Automation of Land Use Planning Based on Geoinformation Modeling

Zudilin, S. N.; Iralieva, Yu S

doi:10.1088/1755-1315/720/1/012039

Cited by 4 publications

(3 citation statements)

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“…Land-use mapping based on remote sensing imagery has become increasingly important in providing essential information for many applications, such as urban planning [ 1 , 2 ], ecological management [ 3 , 4 ], regional design of crop cultivation [ 5 , 6 ] and environmental assessment [ 7 ]. With the rapid development of remote sensing technologies, the spatial resolution of imagery has sharply increased over the past decade, providing opportunities to extract more precise land-cover information [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Land-use classification based on high-resolution remote sensing imagery and deep learning models

Hao,

Dong,

Jiang

et al. 2024

PLoS ONE

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High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the performance of these models. In this study, we applied four well-established and robust deep learning models (FCN-8s, SegNet, U-Net, and Swin-UNet) to an open benchmark high-resolution remote sensing dataset to compare their performance in land-use mapping. The results indicate that FCN-8s, SegNet, U-Net, and Swin-UNet achieved overall accuracies of 80.73%, 89.86%, 91.90%, and 96.01%, respectively, on the test set. Furthermore, we assessed the generalization ability of these models using two measures: intersection of union and F1 score, which highlight Swin-UNet’s superior robustness compared to the other three models. In summary, our study provides a systematic analysis of the classification differences among these four deep learning models through experiments. It serves as a valuable reference for selecting models in future research, particularly in scenarios such as land-use mapping, urban functional area recognition, and natural resource management.

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Section: Introductionmentioning

confidence: 99%

Land-use classification based on high-resolution remote sensing imagery and deep learning models

Hao,

Dong,

Jiang

et al. 2024

PLoS ONE

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“…The result of the work was a geospatial data infrastructure based on industry requirements and existing standards. Zudilin and Iralieva (2021) examines the implementation of automated design of land plots for agricultural purposes based on geoinformation systems in problem areas, for which a geographic information model was developed. Kudryavtsev (2021) studies the increase in the efficiency of agriculture using GIS, as well as finding ways to create new convenient methods for managing both production technologies and natural resources, taking into account the peculiarities of soil fertility and environmental factors.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Creating a Geographic Information System for Transport Infrastructure Facilities

Talipova,

Morozova,

Pestova

et al. 2023

IJTech

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This study examines the methodology for creating a geographic information system of transport infrastructure objects -from obtaining initial data to storing the data itself. The article defines the classification of objects in the transportation system, the procedure for collecting and processing initial data, as well as the sequence of data processing for transmission to the GIS environment. A study was conducted on the dependence of response time on the current number of elements in a database table with indexed and un-indexed data. Until about the 1000 th element, it was determined that the sampling rate is higher by "direct search", but after that, a smooth linear growth begins to a sharp peak and a nonlinear increase in search time. Thus, the support of database management system functions for working with spatial data is important for its functioning in the context of geometric data aggregation tasks.

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“…CBERS optical remote sensing spectral information is rich, but the spatial resolution is low, and it is easily affected by weather, while ASAR data have high spatial resolution and are less affected and restricted by weather. Therefore, the fusion of CBERS and ASAR remote sensing data can make CBERS optical remote sensing data and ASAR radar remote sensing data form complementary advantages, thereby improving the ability of target recognition, analysis, and interpretation [ 17 – 19 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Land Use Planning Based on Multisource Remote Sensing Data

Wang

Pei

Lei

2022

Computational Intelligence and Neuroscience

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Land use changes are analyzed correctly, a series of improvements according to the changes are carried out appropriately, the relationship between land use development and economic and human survival is handled correctly, and the healthy and orderly development of the entire society is promoted. Aiming at the combination of multisource remote sensing data and monitoring changes in land planning, this study uses CBERS data and ASAR data as multisource remote sensing data sources to conduct in-depth research and discussion on the land use change in this area and uses the HPF pixel-level fusion method for data fusion to generate HPF. The data are integrated, and then, the CBERS data and HPF fusion data are used to extract the land use type information of Zhenning County, respectively, and a confusion matrix is built based on the field sample points to verify the accuracy, compare and analyze the relative error of the land use type information extraction before and after data fusion, and evaluate the CBERS data. Regarding the extraction effect of land use type information of fusion data with HPF, the results show that the two kinds of remote sensing data have good effects in extracting water body type information, and the accuracy has reached 100%. Using multisource remote sensing image processing can well summarize and analyze land use changes and make the changes in various indicators in the study area. Accurate statistics is obtained.

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Automation of Land Use Planning Based on Geoinformation Modeling

Cited by 4 publications

References 0 publications

Land-use classification based on high-resolution remote sensing imagery and deep learning models

Land-use classification based on high-resolution remote sensing imagery and deep learning models

Methodology for Creating a Geographic Information System for Transport Infrastructure Facilities

Research on Land Use Planning Based on Multisource Remote Sensing Data

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