Опубликована: Дек. 10, 2023

Forests cover about 30% of the Earth's surface, having a significant global impact on climate and atmosphere. A change (i.e., forest loss) in world's forests has been brought by factors, such as rising population, increased urbanization, environmental pollution due to economic activities. Consequently, loss mapping monitoring are vital. Convolutional Neural Networks (CNNs) among most utilized segmentation algorithms for deforestation mapping. However, CNNs may be more prone model variance, over-sensitivity, lack generalizability. Thus, new concepts, Cosine Similarity can investigated an alternative approach current extensively CNNs. this study, we develop propose SCS-UNet precise utilizing satellite imagery Sentinel-2 South America. The results illustrated that proposed algorithm exhibited least training time complexity compared other implemented models, UNet, Attention R2UNet, ResUNet, Swin UNet+++, TransUNet, TransUNet++, while resulting comparable statistical U-Net model.

Язык: Английский