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Wetland park environmental data monitoring based on GIS high resolution images and machine learning

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Abstract

High-resolution remote sensing images not only improve the processing quality of image information, but also become more effective, accurate, and clear in specific practical applications such as feature recognition, classification, and object detection. The construction and development of urban wetland parks have become a new trend in urban construction both domestically and internationally. This article first builds a map symbol library and formulates data processing rules based on ArcGIS software, and designs a high-resolution GIS image flowchart. Adding jump connections to the convolutional neural network can effectively improve the performance of the overall network model. By utilizing the advantages of jump connections in convolutional neural networks, a more dense connection network is proposed through multiple uses and improvements in convolutional neural networks. Finally, design a wetland park environmental data monitoring platform and analyze and process the data based on the wetland park environmental monitoring indicators. The research results indicate that the algorithm proposed in this paper retrieves most high-frequency information without adding too much noise. The edge sharpening effect of traditional algorithms is relatively poor, and the model proposed in this paper outperforms other super-resolution reconstruction algorithms, improving the performance of the model in terms of visual effects in remote sensing image reconstruction. The typical application scenario of wetland protection and management based on temporal and spatial data constructed in this article has achieved large-scale, fast, and accurate analysis of wetland resource conditions, and verified the application support capabilities of cloud computing platforms.

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  1. Zhejiang Vocational Academy of Art, Hangzhou, 310053, Zhejiang, China

    Hanrong Zheng

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  1. Hanrong Zheng
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Correspondence to Hanrong Zheng.

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Zheng, H. Wetland park environmental data monitoring based on GIS high resolution images and machine learning. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08898-z

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