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Land-Use Classification Using Convolutional Neural Networks

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Abstract

Convolutional neural networks (CNNs) have been used in several classification tasks. This study aims to evaluate the performance of CNN methods for land-use classification. CNN-based model was evaluated on aerial orthophoto data for land-use scene classification. Ground-truth data set containing 25 253 records with known land-use category were used to train the CNN model to solve a practical issue. The overall accuracy of the best model on the test data set was 94.00%. The obtained results indicated that CNN mode showed high accuracy and is suitable for land-use classification tasks.

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Authors and Affiliations

  1. Department of Remote Sensing, Engineering Research Institute Ventspils International Radioastronomy Centre, Ventspils University of Applied Sciences, Ventspils, Latvia

    A. M. Stepchenko

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  1. A. M. Stepchenko
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Correspondence to A. M. Stepchenko.

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Stepchenko, A.M. Land-Use Classification Using Convolutional Neural Networks. Aut. Control Comp. Sci. 55, 358–367 (2021). https://doi.org/10.3103/S0146411621040088

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