Ma et al. (2021) used ResNet-50+FPN(He et al. 2016; Lin et al. 2017) to carry a semantic segmentation neural network, demonstrating the feasibility of deep learning in large-scale AGs mapping. Chen et al. (2021) successfully extracted AGs with the help of the classic semantic segmentation network UNet, and since then, some segmentation models specifically designed for AGs mapping tasks have been proposed(He et al. 2023; Liu et al. 2023). Although these models are based on classical convolutional neural networks (CNNs) and improved with the help of advanced components in CNNs to achieve better results, there are still three main problems in AGs mapping: difficult to extract spatially dense distribution, algorithm maladaptation, and lack of trainable data. On the other hand, the intrinsic relationship between the visual features of AGs and the network architecture has not been sufficiently explained. How to implement an efficient AGs segmentation model based on the unique or more niche characteristics of AGs still needs to be supplemented more.

Ma等人（2021）采用ResNet-50+FPN（He等人2016；Lin等人2017）构建了一个语义分割神经网络，展示了深度学习在大规模农田地块映射中的可行性。陈等人（2021）成功地利用经典的语义分割网络UNet提取了农田地块，并且此后还提出了一些专门针对农田地块映射任务设计的分割模型（He等人2023；Liu等人2023）。尽管这些模型以经典卷积神经网络（CNNs）为基础，并借助CNNs中的先进组件做出改进取得了更好的效果，但农田地块映射仍存在三个主要问题：难以提取空间密集分布、算法不适应性以及缺少可训练数据。另一方面，农田地块视觉特征与网络架构之间的内在关系还没有得到充分的解释。如何基于农田地块的独特或更专业的特性来实现高效的农田地块分割模型，仍需要更多的补充。