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<https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4> a np:Nanopublication;
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  dct:created "2026-05-01T17:43:55.415Z"^^xsd:dateTime;
  dct:creator orcid:0009-0008-8411-2742;
  dct:license <https://creativecommons.org/licenses/by/4.0/>;
  npx:introduces <https://ieeexplore.ieee.org/document/11408843>;
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<https://ieeexplore.ieee.org/document/11408843> a <https://w3id.org/fair/ff/terms/article>,
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  dct:contributor orcid:0000-0002-4135-7634;
  dct:creator orcid:0009-0004-4939-2970, orcid:0009-0009-0781-3438;
  dct:publisher <https://ror.org/01n002310>;
  dct:subject <http://aims.fao.org/aos/agrovoc/c_6498>;
  rdfs: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""";
  rdfs:label "A Pseudomultitask Neural Network Classification Model for Cropland Mapping in Mountainous Areas Using High-Resolution Remote Sensing Images";
  <https://schema.org/funder> <https://ror.org/021nxhr62>;
  <https://w3id.org/fdof/ontology#hasMetadata> <https://w3id.org/np/RA60xA01g3fqj5OJJLpReKc3P7Dof9SA6hkY_izzpCYz4>;
  <https://www.w3.org/ns/dcat#contactPoint> "xzhou@mtech.edu";
  <https://www.w3.org/ns/dcat#endDate> "January 2026";
  <https://www.w3.org/ns/dcat#startDate> "2025" .

sub:assertion prov:wasAttributedTo orcid:0009-0008-8411-2742 .

orcid:0009-0008-8411-2742 foaf:name "Emily Regalado" .

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