Abstract
Earth Observation increasingly uses machine learning to evaluate and monitor the environment. However, the potential of deep learning for studying wilderness is an under-explored frontier. This study aims to give insights into using different architectures (ResNet18, ResNet50, U-Net, DeepLabV3, and FCN), batch sizes (small, medium, and large), and spectral setups (RGB, RGB+NIR, full spectrum) for the classification and semantic segmentation of Sentinel-2 images. The focus is on optimising performance over accuracy using limited computational resources and pre-trained networks widely from the AI community. Experiments are performed on the AnthroProtect dataset, which was developed explicitly for this purpose. Results show that when computation resources are a concern, ResNet18 with 64 or 256 batch size is an optimal configuration for image classification. The U-Net is a sub-optimal solution for semantic segmentation, but our experiments did not identify a clear optimality for the batch size. Finally, different spectral setups highlight no significant impact on the data processing, thus raising critical thinking on the usefulness of neural networks in Earth Observation that are pre-trained with generic data like ImageNet, which is widely used in the AI community.