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It is of great significance for agricultural resources monitoring to accurately extract cultivated land map information from remote sensing images.To improve the defects of traditional models for extracting cultivated land and solve the problem that most FCN model pays more attention to accuracy but ignores the consumption of time and computing resources, a lightweight model for extracting cultivated land map spots was established based on FCN (LWIBNet), and post-processing combined with mathematical morphology algorithm were used to carry out automatic extraction of cultivated land information. LWIBNet drew on the advantages of lightweight convolutional neural network and U-Net model, and it was built with the core of Inv-Bottleneck (composed of deep separable convolution, compression-excitation block and inverse residual block) and the skeleton of efficient coding-decoding structure. Compared LWIBNet with the cultivated land extraction effect of traditional model, and the computational resources and time consumption of classical FCN model.The results showed that LWIBNet was 12.0% higher than the Kappa coefficient of the best traditional model, and compared with U-Net, LWIBNet had 96.5%, 87.1%,78.2% and 75% less parameters, calculation, training time and split time-consuming, respectively. Moreover, the segmentation accuracy of LWIBNet was similar to that of the classical FCN model.