2026-10-14T08:18:49Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34049738 2026-10-01T17:52:32Z category_146 category_39 category_734 category_931 category_106 portal_5 item_type_1 month_year_10_2026

<p>Ablation evidence for the feature-fusion design.</p> Feng Yang (132135) Miaoxing Zhao (25158092) Leixiang Ding (25158095) Yan Li (23143) Jing Wang (6206297) Space Science Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Science Policy type texts related total parameter count proposed model achieved high omission rates experimental results show dimensional feature system bootstrap resampling gave ante early warning 93 &# 8211 39 &# 8211 textual semantic features deep semantic encoding combining professional knowledge lightweight transformer model enhanced lightweight transformer source planning texts text statistical attributes land use conflicts lightweight transformer statistical features use conflicts single text deep fusion territorial planning planning schemes planning compilation test set study proposes strong discriminability specific validation small number process review point estimates plan integration low efficiency inference time individual cases inadequate standardization domain knowledge compliance review based framework auxiliary tool automatic identification authoritative channels 57 %, 5 seconds <div><p>Implicit information on land use conflicts in territorial spatial planning texts directly affects the coordination and implementability of planning schemes. Traditional manual interpretation approaches have low efficiency, high omission rates, and inadequate standardization, making them difficult to use for full-process review of massive multi-source planning texts under multi-plan integration. To address this problem, this study proposes a lightweight Transformer-based framework for automatic land use conflict identification by integrating domain prior features with deep semantic encoding. Multi-type texts related to territorial planning were collected from official and authoritative channels to construct a domain-specific dataset containing 1200 valid samples. A multi-dimensional feature system with strong discriminability and interpretability was then extracted by combining professional knowledge of territorial planning with text statistical attributes. Finally, a lightweight Transformer model with a total parameter count of only 700000 was designed. Through the deep fusion of textual semantic features and statistical features, the model performs binary classification of land use conflicts. Experimental results show that the proposed model achieved an accuracy of 98.75% and an F1-score of 96.9% on the held-out test set, with an inference time of less than 0.5 seconds for a single text. On the 240-sample held-out test set, the 95% confidence interval for accuracy was 96.39–99.57%, and bootstrap resampling gave a 95% interval of 92.93–100.00% for the F1-score, indicating that the point estimates were not driven by a small number of individual cases. The framework may support lightweight local deployment in grassroots planning management departments after site-specific validation and can provide an auxiliary tool for planning compilation, compliance review, and ex-ante early warning of conflicts in territorial spatial planning.</p></div> 2026-10-01T17:52:23Z Image Figure 10.1371/journal.pone.0359870.g007 https://figshare.com/articles/figure/_p_Ablation_evidence_for_the_feature-fusion_design_p_/34049738 CC BY 4.0