Abstract
This paper proposes and implements a novel framework that uses deep learning to classify and visualise satellite land images. The proposed framework uses deep learning to accurately detect features in satellite images, automating the extraction of useful information from large datasets. It involves building and training a deep learning module using various algorithms and settings to improve geographic data processing. Overall, this paper contributes to the field of spatial image processing and highlights the potential benefits of deep learning in land-use mapping and related applications. The implementation of this technology can increase agricultural productivity, improve natural disaster management, and protect the environment.
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Liao, X., Huang, X., Huang, W. (2023). A Deeping Learning Based Framework and System for Effective Land Use Mapping. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2023. Lecture Notes in Computer Science, vol 14166. Springer, Cham. https://doi.org/10.1007/978-3-031-43815-8_17
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