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. 2024 Apr 21:12:102719.
doi: 10.1016/j.mex.2024.102719. eCollection 2024 Jun.

Development of a convolutional neural network to accurately detect land use and land cover

Carolina Acuña-Alonso  1   2 , Mario García-Ontiyuelo  1 , Diego Barba-Barragáns  1 , Xana Álvarez  1
Affiliations

Development of a convolutional neural network to accurately detect land use and land cover

Carolina Acuña-Alonso et al. MethodsX. .

Abstract

The detection and modeling of Land Use and Land Cover (LULC) play pivotal roles in natural resource management, environmental modeling and assessment, and ecological connectivity management. However, addressing LULCC detection and modeling constitutes a complex data-driven process. In the present study, a Convolutional Neural Network (CNN) is employed due to its great potential in image classification. The development of these tools applies the deep learning method. A methodology has been developed that classifies the set of land uses in a natural area of special protection. This study area covers the Sierra del Cando (Galicia, northwest Spain), considered by the European Union as a Site of Community Interest and integrated in the Natura 2000 Network. The results of the CNN model developed show an accuracy of 91 % on training dataset and 88 % on test dataset. In addition, the model was tested on images of the study area, both from Sentinel-2 and PNOA. Despite some confusion especially in the residential class due to the characteristics in this area, CNNs prove to be a powerful classification tool.•Classifications based on a CNN model•LULC are classified into 10 different classes•Training and test accuracy are 91 % and 88 %, respectively.

Keywords: CNN-LULC Predictor; Deep learning; Image classification; Image prediction; Sentinel-2.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image, graphical abstract
Graphical abstract
Fig. 1
Fig. 1
Flowchart of the method used.
Fig. 2
Fig. 2
Data augmentation example, where the Original image represents the unmodified area under study and the Augmented image is rotated 180° from the original image.
Fig. 3
Fig. 3
Architecture of deep learning convolutional neural network.
Fig. 4
Fig. 4
Location of Serra do Cando in Pontevedra (NW Spain). The map coordinate system is EPSG:25829 ETRS89/UTM zone 29 N.
Fig. 5
Fig. 5
Confusion matrix drawn from the 15 % test data (4096 images).
Fig. 6
Fig. 6
Training and validation accuracy and loss.
Fig. 7
Fig. 7
Predictions on the EuroSat test dataset.
Fig. 8
Fig. 8
Predictions on the study area (Sentinel-2).
Fig. 9
Fig. 9
Predictions on the study area (PNOA).
See this image and copyright information in PMC

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