# Optimizing Mobile Vision Transformers for Land Cover Classification
## 2 Previous Work
In [12], an open-source benchmark to evaluate deep learning models for image classification in Earth observation (EO) was proposed. The authors conducted a comprehensive analysis of models from ten different deep learning architectures by comparing them to a variety of multi-class and multi-label image classification tasks from 22 datasets along with a related repository that can help build the foundation of guiding design principles for evaluating and documenting machine learning approaches in the different domains of EO. The process mostly used pre-trained models with a significantly large number of trainable parameters. In [26], [PERSON] et al. proposed the RemoteCLIP model, which has rich semantics and aligned text embeddings for seamless downstream application. The researchers evaluated the model based on 16 datasets. The results indicated that RemoteCLIP consistently outperformed baseline foundation models across different model scales. In [27], the authors proposed a model based on MobileNetV3 for remote sensing image classification. They aimed to develop a model with fewer parameters so that it could be run on portable devices with reasonable accuracy. After comparing with other models by conducting experiments on different datasets, the results showed that the proposed model is not only lightweight but also has improved accuracy. The authors of [28] proposed a model that enables and enhances a transfer learning model (Xception) for scene classification. After setting up the model, they evaluated it using datasets, including EuroSAT, UC-Merced, and AID. The results indicated that the proposed model outperformed state-of-the-art methods and had better accuracy and computational efficiency in each case. [PERSON] et al. [29] proposed the RSCNet model, with the aim of improving the efficiency of remote sensing scene classification through lightweight neural networks. The model was evaluated by using the AID and UC-Merced datasets. The results showed that the RSCNet model has higher classification accuracy and faster processing speed on the two datasets, which provides the basic theory and key technical support for conducting fast classification of large amounts of remote sensing images. [PERSON] et al. [30] proposed the BPKM model, with aim of building a lightweight network that can be applied on a mobile terminal or embedded device. After setting up the network, they evaluated the model by using the AID and UC-Merced datasets. The results showed that the BPKM model can make great classification of similar categories in aerial scene images, and the authors reduced the size of the network by about 24 times compared with popular networks. [PERSON] et al. [31] explored improving the classification performance while also avoiding drastically increasing the complexity of the model by using an AMB-CNN model for remote sensing image scene classification. They conducted experiments on the proposed model by using datasets, including AID, UC-Merced, NWPU45, and RSSCN7. Compared with some state-of-the-art methods, the number of parameters of the proposed method is only 5.6 million, and it has a great advantage in classification accuracy. In [32], [PERSON] et al. proposed a novel scene classification model that integrates multi-source heterogeneous features, addressing the challenges of difficult distinctions of socio-economic attributes, visual-semantic discrepancies, intra-class differences, and high inter-class similarity. The experimental results indicated that the proposed model was better at solving the first three challenges. Since deep learning requires a huge number of training samples to ensure the optimal learning procedure, [PERSON] et al. [33] tried to address the issue of limited training samples in real-life situations for land use and land cover deep learning classification. The research focused on considering the fraction of multi-spectral data and evaluated the exemplary CNN architectures with different tuning variants along with additional layers before classification.