*2.4. Materials*

LSNet is firstly validated using the CIFAR-100 dataset [36], which is one of the most widely used datasets for deep learning reaches. Then, we conduct experiments on the AID dataset [37] to demonstrate the effectiveness of LSNet on the scene classification task.

**CIFAR-100 dataset**: This dataset contains 60,000 images, which are grouped into 100 classes. Each class contains 600 32 × 32 colored images, which are further divided into 500 training images and 100 testing images. The 100 classes in the CIFAR-100 dataset are grouped into 20 superclasses. In this dataset, a "fine" label indicates the class to which the image belongs, while a "coarse" label indicates the superclass to which the image belongs.

**AID dataset**: The AID dataset is a large-scale high-resolution remote sensing dataset proposed by Xia et al. [37] for aerial scene classification. With high intra-class diversity and low inter-class dissimilarity, the AID dataset is suitable as the benchmark for aerial scene classification models. Thirty classes are included, each with 220 up to 420 600 × 600 images. In our experiment, 80% images of each class are chosen as the training data, while the rest 20% are chosen as testing data. Each image is resized to 64 × 64. Some samples of the AID dataset are shown in Figure 5.

**Figure 5.** Some samples of AID dataset.
