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To address these issues, this paper proposes a U-Net\u2013based remote sensing semantic segmentation network termed RTAS-Net (ResNet\u2013Transformer\u2013ASPP Segmentation Network), which enhances feature representation through a collaborative design of multi-scale context aggregation, fine-scale reinforcement, and local-to-global modeling. At the high-semantic level, the network incorporates ASPP to aggregate multi-scale contextual information and enlarge the effective receptive field, while integrating the window-based self-attention mechanism of Swin Transformer to model cross-region dependencies, thereby improving semantic consistency over large-scale areas. At high-resolution skip connections, a lightweight mini-ASPP is embedded to reinforce and pre-fuse fine-scale neighborhood information, and MobileViT is introduced to strengthen local texture and fine-grained structural representations, thus enhancing the recognition and boundary delineation of small objects. Rather than a simple stacking of modules, RTAS-Net achieves unified modeling of global semantics and local details through coordinated cross-level pathways. Experimental results on the ISPRS Potsdam, Vaihingen and LoveDA datasets demonstrate that the proposed method achieves consistent improvements in mIoU, mF1, and OA, and further provides a comprehensive analysis of parameter scale and inference efficiency, validating its effectiveness and practical applicability.<\/jats:p>","DOI":"10.1371\/journal.pone.0343729","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T17:41:26Z","timestamp":1775151686000},"page":"e0343729","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["RTAS-Net: A ResNet-transformer-ASPP semantic segmentation network for remote sensing images"],"prefix":"10.1371","volume":"21","author":[{"given":"Ziheng","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8314-6448","authenticated-orcid":true,"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kejia","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"pone.0343729.ref001","doi-asserted-by":"crossref","first-page":"112480","DOI":"10.1016\/j.rse.2021.112480","article-title":"Mapping large-scale and fine-grained urban functional zones from VHR images using a multi-scale semantic segmentation network and object based approach","volume":"261","author":"S Du","year":"2021","journal-title":"Remote Sensing of Environment"},{"issue":"23","key":"pone.0343729.ref002","doi-asserted-by":"crossref","first-page":"4573","DOI":"10.3390\/rs16234573","article-title":"LULC-SegNet: Enhancing Land Use and Land Cover Semantic Segmentation with Denoising Diffusion Feature Fusion","volume":"16","author":"Z Shi","year":"2024","journal-title":"Remote Sensing"},{"issue":"16","key":"pone.0343729.ref003","doi-asserted-by":"crossref","first-page":"3088","DOI":"10.3390\/rs16163088","article-title":"HA-Net for Bare Soil Extraction Using Optical Remote Sensing Images","volume":"16","author":"J Zhao","year":"2024","journal-title":"Remote Sensing"},{"issue":"12","key":"pone.0343729.ref004","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.3390\/rs11121443","article-title":"Unmanned Aerial Vehicle for Remote Sensing Applications\u2014A Review","volume":"11","author":"H Yao","year":"2019","journal-title":"Remote Sensing"},{"key":"pone.0343729.ref005","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2022.12.020","article-title":"PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes.","volume":"196","author":"W Gao","year":"2023","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"pone.0343729.ref006","doi-asserted-by":"crossref","first-page":"113452","DOI":"10.1016\/j.rse.2023.113452","article-title":"Semantic segmentation of water bodies in very high-resolution satellite and aerial images","volume":"287","author":"M Wieland","year":"2023","journal-title":"Remote Sensing of Environment"},{"issue":"8","key":"pone.0343729.ref007","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.3390\/rs15082208","article-title":"Multi-Scale and Context-Aware Framework for Flood Segmentation in Post-Disaster High Resolution Aerial Images","volume":"15","author":"SD Khan","year":"2023","journal-title":"Remote Sensing"},{"issue":"19","key":"pone.0343729.ref008","doi-asserted-by":"crossref","first-page":"3620","DOI":"10.3390\/rs16193620","article-title":"Fine-Grained High-Resolution Remote Sensing Image Change Detection by SAM-UNet Change Detection Model","volume":"16","author":"X Zhao","year":"2024","journal-title":"Remote Sensing"},{"key":"pone.0343729.ref009","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. 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