Toward Agricultural Cultivation Parcels Extraction in the Complex Mountainous Areas Using Prior Information and Deep Learning

Accurately determining the spatial position and distribution structure of agricultural cultivation parcels (ACPs) is essential for regional agricultural planning and food security. Currently, utilizing deep learning technology based on very high resolution remote sensing imagery has proven effective for intelligent parcel extraction. However, relying solely on the model output, especially from single-task models in mountainous regions with complex, heterogeneous, and fragmented smallholder agriculture, remains questionable. To address this challenge, leveraging geographical prior knowledge is critical. This article proposes using the deep semantic segmentation algorithm in conjunction with comprehensive prior strategies. An improved densely connected link network (D-LinkNet) is employed to delineate the parcels, while geographical zoning, coarse spatial scope, stratification strategy, and homogeneity checking are exerted to understand regions, facilitate samples, reduce interferences, decompose objects, and identify undersegmentation. The proposed framework was validated in Jiangjin district, Chongqing of China, using Gaofen-2 images as the vital data. Compared to the method relying solely on deep learning, our method achieved superior performance with an overall accuracy of 0.924, Kappa coefficient of 0.847, $F1$ score of 0.921, and IoU exceeding 0.8. Moreover, the results demonstrated high accuracy in the individual geometric precision of parcel. Over 1.23 million parcels were identified, comprising 77% cultivated land and 23% garden land. The areal proportion of paddy fields, drylands, and pepper gardens approximated 1:1:1, consistent with statistical data. This method offers a feasible approach for finely extracting agricultural parcels.