https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/Head https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/assertion https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/provenance https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/pubinfo https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/assertion https://ieeexplore.ieee.org/document/11408843 http://purl.org/dc/terms/contributor https://orcid.org/0000-0002-4135-7634 https://ieeexplore.ieee.org/document/11408843 http://purl.org/dc/terms/creator https://orcid.org/0009-0004-4939-2970 https://ieeexplore.ieee.org/document/11408843 http://purl.org/dc/terms/creator https://orcid.org/0009-0009-0781-3438 https://ieeexplore.ieee.org/document/11408843 http://purl.org/dc/terms/publisher https://ror.org/01n002310 https://ieeexplore.ieee.org/document/11408843 http://purl.org/dc/terms/subject http://aims.fao.org/aos/agrovoc/c_6498 https://ieeexplore.ieee.org/document/11408843 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fair/ff/terms/article https://ieeexplore.ieee.org/document/11408843 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fdof/ontology#FAIRDigitalObject https://ieeexplore.ieee.org/document/11408843 http://www.w3.org/2000/01/rdf-schema#comment This article presents a pseudomultitask (PMT) segmentation neural network (PMTNet) for cropland mapping in mountainous regions using high-resolution remote sensing images. PMTNet extends BsiNet by introducing two key innovations: 1) a pixel-level mask and edge features fusing module using distance features (MEF_D), and 2) a PMT module that replaces the conventional multibranch-task predictions. The MEF_D module leverages spatial attention guided by distance features as weighting indicators to effectively fuse mask and edge features at the pixel level, leading to improved boundary representation. The PMT module, serving as the core prediction component, consists of a single branch dedicated to mask prediction. The two auxiliary tasks—edge detection and distance mapping—are derived directly from the mask output using the Canny edge detecting algorithm and Euclidean distance transformation, respectively. The model was trained and evaluated using cropland samples from Chongqing and Fenghuang, China, based on high-resolution remote sensing images. Comparative experiments were conducted against two representative multitask neural networks (BsiNet and SEANet) and two transformer-based semantic segmentation models (HRFormer + OCR and LRFormer). The results demonstrated that PMTNet consistently outperformed these baselines, achieving the highest scores across multiple metrics, including precision, recall, F1- score, intersection over union, overall accuracy, and the Kappa coefficient—all within a compact model size. Applicability analysis confirmed that PMTNet can effectively identify croplands of diverse types, shapes, and cultivation stages, as long as their boundaries in the images are visually distinguishable https://ieeexplore.ieee.org/document/11408843 http://www.w3.org/2000/01/rdf-schema#label A Pseudomultitask Neural Network Classification Model for Cropland Mapping in Mountainous Areas Using High-Resolution Remote Sensing Images https://ieeexplore.ieee.org/document/11408843 https://schema.org/funder https://ror.org/021nxhr62 https://ieeexplore.ieee.org/document/11408843 https://w3id.org/fdof/ontology#hasMetadata https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 https://ieeexplore.ieee.org/document/11408843 https://www.w3.org/ns/dcat#contactPoint xzhou@mtech.edu https://ieeexplore.ieee.org/document/11408843 https://www.w3.org/ns/dcat#endDate January 2026 https://ieeexplore.ieee.org/document/11408843 https://www.w3.org/ns/dcat#startDate 2025 https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/provenance https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/pubinfo https://orcid.org/0009-0008-8411-2742 http://xmlns.com/foaf/0.1/name Emily Regalado https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 http://purl.org/dc/terms/created 2026-05-01T17:43:55.415Z https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 http://purl.org/dc/terms/creator https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 http://purl.org/dc/terms/license https://creativecommons.org/licenses/by/4.0/ https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 http://purl.org/nanopub/x/introduces https://ieeexplore.ieee.org/document/11408843 https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 http://purl.org/nanopub/x/wasCreatedAt https://nanodash.knowledgepixels.com/ https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 https://w3id.org/np/o/ntemplate/wasCreatedFromProvenanceTemplate https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RACJ58Gvyn91LqCKIO9zu1eijDQIeEff28iyDrJgjSJF8 https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate https://w3id.org/np/RArM5GTwgxg9qslGX-XiQ-KTTUwdoM0KB1YqmT4GqTizA https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/sig http://purl.org/nanopub/x/hasAlgorithm RSA https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/sig http://purl.org/nanopub/x/hasPublicKey MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxzr6UBGMW6c8tegz0babaledWUEQ0PLDE4tp7Iinbe2DZtAtY5JUptKYuStWDZx+QER4808P8dejNWRnBDzgthYJm/AyNSXflHSJhz2+NC+h7RylOLxbwLEQocmyKKiYxa2gT85m6ajVL2M6TnfG67nnK+K2f7iCGL6wYXRITD1q+7+5SWqBdDXIV921W4IKWaD2GJk+NRBoOqQhbsrk8Tn5XsNd7DMYVHk47oMDGbeBnrOIoRPsbBgAcoCsxxhiB9yN6Lf8EUbnlXVEDzJuZk048L1BDZL+6nkA8btTQGP2ijUFWA7rTrod3LjUDQWLZS95njjl867dtmv/znYkzwIDAQAB https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/sig http://purl.org/nanopub/x/hasSignature d9Kesh3LxI5756VpO1MYDoBq39Fmf3qnh2ruP/pJgvepMoAbcmL7zhb86Bo1FJhXmkNRJ+ye27r/jrS2jChe5j1e9HtNDTuAoBMPLrVXYYNrNeCZV4LuC2hi+XI1AxyZSS03XqQDKvsI1iaVBHiStC+El4ZxFKAeFOoBGBkSI/cIAgAJQetXG8qzPbzzmYlxzYIUUNu4sXHe8yj9xxvmP8Did21bq2cGE2hykfJP4Zb1pUjHjnxpGGQojAoV69fIa0bgWhNnZxkwDMXAgxL4Hc/IXdZtGIaQUvOHfFHbi/kVmOt4rFXN2/7XgiWgNaFTCfsFSxs51kJyKv/iKVc3vg== https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/sig http://purl.org/nanopub/x/hasSignatureTarget https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4 https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4/sig http://purl.org/nanopub/x/signedBy https://orcid.org/0009-0008-8411-2742