Abstract: Arable land in hilly and mountainous areas exhibits small, narrow, and complex structures with blurred boundaries, posing challenges in extracting arable land information quickly and accurately. Hence, this study proposed a model for extracting the information on sloping arable land based on improved DexiNed and DeepLabv3+ networks in a cascade connection. First, the backbone network of the DeepLabv3+ model uses MobileNetV2 to replace the original Xception model. A closely related low-level information extraction method preliminarily fuses the lower- and higher-level information as the input of the original low-level information. Second, the original atrous spatial pyramid pooling (ASPP) module of the DeepLabv3+ model is optimized through dilated convolution, with dilation rate values of 2, 4, 8, and 16. Third, cascaded edge detection technology enables the interconnection of arable land edges and semantic features. The proposed model was applied to extract information on arable land in the Lufeng Dinosaur Valley in Yunnan Province using the GF-2 image as the data source. The results show that the proposed model with an improved architecture and algorithm identified the arable land more accurately, with the extraction results closely matching the image with real arable land annotated. With reduced extraction missing and errors, the proposed model exhibits enhanced accuracy and stability overall.