Remote Sensing Data Processing of Urban Land Using Based on Artificial Neural Network

Zhang, Han

doi:10.1155/2022/6994179

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…The F1 scores for the CA-Markov and LR models were particularly low, while the MLP-ANN and MLP-LSTM models, which had higher overall accuracy, still only achieved F1 scores of around 73%. This seems to be because urban areas are more affected by human activities and policies, as confirmed by other studies (Xu et al 2022 ; Zhang and Bin 2022 ). To improve the accuracy of land use predictions, future researches need these factors.…”

Section: Discussionsupporting

confidence: 76%

Spatiotemporal change and prediction of land use in Manasi region based on deep learning

Wang

Yin

Liu

et al. 2023

Environ Sci Pollut Res

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The Manasi region is located in an arid and semi-arid region with fragile ecology and scarce resources. The land use change prediction is important for the management and optimization of land resources. We utilized Sankey diagram, dynamic degree of land use, and landscape indices to explore the temporal and spatial variation of land use and integrated the LSTM and MLP algorithms to predict land use prediction. The MLP-LSTM prediction model retains the spatiotemporal information of land use data to the greatest extent and extracts the spatiotemporal variation characteristics of each grid through a training set. Results showed that (1) from 1990 to 2020, cropland, tree cover, water bodies, and urban areas in the Manasi region increased by 855.3465 km2, 271.7136 km2, 40.0104 km2, and 109.2483 km2, respectively, whereas grassland and bare land decreased by 677.7243 km2 and 598.5945 km2, respectively; (2) Kappa coefficients reflect the accuracy of the mode’s predictions in terms of quantity. The Kappa coefficients of the land use data predicted by the MLP-LSTM, MLP-ANN, LR, and CA-Markov models were calculated to be 95.58%, 93.36%, 89.48%, and 85.35%, respectively. It can be found that the MLP-LSTM and MLP-ANN models obtain higher accuracy in most levels, while the CA–Markov model has the lowest accuracy. (3) The landscape indices can reflect the spatial configuration characteristics of landscape (land use types), and evaluating the prediction results of land use models using landscape indices can reflect the prediction accuracy of the models in terms of spatial features. The results indicate that the model predicted by MLP-LSTM model conforms to the development trend of land use from 1990 to 2020 in terms of spatial features. This gives a basis for the study of the Manasi region to formulate relevant land use development and rationally allocate land resources.

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Section: Discussionsupporting

confidence: 76%

Spatiotemporal change and prediction of land use in Manasi region based on deep learning

Wang

Yin

Liu

et al. 2023

Environ Sci Pollut Res

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Urban sprawl and thermal response in the Guangdong-Hong Kong-Macao Greater Bay Area, 2000–2020

Yuan,

Chen,

Zhang

et al. 2024

Environ. Res. Commun.

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The urban heat island (UHI) environment is closely related to the daily life of residents, and the impact of UHI is further expanding in the era of rapid urbanization, which is particularly obvious in the Beijing-Tianjin-Hebei urban agglomeration and Guangdong-Hong Kong-Macao Greater Bay Area. To explore the connection between UHI and urban expansion, this study takes Guangdong, Hong Kong, and Macao Greater Bay Area as the study area, based on Google Earth Engine platform and Landsat satellite data. The mean standard deviation was used to analyze the heat island zoning in the Guangdong-Hong Kong-Macao Greater Bay Area from 2000 to 2020, and the direction and type of urban expansion in the Guangdong-Hong Kong-Macao Greater Bay Area were investigated by standard deviation ellipse and LEI, to explore the link between the type of urban expansion and the heat island region through geospatial analysis methods. The results of the study show that: (1) From 2000 to 2020, the proportion of the area of heat island regions generally showed an upward trend, with the largest increase in heat island regions from 2015 to 2020, and the spatial distribution of heat island regions is characterized by the Pearl River Delta as the center, radiating in all directions. (2) The speed of movement of urban centroids in the Guangdong-Hong Kong-Macao Greater Bay Area decreases in 2000-2015 and increases in 2015-2020. The direction of urban expansion in the first five years is dominated by the northeast, and the direction of urban expansion in the latter 15 years is generally dominated by the southwest, with the type of urban expansion dominated by the fringe type, and the enclave type of expansion accounting for a smaller proportion.

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An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images

Lu,

Peng,

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Remote Sensing Data Processing of Urban Land Using Based on Artificial Neural Network

Cited by 3 publications

References 29 publications

Spatiotemporal change and prediction of land use in Manasi region based on deep learning

Spatiotemporal change and prediction of land use in Manasi region based on deep learning

Urban sprawl and thermal response in the Guangdong-Hong Kong-Macao Greater Bay Area, 2000–2020

An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images

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