## 1 Introduction  
Timely and accurate agricultural mapping is an important basis for the country to formulate agricultural policies, and it is of great significance to ensure food security [1, 2]. With the continuous improvement of the spatial resolution of satellite remote sensing images, the visual features of ground objects are more refined, which makes it possible to extract the cultivated land boundary more automatically. However, due to the influence of different satellite payloads, different imaging conditions, complex background features, and changeable time phase, it is still a challenge to obtain the boundaries of cropland with high precision and automation in high-resolution remote sensing images. Although in recent years many researchers have made advancements in this area of R&D activities, it still remains a challenging task to achieve high-quality cropland segmentation results, mainly due to the following reasons. Seasonal: cropland targets are different from traditional objects such as building targets, and crops on cultivated land are growing with the change of time, and there is a strong time correlation. Regional: The regional distribution of cropland is quite different. The cropland in the north is relatively complete, but the cultivated land in the south is relatively broken. Therefore, it is difficult to obtain a universal model. Heterogeneity: In high-resolution semantic segmentation, there are rich texture features, but often fewer spectral features. Fallow land and bare soil will cause confusion in semantic segmentation. Since high-resolution remote sensing images have the characteristics of massive data, scale dependence, and strong spatial correlation, the use of multi-scale convolutional neural networks combined with context information to extract cropland with higher accuracy has become the focus of this paper. In this paper, we propose a new method for cropland semantic segmentation which considers high-resolution images of complex terrain and changeable phase. First, we construct a multi-temporal cropland sample dataset. Then, comprehensively considering the geographic relevance of remotely sensed features, a high-resolution network combined with contextual feature expression (HRNet-CFR) was improved to complete the extraction of cropland elements. It uses the neighborhood surrounding the pixel as the context feature of the pixel to fuse the backbone network features. We modify the input convolution kernel of the first layer of the backbone network, and the improved network can adapt to multi-band spectral data as input. After the backbone network features are fused, the contextual feature representation (CFR) module is connected to obtain the features of the fusion context for semantic segmentation, so as to improve the separability of cropland. In the model training process, in order to solve the imbalance in the frequency of cropland and non-cropland, the loss function of LovaszSoftmax [3] is used to improve the separability of target and background features. Finally, the initial semantic segmentation results are amended by the morphological post-processing algorithm, and the broken spots are ablated to obtain the internal homogeneous cropland. In order to verify the effectiveness of the method, the Jilin-1 satellite data and public GID data [4] are selected for visual qualitative and quantitative evaluation. The average overall accuracy of cropland extraction in the central and western regions of Jilin Province, China, can reach 92.55%. Compared with the current popular Deeplabv3+ and UPerNet [5] semantic segmentation algorithms in the public GID data, the overall accuracy of HRNet-CFR is improved by about 3.4-5.12%. The experimental results show that the improved cropland extraction network model in this paper shows a more superior recognition performance. In summary, the contributions of our approach are three-fold:  
1.