Advances in Semantic Segmentation of High-Resolution Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 41
Special Issue Editors
Interests: remote sensing image processing; domain adaptation/generalization; large multimodal models; robust pattern recognition
Interests: intelligent processing of remote sensing images; multimodal image registration
Interests: large-scale information extraction; geospatial big data; automated land cover mapping; land cover dynamics; multi-source data fusion
Special Issue Information
Dear Colleagues,
With the rapid advancement of remote sensing technologies, high-resolution remote sensing imagery (HRRSI) with diverse modalities has emerged, enabling a range of practical applications such as individual building recognition, road network extraction, and fine-grained agricultural classification. However, semantic segmentation of HRRSI remains challenging due to high intra-class variability, inter-class similarity, geographic complexity, and cross-modality heterogeneity. In addition, the substantial computational and economic costs associated with large-scale interpretation hinder the real-world deployment of HRRSI segmentation models. Addressing these challenges is essential for advancing remote sensing applications in environmental monitoring, urban planning, precision agriculture, and disaster management.
This Special Issue aims to bring together innovative research on the semantic segmentation of HRRSI, with a focus on methodological advances that enable interpretation in dynamic and open-set environments. We particularly welcome contributions that breakthrough the boundaries of traditional closed-set segmentation, including approaches leveraging large multi-modal models (LMMs), domain-agnostic learning, multi-/cross-modal learning, zero-shot and incremental learning, and other emerging paradigms. In addition, we also encourage studies on model lightweighting (e.g., knowledge distillation, neural architecture search, and pruning) to support scalable deployment, along with autonomous workflows (e.g., AI agents) for complex interpretation tasks. Overall, this Special Issue aims to foster new research directions that integrate methodological innovation with practical applicability in high-resolution remote sensing.
Topics of interest include, but are not limited to, the following:
- Advanced architectures and learning frameworks for HRRSI semantic segmentation.
- Open-set, zero-shot, domain-agnostic, and incremental segmentation methods for HRRSI.
- Resource-efficient HRRSI semantic segmentation methods (distillation, pruning, and quantization).
- Multi-/cross-modal fusion for heterogeneous HRRSI segmentation (e.g., optical, SAR, and LiDAR data).
- AI agents and autonomous workflows for large-scale complex HRSSI interpretation.
- Benchmark segmentation datasets and real-world applications of HRSSI.
Dr. Chenbin Liang
Dr. Yunyun Dong
Dr. Ranyu Yin
Dr. Zhihao Wang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- high-resolution remote sensing imagery (HRRSI)
- semantic segmentation
- robust pattern recognition
- multi-modal fusion
- large multi-modal models
- intelligent interpretation
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