# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 1 Introduction
[3] processed GF-2 satellite data and SAR data using SubFus pixel-level image fusion methods, enhancing the classifier's performance in tea plantation mapping. Moreover, since tea plantations are mostly distributed in hilly and mountainous areas with loose and fragmented layouts, the introduction of multi-source data from different medium- and low-resolution scale images can result in single pixels including multiple land covers, affecting classification results. Therefore, fine classification of tea plantations requires high-resolution images. Unmanned aerial vehicle (UAV) images provide ultra-high-resolution information, offering a reference for creating tea plantation labels and improving classification accuracy. For example, [PERSON] et al. [17; 18] used UAVs equipped with digital cameras and light detection and ranging (LiDAR) devices to obtain ultra-high precision (0.2 m) radar and optical image feature data, showing that UAV remote sensing has great potential in tea plantation identification. In recent years, deep learning technology, due to its powerful ability to extract high-level semantic features from images, has been closely integrated with remote sensing, being applied to almost all types of images and remote sensing applications, with related research growing exponentially every year [19; 20; 21]. Compared to traditional machine learning algorithms, neural networks have higher complexity, strong data processing capabilities, and can automatically extract high-level features from remote sensing data. Typical network structures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are highly efficient and accurate in processing tea plantations with typical strip spatial arrangement features and spectral features with periodic changes. For example, [PERSON] et al. [22] proposed an object-based CNN method to automatically extract high-level features of tea leaf images obtained through image segmentation, achieving good results. [PERSON] et al. [23] constructed an R-CNN model based on the U-Net structure and gated recurrent unit (GRU) module, feeding the extracted spatial feature vectors into the time series GRU module, significantly improving tea plantation identification accuracy compared to other traditional machine learning algorithms and neural networks. The U-Net network, built on a fully convolutional neural network (FCN), is widely used in the field of image recognition due to its excellent multi-scale feature extraction and deep and shallow feature fusion capabilities [24]. Based on this, many excellent variants of U-Net have emerged. For example, the attention U-Net developed by [PERSON] et al. [25] and the Unet++ proposed by [PERSON] et al. [26] have achieved good results in medical image classification. Given the powerful performance of these models, their performance in remote sensing image classification is highly anticipated. Despite significant advancements in remote sensing and deep learning, there remains a lack of systematic evaluation of the latest attention mechanism-based semantic segmentation models for tea plantation mapping. Additionally, there is also limited research on the evaluation of the impact of different terrain factors on the accuracy of tea plantation mapping. To address these gaps, this study evaluates deep learning models for tea plantation mapping using multi-temporal Sentinel-1, Sentinel-2, and DEM images. Three models--U-Net, SE-UNet, and Swin-UNet--were compared. U-Net's limitations in capturing long-range dependencies are addressed by SE-UNet's attention mechanism and Swin-UNet's transformer-based architecture. The models were tested with multi-source data to assess their impact on mapping accuracy. The study also examines the spatial distribution of tea plantations and mapping accuracy under different terrain conditions. The rest of this paper is organized as follows. Section 2 describes the study area and datasets. Section 3 specifically describes the classification methods and steps for tea plantation identification.