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Remote Sensing Intelligent Interpretation in the Era of Large Models and Intelligent Agents: New Challenges, Methods and Opportunities

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 15 October 2026 | Viewed by 2089

Special Issue Editors

Prof. Dr. Lei Wang

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: intelligent interpretation of multimodal remote sensing data; intelligent agent design and applications
Dr. Xiyu Qi

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: Intelligent interpretation of multimodal remote sensing data; intelligent agent design and applications
Dr. Yongqiang Mao

E-Mail Website
Guest Editor
Department of Electronic Engineering, Tsinghua University (THU), Beijing 100084, China
Interests: optical remote sensing image; multimodal remote sensing; data fusion; foundation models; object detection; semantic segmentation
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoxuan Liu

E-Mail Website
Guest Editor
Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: intelligent interpretation of remote sensing images; agent technology and applications
Dr. Zicong Zhu

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
Interests: computer vision; pattern recognition; remote sensing image interpretation; object detection; image pretraining; cross-modal image–text retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Zhuohong Li

E-Mail Website
Guest Editor
Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
Interests: land-cover mapping; weakly supervised learning; earth observation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of large language models (LLMs) and intelligent agents has ushered in a transformative era for remote sensing applications. These cutting-edge technologies are revolutionizing how we process, analyze, and interpret vast amounts of Earth observation data. They enable unprecedented capabilities in automated feature extraction, pattern recognition, and decision-making processes. The integration of foundation models with remote sensing workflows presents remarkable opportunities alongside significant challenges that demand innovative methodological approaches and interdisciplinary collaboration.

This Special Issue aims to explore the intersection of large models and intelligent agents with remote sensing intelligent interpretation. We focus on how these technologies can enhance our understanding of Earth systems while overcoming existing limitations in data processing, model generalization, and real-world deployment. The scope aligns with the Remote Sensing journal's mission to advance cutting-edge research in Earth observation technologies and their applications across environmental, agricultural, urban, and climate studies.

We invite original research articles, comprehensive reviews, and technical notes addressing, but not limited to, the following topics:

  • Foundation models for remote sensing image analysis and interpretation
  • Multi-modal large models integrating optical, SAR, and hyperspectral data
  • Intelligent agents for autonomous Earth observation systems
  • Transfer learning and domain adaptation in remote sensing applications
  • Prompt engineering and fine-tuning strategies for geospatial tasks
  • Challenges in model interpretability and uncertainty quantification
  • Real-time processing and edge computing solutions
  • Ethical considerations and bias mitigation in AI-driven remote sensing
  • Novel architectures for spatio-temporal data analysis
  • Applications in disaster monitoring, climate change, and sustainable development

Prof. Dr. Lei Wang
Dr. Xiyu Qi
Dr. Yongqiang Mao
Dr. Xiaoxuan Liu
Dr. Zicong Zhu
Dr. Zhuohong Li
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

  • large language models (LLMs)
  • intelligent agents
  • remote sensing
  • foundation models
  • multi-modal learning
  • earth observation
  • transfer learning
  • multi-modal learning
  • spatio-temporal analysis
  • sustainable development
  • geospatial tasks

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

22 pages, 3734 KB  
Article
CLEAR: A Cognitive LLM-Empowered Adaptive Restoration Framework for Robust Ship Detection in Complex Maritime Scenarios
by Min Li, Xinyu Zhao and Yunfeng Wan
Remote Sens. 2026, 18(8), 1142; https://doi.org/10.3390/rs18081142 - 12 Apr 2026
Abstract
Ship detection in remote sensing imagery serves as a cornerstone of modern maritime surveillance. Existing visible light detectors suffer from severe performance degradation in adverse environmental conditions (e.g., fog, low light) due to domain gaps. Traditional global enhancement methods often lack adaptability, leading [...] Read more.
Ship detection in remote sensing imagery serves as a cornerstone of modern maritime surveillance. Existing visible light detectors suffer from severe performance degradation in adverse environmental conditions (e.g., fog, low light) due to domain gaps. Traditional global enhancement methods often lack adaptability, leading to “negative transfer”—where artifacts are introduced into clean images or mismatched with degradation types. To address these challenges, we propose CLEAR (Cognitive Large Language Model (LLM)-Empowered Adaptive Restoration) framework. Inspired by the dual-process theory of cognition, we introduce a dynamic switching mechanism between fast perception and deep reasoning. Rather than processing all images indiscriminately, it utilizes a hybrid gating mechanism to efficiently filter nominal samples, triggering Vision–Language Model (VLM) only when necessary to diagnose degradation and dispatch targeted restoration operators. Extensive experiments on the constructed HRSC-Robust dataset demonstrate that CLEAR achieves an overall mean Average Precision (mAP) at 0.5 Intersection-over-Union (IoU) of 86.92%, outperforming the baseline by 7.74%. Notably, it establishes a “fail-safe” mechanism for optical degradations. By adaptively resolving fog and low-light, it effectively mitigates detector blindness—exemplified by a doubled Recall rate (52.52%) in dark scenarios. Furthermore, a confidence-based sparse triggering strategy ensures operational efficiency, maintaining a throughput of ~11.8 FPS in nominal conditions. This work validates the potential of VLMs for interpretable and robust remote sensing tasks. Full article
(This article belongs to the Special Issue Remote Sensing Intelligent Interpretation in the Era of Large Models and Intelligent Agents: New Challenges, Methods and Opportunities)
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22 pages, 11365 KB  
Article
Addressing Dense Small-Object Detection in Remote Sensing: An Open-Vocabulary Object Detection Framework
by Menghan Ju, Yingchao Feng, Wenhui Diao and Chunbo Liu
Remote Sens. 2026, 18(6), 851; https://doi.org/10.3390/rs18060851 - 10 Mar 2026
Viewed by 516
Abstract
Remote sensing open-vocabulary object detection focuses on identifying and localizing unseen categories within remote sensing imagery. However, constrained by characteristics such as dense target distribution, complex background interference, and drastic scale variations inherent to remote sensing scenarios, existing methods are prone to background [...] Read more.
Remote sensing open-vocabulary object detection focuses on identifying and localizing unseen categories within remote sensing imagery. However, constrained by characteristics such as dense target distribution, complex background interference, and drastic scale variations inherent to remote sensing scenarios, existing methods are prone to background noise interference when extracting features from dense, small target regions. This leads to weakened semantic representation and reduced localization accuracy. Therefore, we propose RS-DINO to address these challenges. Specifically: Firstly, to address the issue of small features being obscured by the background, the feature extraction module incorporates a multi-scale large-kernel attention mechanism. This expands the receptive field while enhancing local detail modelling, significantly improving the feature representation of minute targets. Secondly, a cross-modal feature fusion module employing bidirectional cross-attention achieves deep alignment between image and textual features. Subsequently, a language-guided query selection mechanism enhances detection accuracy through hybrid query strategies. Finally, to enhance the spatial sensitivity and channel adaptability of fusion features, the multimodal decoder integrates a convolutional gated feedforward network, significantly boosting the model’s robustness in dense, multi-scale scenes. Experiments on DIOR, DOTA v2.0, and NWPU-VHR10 demonstrate substantial gains, with fine-tuned RS-DINO surpassing existing methods by 3.5%, 3.7%, and 4.0% in accuracy, respectively. Full article
(This article belongs to the Special Issue Remote Sensing Intelligent Interpretation in the Era of Large Models and Intelligent Agents: New Challenges, Methods and Opportunities)
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22 pages, 87137 KB  
Article
FLD-Net for Floating Litter Detection in UAV Remote Sensing
by Xingyue Wang, Bin Zhou, Xia Ye, Lidong Wang and Zhen Wang
Remote Sens. 2026, 18(5), 736; https://doi.org/10.3390/rs18050736 - 28 Feb 2026
Viewed by 273
Abstract
Unmanned Aerial Vehicles provide a cost-effective solution for water environment monitoring, yet detecting floating litter remains challenging due to small target scales, complex geometries, and severe surface interferences. To bridge the data deficiency in this domain, this study introduces UAV-Flow, a multi-scenario benchmark [...] Read more.
Unmanned Aerial Vehicles provide a cost-effective solution for water environment monitoring, yet detecting floating litter remains challenging due to small target scales, complex geometries, and severe surface interferences. To bridge the data deficiency in this domain, this study introduces UAV-Flow, a multi-scenario benchmark dataset wherein small-scale targets constitute 78.9%. Building upon this foundation, we propose the Floating Litter Detection Network (FLD-Net), a lightweight, real-time detection framework tailored for edge deployment. Adopting a progressive optimization paradigm, FLD-Net integrates three cascaded enhancement modules to achieve holistic performance gains across feature extraction, cross-scale fusion, and noise suppression. Specifically, the Deformation Feature Extraction Module (DFEM) enhances backbone adaptability to small targets and non-rigid deformations; the Dynamic Cross-scale Fusion Network (DCFN) facilitates efficient cross-scale semantic fusion via content-aware upsampling and an asymmetric topology; and the Dual-domain Anti-noise Attention (DANA) mechanism achieves discriminative decoupling between target semantics and structural noise through spatial-channel interaction. Experimental results on UAV-Flow demonstrate that FLD-Net achieves an mAP50 of 80.47%. Compared to the YOLOv11s baseline, it improves Recall and mAP50 by 11.66% and 8.51%, respectively, with only 9.9 M parameters. Furthermore, deployment on the NVIDIA Jetson Xavier NX yields an inference latency of 14 ms and an energy efficiency of 4.80 FPS/W, confirming the system’s robustness and viability for automated pollution monitoring. Full article
(This article belongs to the Special Issue Remote Sensing Intelligent Interpretation in the Era of Large Models and Intelligent Agents: New Challenges, Methods and Opportunities)
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27 pages, 9162 KB  
Article
Multi-Domain Incremental Learning for Semantic Segmentation via Visual Domain Prompt in Remote Sensing Data
by Junxi Li, Zhiyuan Yan, Wenhui Diao, Yidan Zhang, Zicong Zhu, Yichen Tian and Xian Sun
Remote Sens. 2026, 18(3), 464; https://doi.org/10.3390/rs18030464 - 1 Feb 2026
Viewed by 653
Abstract
Domain incremental learning for semantic segmentation has gained lots of attention due to its importance for many fields including urban planning and autonomous driving. The catastrophic forgetting problem caused by domain shift has been alleviated by structure expansion of the model or data [...] Read more.
Domain incremental learning for semantic segmentation has gained lots of attention due to its importance for many fields including urban planning and autonomous driving. The catastrophic forgetting problem caused by domain shift has been alleviated by structure expansion of the model or data rehearsal. However, these methods ignore similar contextual knowledge between the new and the old data domain and assume that new knowledge and old knowledge are completely mutually exclusive, which cause the model to be trained in a suboptimal direction. Motivated by the prompt learning, we proposed a new domain incremental learning framework named RS-VDP. The key innovation of RS-VDP is to utilize a visual domain prompt to change the optimization direction from input data space and feature space. First, we designed a domain prompt based on a dynamic location module, which applied a visual domain prompt according to a local entropy map to update the distribution of the input images. Second, in order to filter the feature vectors with high confidence, a representation feature alignment based on an entropy map module is proposed. This module ensures the accuracy and stability of the feature vectors involved in the regularization loss, alleviating the problem of semantic drift. Finally, we introduced a new evaluation metric to measure the overall performance of the incremental learning models, solving the problem that the traditional evaluation metric is affected by the single-task accuracy. Comprehensive experiments demonstrated the effectiveness of the proposed method by significantly reducing the degree of catastrophic forgetting. Full article
(This article belongs to the Special Issue Remote Sensing Intelligent Interpretation in the Era of Large Models and Intelligent Agents: New Challenges, Methods and Opportunities)
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