Abstract

MONTIEL GONZALEZ, Rodolfo et al. Classification of land use and vegetation with convolutional neural networks. Rev. mex. de cienc. forestales [online]. 2022, vol.13, n.74, pp.97-119.  Epub Dec 09, 2022. ISSN 2007-1132.  https://doi.org/10.29298/rmcf.v13i74.1269.

The classification of land use and vegetation is a complex exercise difficult to perform with traditional methods, thus deep learning models constitute a viable alternative because they are highly capable of learning this complex semantics, a trait which allows their application in the automatic identification of land use and vegetation, based on spatiotemporal patterns derived from their appearance. The objective of this study was to propose and evaluate a deep learning convolutional neural network model for the classification of 22 different land covers and land use classes located in the Atoyac-Salado basin. The proposed model was trained using digital data captured in 2021 by the Sentinel-2 satellite; a different combination of hyperparameters was applied in which the accuracy of the model depends on the optimizer, the activation function, the filter size, the learning rate and the batch size. The results provided an accuracy of 84.57 % for the data set. A regularization method called Dropout was used to reduce overadjustment, with great effectiveness. It was proven with sufficient accuracy that deep learning with convolutional neural networks identifies patterns in the reflectance data captured by Sentinel-2 satellite images for land use and vegetation classification in intrinsically difficult areas of the Atoyac-Salado basin.

Keywords : Machine learning; automatic classification; Atoyac-Salado basin; Sentinel-2 images; artificial intelligence; remote sensing.