Small Convolutional Neural Networks and Random Forest in Land Use, Land-Use Change and Forestry (LULUCF)

Abstract The focus of this paper is to present an innovative approach to classify land cover, with specific emphasis on Land Use, Land-Use Change and Forestry (LULUCF) monitoring. LULUCF is a sector in greenhouse gas inventory that tracks changes in greenhouse gas levels in the atmosphere due to land use and land-use change. In this study, we employed Deep Learning classifiers and Random Forest to classify land cover/land use in Czechia, adhering to LULUCF regulations. We evaluated the effectiveness of 2D and 3D Convolutions in Convolutional Neural Networks, with varying filter sizes and training methods, alongside the use of Random Forest classifier. We used Sentinel-2 bands with 10 m and 20 m spatial resolution, NDVI, NDVI variance, and SRTM altitude data to create input paths of 5×5 pixels. The results indicate that the 3D model trained with classical training and 3×3-pixel filters achieved the best F1 Score of 0.84. One significant advantage of using convolutional neural networks is their ability to include information from a pixel’s neighbourhood in the classification process, in contrast to solely considering the pixel itself.