopts a code-decode structure to learn high-level semantic information through successive convolutional operations of the network. However, the targets in remote sensing images are small and dense, and a series of convolution will cause the aggregation of small target features, resulting in problems such as missed detection and false detection. Therefore, this paper proposes a detection model AFF-CenterNet based on CenterNet algorithm, which combines attention mechanism and parallel-layer feature sharing structure. This method combines deep features and shallow features, effectively combining the advantages of strong semantic information of deep features and strong location and texture information of shallow features, which is effective in improving small target detection. The channel attention module is added before the parallel-layer module to reduce background interference. This method adaptively calibrates the feature responses between different channels, which effectively improves the feature extraction capability of the network. The model structure diagram is shown in Figure3.Figure 3
Model structure.


### 3.2. Parallel Layer Structure

In the network structure of CenterNet algorithm, the Conv1 and Conv7 layers, Conv2 and Conv6 layers, and Conv3 and Conv5 layers are fused in this paper. Since these feature layers have different spatial sizes, they are processed by a “Feature Fusion” module before being fused. For this module, two topologies, named CenterNet-C and CenterNet-T, are designed as shown in Figure4, where CenterNet-C has a standard 1×1 convolution to make the feature layers have the same spatial size before and after fusion, and CenterNet-T replaces the standard convolution in CenterNet-C with a hole convolution for testing. Since each feature value in different layers has a different scale, it needs to be processed by batch normalization and ReLU activation after convolution.Figure 4
Parallel fusion module structure diagram.


### 3.3. Channel Attention Module

The attention mechanism can focus on the local information of the image, locate the information of interest, and suppress the useless information. To make the model focus more on the channels with effective information, this paper introduces the Squeeze-Excitation Attention Module (SE-Net) before the parallel feature sharing module. This module first performs a squeeze operation on theH×W feature map with the input channel number C to obtain a 1×1 feature map with the channel number C, which corresponds to the global pooling. Then, the obtained feature maps are subjected to the excitation operation to obtain the weight values between channels, which corresponds to the twice fully connected and sigmoid layers. Finally, the original feature map is multiplied by the weights of the corresponding channels through scale scaling to obtain the new feature map, to update the effective information channels, and to suppress the useless information channels.


### 3.4. Multiocular Visual Depth Values

Assuming a pointpx,y,zon the three-dimensional space, with the left camera as the reference coordinate system, the projection points on the left and right lenses of the horizontally placed binocular camera areLxl,yl,Rxr,yr, andOl,Orwhich are the center points of the left and right lenses, respectively;fis the focal length of the camera, andBis the baseline of the binocular camera; according to the principle of triangular similarity of perspective transformation, it is known that
(1)zf=yy1=yyr=xx1=x−Bxr.The parallax under the model can be expressed as(2)d=x1−xr.From the equation, the spatial coordinates of point p and the depth value can be deduced as(3)z=fBx1−xr,x=x1Bx1−xr,y=y1Bx1−xr.According to the above three-dimensional spatial point coordinates and depth value solution process, in order to meet the accuracy and efficiency of octree map construction for complex environment of land use space, it is necessary to extract the two-dimensional edge feature points of the environment and solve the corresponding three-dimensional spatial coordinate points of the feature points, and the distribution of the feature points is closely related to the environmental information, and a reasonable feature point extraction method can not only maintain the integrity of the map but also reduce the system operation. A reasonable feature point extraction method can not only maintain the integrity of the map but also reduce the operation complexity of the system, thus ensuring the real-time and efficient system. Therefore, this paper proposes a method of feature point extraction based on edge binary map, by finding the coordinates of feature points of edge binary map and then obtaining the corresponding feature point parallax values from parallax map and calculating the spatial coordinates and depth values.


### 3.5. Loss Function

AFF-CenterNet still adopts the construction method of CenterNet loss function, which consists of three parts: centroid prediction lossLk, bias loss Loff, and width-height loss Lsize. CenterNet uses pixel-level logistic regression focal loss loss function to solve the problem of uneven distribution of positive and negative samples.
(4)Lk=−1N∑xyc1−Y∧xycαlogY^xyc,Yxyc=1,1−YxycβY∧xycαlog1−Y^xyc,where α and β are the hyperparameters of focal loss, which are taken as 2 and 4, respectively, in the experiment. n is the number of key points in the image, which serves to normalize all focal losses. Y^xyc is the predicted value and Yxyc the true label value. When Yxyc is equal to 1, for easily distinguishable samples, the predicted value Y^xyc is close to 1, making 1−Y∧xycα and logY^xyc close to 0 to obtain a smaller Lk. On the contrary, for hard-to-distinguish samples, the predicted value Y^xyc is close to 0, making the final Lk larger. When Y^xyc is not equal to 1, the theoretical value of the predicted value Y^xyc should be 0. If this value is larger, Y∧xycα will increase to serve as a penalty. If the predicted value is close to 0, then Y∧xycα will be small to reduce the loss weight. And 1−Yxycβ serves to weaken the loss weight of negative samples around the centroid when Yxyc is not equal to 1. Since the resolution of the feature map after backbone extraction network processing becomes one-fourth of the input image, which is equivalent to one pixel point of the output feature map corresponding to a 4×4 region of the original image, it causes the loss of details of the original image and makes the prediction biased. Therefore, the centroid bias value O^ is introduced, and the bias value is trained using the L1 loss function.
(5)Loff=1N∑pO^p~−pR−p~,where N is the number of key points in the image, p is the target frame centroid, and R is the downsampling factor, which takes the value of 4 during the experiment. CenterNet regresses to the object size sk=x2k−x1k,y2k−y1k for each object after predicting all the centers. To reduce the computational effort, a single size prediction S^ is used for each target species, and the width-height loss Lsize is trained using the L1 loss function, as shown in
(6)Lsize=1N∑k=1NS^pk−sk.The total lossLdet is obtained from the weighted sum of the losses of the above branches, as shown in
(7)Ldet=Lk+λoffLoff+λsizeLsize,where the weights λoff and λsize are taken as 1 and 0.1, respectively.


## 3.1. Model Architecture

The traditional CenterNet network adopts a code-decode structure to learn high-level semantic information through successive convolutional operations of the network. However, the targets in remote sensing images are small and dense, and a series of convolution will cause the aggregation of small target features, resulting in problems such as missed detection and false detection. Therefore, this paper proposes a detection model AFF-CenterNet based on CenterNet algorithm, which combines attention mechanism and parallel-layer feature sharing structure. This method combines deep features and shallow features, effectively combining the advantages of strong semantic information of deep features and strong location and texture information of shallow features, which is effective in improving small target detection. The channel attention module is added before the parallel-layer module to reduce background interference. This method adaptively calibrates the feature responses between different channels, which effectively improves the feature extraction capability of the network. The model structure diagram is shown in Figure3.Figure 3
Model structure.


## 3.2. Parallel Layer Structure

In the network structure of CenterNet algorithm, the Conv1 and Conv7 layers, Conv2 and Conv6 layers, and Conv3 and Conv5 layers are fused in this paper. Since these feature layers have different spatial sizes, they are processed by a “Feature Fusion” module before being fused. For this module, two topologies, named CenterNet-C and CenterNet-T, are designed as shown in Figure4, where CenterNet-C has a standard 1×1 convolution to make the feature layers have the same spatial size before and after fusion, and CenterNet-T replaces the standard convolution in CenterNet-C with a hole convolution for testing. Since each feature value in different layers has a different scale, it needs to be processed by batch normalization and ReLU activation after convolution.Figure 4
Parallel fusion module structure diagram.


## 3.3. Channel Attention Module

The attention mechanism can focus on the local information of the image, locate the information of interest, and suppress the useless information. To make the model focus more on the channels with effective information, this paper introduces the Squeeze-Excitation Attention Module (SE-Net) before the parallel feature sharing module. This module first performs a squeeze operation on theH×W feature map with the input channel number C to obtain a 1×1 feature map with the channel number C, which corresponds to the global pooling. Then, the obtained feature maps are subjected to the excitation operation to obtain the weight values between channels, which corresponds to the twice fully connected and sigmoid layers. Finally, the original feature map is multiplied by the weights of the corresponding channels through scale scaling to obtain the new feature map, to update the effective information channels, and to suppress the useless information channels.


## 3.4. Multiocular Visual Depth Values

Assuming a pointpx,y,zon the three-dimensional space, with the left camera as the reference coordinate system, the projection points on the left and right lenses of the horizontally placed binocular camera areLxl,yl,Rxr,yr, andOl,Orwhich are the center points of the left and right lenses, respectively;fis the focal length of the camera, andBis the baseline of the binocular camera; according to the principle of triangular similarity of perspective transformation, it is known that
(1)zf=yy1=yyr=xx1=x−Bxr.The parallax under the model can be expressed as(2)d=x1−xr.From the equation, the spatial coordinates of point p and the depth value can be deduced as(3)z=fBx1−xr,x=x1Bx1−xr,y=y1Bx1−xr.According to the above three-dimensional spatial point coordinates and depth value solution process, in order to meet the accuracy and efficiency of octree map construction for complex environment of land use space, it is necessary to extract the two-dimensional edge feature points of the environment and solve the corresponding three-dimensional spatial coordinate points of the feature points, and the distribution of the feature points is closely related to the environmental information, and a reasonable feature point extraction method can not only maintain the integrity of the map but also reduce the system operation. A reasonable feature point extraction method can not only maintain the integrity of the map but also reduce the operation complexity of the system, thus ensuring the real-time and efficient system. Therefore, this paper proposes a method of feature point extraction based on edge binary map, by finding the coordinates of feature points of edge binary map and then obtaining the corresponding feature point parallax values from parallax map and calculating the spatial coordinates and depth values.


## 3.5. Loss Function

AFF-CenterNet still adopts the construction method of CenterNet loss function, which consists of three parts: centroid prediction lossLk, bias loss Loff, and width-height loss Lsize. CenterNet uses pixel-level logistic regression focal loss loss function to solve the problem of uneven distribution of positive and negative samples.
(4)Lk=−1N∑xyc1−Y∧xycαlogY^xyc,Yxyc=1,1−YxycβY∧xycαlog1−Y^xyc,where α and β are the hyperparameters of focal loss, which are taken as 2 and 4, respectively, in the experiment. n is the number of key points in the image, which serves to normalize all focal losses. Y^xyc is the predicted value and Yxyc the true label value. When Yxyc is equal to 1, for easily distinguishable samples, the predicted value Y^xyc is close to 1, making 1−Y∧xycα and logY^xyc close to 0 to obtain a smaller Lk. On the contrary, for hard-to-distinguish samples, the predicted value Y^xyc is close to 0, making the final Lk larger. When Y^xyc is not equal to 1, the theoretical value of the predicted value Y^xyc should be 0. If this value is larger, Y∧xycα will increase to serve as a penalty. If the predicted value is close to 0, then Y∧xycα will be small to reduce the loss weight. And 1−Yxycβ serves to weaken the loss weight of negative samples around the centroid when Yxyc is not equal to 1. Since the resolution of the feature map after backbone extraction network processing becomes one-fourth of the input image, which is equivalent to one pixel point of the output feature map corresponding to a 4×4 region of the original image, it causes the loss of details of the original image and makes the prediction biased. Therefore, the centroid bias value O^ is introduced, and the bias value is trained using the L1 loss function.
(5)Loff=1N∑pO^p~−pR−p~,where N is the number of key points in the image, p is the target frame centroid, and R is the downsampling factor, which takes the value of 4 during the experiment. CenterNet regresses to the object size sk=x2k−x1k,y2k−y1k for each object after predicting all the centers. To reduce the computational effort, a single size prediction S^ is used for each target species, and the width-height loss Lsize is trained using the L1 loss function, as shown in
(6)Lsize=1N∑k=1NS^pk−sk.The total lossLdet is obtained from the weighted sum of the losses of the above branches, as shown in
(7)Ldet=Lk+λoffLoff+λsizeLsize,where the weights λoff and λsize are taken as 1 and 0.1, respectively.


## 4. Experiments and Results

### 4.1. Experiment Setup

In order to verify the feasibility of the proposed algorithm, the category images of an urban land space remote sensing image dataset in China are selected for the training and testing of the network, respectively. Among them, UCASAOD dataset is produced by the University of Chinese Academy of Sciences, which contains 1000 remote sensing images with 7482 samples, with more concentrated data and more uniform distribution of target directions. RSOD dataset contains 446 remote sensing images with 4993 samples, with diverse brightness and contrast in the images, and interference such as occlusion, shadow, and distortion. During the experiment, 10% of the images of the two datasets were randomly selected as test samples, 10% of the remaining 90% of the images were selected as the validation set, and the rest were used as the training set, and the configuration of the experimental environment is shown in Table1.Table 1
Software and hardware environment configuration.
ParametersConfigurationOperating systemUbuntu 20.04.2 LTSCPUInter Core i9-10900KFCPU memory62.7 GGPUGeForce RTX 3080GPU memory10GProgramming languagePythonProgramming platformsPycharmGraphics acceleration environmentCUDA11.2, CuDNN7.6Deep learning frameworkPytorch 1.7.0, Torchvision 0.8.1In the experiments, the downsampling rateR is 4, and the Adam optimizer is used for iterative training, and the input images are uniformly scaled to a resolution of 512×512. The initial learning rate is set to 1e-3, and the batch size is 4. After training 50 epochs, the learning rate is reduced to 1e-4, and then, 50 epochs are trained. In addition, to speed up the convergence, the backbone of ResNet-50 is trained using the pretrained weights obtained from the ImageNet classification task.


### 4.2. Experimental Results

The comparison results of several network structures before and after the improvement on the UCFAS-AOD test set are shown in Figures5 and 6. The detection accuracy of the improved network is significantly improved while maintaining certain computational efficiency. CenterNet-T with null convolution improves the detection accuracy by 0.59 percentage points compared with CenterNet-C with standard 1×1 convolution, and CenterNet-SC and CenterNet-ST improve the detection accuracy by 1.34 percentage points and 1.46 percentage points compared with CenterNet-C and CenterNet-T, respectively. CenterNet-ST, which has the best overall effect, improves the detection accuracy by 16 percentage points and 0.71 percentage points over CenterNet and CenterNet-SC, respectively, so the CenterNet-ST topology is subsequently used as the AFF-CenterNet algorithm model.Figure 5
Comparison of different improved structure FPS.Figure 6
Comparison of different improved structure map.Three sets of representative images were selected from the dataset for testing and the before and after improvement comparison graphs. Because the feature extraction layer of the original network has gone through the “encoding-decoding” structure, the semantic information of small targets is seriously lost, which makes it difficult to detect small targets with similar background color, while the parallel-layer feature sharing network of AFF-CenterNet effectively fuses deep and shallow features to reduce the semantic loss, and the small targets with low contrast with the background are still detected. Small targets with low background contrast can still be effectively recognized.Table2 shows the confusion matrix of the accuracy of the first-level ground class, and the overall accuracy and kappa coefficient are used to determine the accuracy of the ground class. The overall accuracy of primary land class is 86.0%, and the kappa coefficient is 0.75; the overall accuracy of secondary land class is 73.9%, and the kappa coefficient is 0.69. Among them, the accuracy of commercial land user is 95.1%, and the accuracy of park and green space user is 97.1%; the accuracy of institutional land and medical and health land classification is lower, and the accuracy of the former user is 15.7%, and the latter user is 11.1%. The accuracy of the former is 15.7%, and the accuracy of the latter is 11.1%.Table 2
Confusion matrix for level 1 terrestrial accuracy.
Type of land useResidential landCommercial landIndustrial, mining, and storage landTransportationLand for public administration and public servicesUser accuracy (%)Residential land357315717470509233.8Commercial land0103290080492.7Industrial, mining, and storageland0010184010498.9Transportation4680382669044.0Land for public administration and public services8095709767924844693.9Producer accuracy (%)73.693.476.645.760.633.8Overall accuracy (%)86.0Kappa coefficient0.75Figures7, 8, and 9 present the statistical parameters of the comparison between historical and simulated values of each land use type during 2014-2020. The MRE of forest land, grassland, urban land, transportation land, and water can basically be controlled within or around 5%, while the MRE of cropland and garden land are 11.02% and 7.53%, respectively. The Nash coefficients of cropland are negative, while the Nash coefficients of other land types are greater than 0. The R2 of grassland, urban land, and water reaches 0.99, 0.70, and 0.84, respectively, while the R2 of other land types are not satisfactory, especially for cropland and forest land, due to the poor simulation effect of early data for model validation. Although the simulation results of the model are not satisfactory, they can still be used. Taking the model fit validation curve of cropland in Figures 7, 8, and 9 as an example, the relative error from 2009 to 2012 reached about 30%, and the simulation effect after that was very good, with only 2.24% MRE. A certain degree of error is allowed in system dynamics models, which are often used to study changes in complex systems over time series. The results of the model validation showed that the model could meet the needs of the next study.Figure 7
Model validation MRD statistical parameters.Figure 8
Model validationR2 statistical parameters.Figure 9
Model validation of NSE statistical parameters.Table3 presents the average annual dynamic attitude of each land use type. Both urban land and transportation land in construction land show a slowing trend in growth rate. Under a series of land ecology construction policies in a city in China, forest land and water areas, which are the objects of ecological value protection, have been protected to a certain extent, and soil erosion and habitat encroachment have been effectively controlled, and the reduction of forest land and water areas is expected to be limited to 2% in the future. Among the agricultural land, less than 5% is arable land, but the arable land is changing drastically, and the overall trend is rapidly declining. In one Chinese city, the management of agricultural land is achieved through the establishment of a basic farmland protection system and the designation of basic farmland transformation zones. However, in the context of rapid urbanization, this reactive system of agricultural land control is seriously failing. The actual scale of population growth in a Chinese city exceeds expectations and the total economic volume expands dramatically, which inevitably intensifies the conflict between the expansion of agricultural land and construction land around the city, and the loss of arable land, garden land, and grassland is the inevitable result. The land use map of 2014 is used as input, and the data of each land use type in 2025 predicted by the SD model is used as input to the transfer matrix of the CA model to obtain the land use prediction map of 2025. This simulation scenario does not consider the effects of policies and planning, etc., and follows the current land use change trend. Table 3 integrates and compares the spatial changes of land use pattern in 2004, 2008, 2014, and the projected 2025. The overall land use structure of a city in China is basically unchanged, with construction land and forest land dominating, but there are obvious changes in the quantity and spatial distribution of each land use type. The most obvious is the outward expansion and connectivity of construction land through the encroachment of parkland and forest land, and this trend is gradually increasing, and in 2025, construction land will be more concentrated, and the distribution of agricultural land types will be more fragmented.Table 3
Land use type dynamic attitude.
Land use typeAverage dynamic attitude (%)2004-20082008-20142014 - 2025Cropland-2.38-0.14-3.57Garden land-3.61-3.03-2.97Woodland-2.620.18-0.91Grassland0.00>100-5.03Urban land2.071.852.02Transportation land7.46-0.192.45Waters0.10-0.94-1.54


## 4.1. Experiment Setup

In order to verify the feasibility of the proposed algorithm, the category images of an urban land space remote sensing image dataset in China are selected for the training and testing of the network, respectively. Among them, UCASAOD dataset is produced by the University of Chinese Academy of Sciences, which contains 1000 remote sensing images with 7482 samples, with more concentrated data and more uniform distribution of target directions. RSOD dataset contains 446 remote sensing images with 4993 samples, with diverse brightness and contrast in the images, and interference such as occlusion, shadow, and distortion. During the experiment, 10% of the images of the two datasets were randomly selected as test samples, 10% of the remaining 90% of the images were selected as the validation set, and the rest were used as the training set, and the configuration of the experimental environment is shown in Table1.Table 1
Software and hardware environment configuration.
ParametersConfigurationOperating systemUbuntu 20.04.2 LTSCPUInter Core i9-10900KFCPU memory62.7 GGPUGeForce RTX 3080GPU memory10GProgramming languagePythonProgramming platformsPycharmGraphics acceleration environmentCUDA11.2, CuDNN7.6Deep learning frameworkPytorch 1.7.0, Torchvision 0.8.1In the experiments, the downsampling rateR is 4, and the Adam optimizer is used for iterative training, and the input images are uniformly scaled to a resolution of 512×512. The initial learning rate is set to 1e-3, and the batch size is 4. After training 50 epochs, the learning rate is reduced to 1e-4, and then, 50 epochs are trained. In addition, to speed up the convergence, the backbone of ResNet-50 is trained using the pretrained weights obtained from the ImageNet classification task.


## 4.2. Experimental Results

The comparison results of several network structures before and after the improvement on the UCFAS-AOD test set are shown in Figures5 and 6. The detection accuracy of the improved network is significantly improved while maintaining certain computational efficiency. CenterNet-T with null convolution improves the detection accuracy by 0.59 percentage points compared with CenterNet-C with standard 1×1 convolution, and CenterNet-SC and CenterNet-ST improve the detection accuracy by 1.34 percentage points and 1.46 percentage points compared with CenterNet-C and CenterNet-T, respectively. CenterNet-ST, which has the best overall effect, improves the detection accuracy by 16 percentage points and 0.71 percentage points over CenterNet and CenterNet-SC, respectively, so the CenterNet-ST topology is subsequently used as the AFF-CenterNet algorithm model.Figure 5
Comparison of different improved structure FPS.Figure 6
Comparison of different improved structure map.Three sets of representative images were selected from the dataset for testing and the before and after improvement comparison graphs. Because the feature extraction layer of the original network has gone through the “encoding-decoding” structure, the semantic information of small targets is seriously lost, which makes it difficult to detect small targets with similar background color, while the parallel-layer feature sharing network of AFF-CenterNet effectively fuses deep and shallow features to reduce the semantic loss, and the small targets with low contrast with the background are still detected. Small targets with low background contrast can still be effectively recognized.Table2 shows the confusion matrix of the accuracy of the first-level ground class, and the overall accuracy and kappa coefficient are used to determine the accuracy of the ground class. The overall accuracy of primary land class is 86.0%, and the kappa coefficient is 0.75; the overall accuracy of secondary land class is 73.9%, and the kappa coefficient is 0.69. Among them, the accuracy of commercial land user is 95.1%, and the accuracy of park and green space user is 97.1%; the accuracy of institutional land and medical and health land classification is lower, and the accuracy of the former user is 15.7%, and the latter user is 11.1%. The accuracy of the former is 15.7%, and the accuracy of the latter is 11.1%.Table 2
Confusion matrix for level 1 terrestrial accuracy.
Type of land useResidential landCommercial landIndustrial, mining, and storage landTransportationLand for public administration and public servicesUser accuracy (%)Residential land357315717470509233.8Commercial land0103290080492.7Industrial, mining, and storageland0010184010498.9Transportation4680382669044.0Land for public administration and public services8095709767924844693.9Producer accuracy (%)73.693.476.645.760.633.8Overall accuracy (%)86.0Kappa coefficient0.75Figures7, 8, and 9 present the statistical parameters of the comparison between historical and simulated values of each land use type during 2014-2020. The MRE of forest land, grassland, urban land, transportation land, and water can basically be controlled within or around 5%, while the MRE of cropland and garden land are 11.02% and 7.53%, respectively. The Nash coefficients of cropland are negative, while the Nash coefficients of other land types are greater than 0. The R2 of grassland, urban land, and water reaches 0.99, 0.70, and 0.84, respectively, while the R2 of other land types are not satisfactory, especially for cropland and forest land, due to the poor simulation effect of early data for model validation. Although the simulation results of the model are not satisfactory, they can still be used. Taking the model fit validation curve of cropland in Figures 7, 8, and 9 as an example, the relative error from 2009 to 2012 reached about 30%, and the simulation effect after that was very good, with only 2.24% MRE. A certain degree of error is allowed in system dynamics models, which are often used to study changes in complex systems over time series. The results of the model validation showed that the model could meet the needs of the next study.Figure 7
Model validation MRD statistical parameters.Figure 8
Model validationR2 statistical parameters.Figure 9
Model validation of NSE statistical parameters.Table3 presents the average annual dynamic attitude of each land use type. Both urban land and transportation land in construction land show a slowing trend in growth rate. Under a series of land ecology construction policies in a city in China, forest land and water areas, which are the objects of ecological value protection, have been protected to a certain extent, and soil erosion and habitat encroachment have been effectively controlled, and the reduction of forest land and water areas is expected to be limited to 2% in the future. Among the agricultural land, less than 5% is arable land, but the arable land is changing drastically, and the overall trend is rapidly declining. In one Chinese city, the management of agricultural land is achieved through the establishment of a basic farmland protection system and the designation of basic farmland transformation zones. However, in the context of rapid urbanization, this reactive system of agricultural land control is seriously failing. The actual scale of population growth in a Chinese city exceeds expectations and the total economic volume expands dramatically, which inevitably intensifies the conflict between the expansion of agricultural land and construction land around the city, and the loss of arable land, garden land, and grassland is the inevitable result. The land use map of 2014 is used as input, and the data of each land use type in 2025 predicted by the SD model is used as input to the transfer matrix of the CA model to obtain the land use prediction map of 2025. This simulation scenario does not consider the effects of policies and planning, etc., and follows the current land use change trend. Table 3 integrates and compares the spatial changes of land use pattern in 2004, 2008, 2014, and the projected 2025. The overall land use structure of a city in China is basically unchanged, with construction land and forest land dominating, but there are obvious changes in the quantity and spatial distribution of each land use type. The most obvious is the outward expansion and connectivity of construction land through the encroachment of parkland and forest land, and this trend is gradually increasing, and in 2025, construction land will be more concentrated, and the distribution of agricultural land types will be more fragmented.Table 3
Land use type dynamic attitude.
Land use typeAverage dynamic attitude (%)2004-20082008-20142014 - 2025Cropland-2.38-0.14-3.57Garden land-3.61-3.03-2.97Woodland-2.620.18-0.91Grassland0.00>100-5.03Urban land2.071.852.02Transportation land7.46-0.192.45Waters0.10-0.94-1.54


## 5. Conclusion

With the economic development of China, the differences between various regions are gradually increasing, the spatial classification of land use planning and construction has become an important measure and means to enhance the national economy and social economy, and only by vigorously promoting urban and rural construction efforts can we achieve the goal of building a well-off society and maintaining social stability. In the process of construction, land resources should be strictly managed, reasonable planning should be carried out, and corresponding measures should be taken to deal with various illegal land occupation and illegal land use in accordance with the law, so that land resources can be developed sustainably and make them fully supplied with land use space classification planning and construction. With the joint efforts of the government, the public, and the enterprises, the use of land resources will become more and more reasonable, the level of land management will be enhanced, and the speed of land use space classification planning and construction will become faster and faster.This paper proposes that the land use spatial classification planning based on multivision has the characteristics of large scale, short period, and multiple data types, which can complete the collection and investigation of land resources and other resource information in a short time, ensure the efficiency of work and reduce the work cost, realize the real-time monitoring of land resources use and land use spatial classification quality, and provide a series of information for land use planning. It can also provide a series of information as reference for the formulation of land use planning, which is very important for the efficient use of land resources. In the future, we plan to carry out research on spatial land use classification planning methods based on recurrent neural networks.



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*Source: 9300278-2022-08-28.xml*