Advances in Remote Sensing Large Visual Language Model
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 30 December 2025 | Viewed by 50
Special Issue Editors
Interests: computer vision; pattern recognition; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: model efficiency; model trustworthiness; AI applications
Interests: hyperspectral imaging; computer vision; pattern recognition and their applications to remote sensing; agriculture; environment; medicine
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of large language models (LLMs) with visual data, particularly in the domain of remote sensing, is poised to revolutionize the way in which we interpret and analyze geospatial data. Recent advancements in artificial intelligence (AI) and machine learning, especially the development of large visual–language models (VLLMs), enable the more efficient and accurate extraction of information from diverse remote sensing data sources. These models, which combine the capabilities of natural language processing (NLP) with visual understanding, enable us to interpret satellite imagery, aerial photos, and sensor data.
This Special Issue aims to explore the latest breakthroughs in the application of large visual language models to remote sensing, showcasing novel methodologies, innovative algorithms, and real-world applications. The scope of this Special Issue includes, but is not limited to, the following topics:
- Theoretical developments and architectures for integrating visual and textual data in remote sensing.
- Multimodal data fusion, leveraging VLLMs to process and interpret data from heterogeneous sources such as optical, radar, and LiDAR sensors.
- Automated feature extraction and object detection from remote sensing imagery using VLLMs.
- AI-driven mapping and land use classification, using large-scale datasets and pretrained models for global monitoring.
- Visual Question Answering applied to remote sensing imagery, where users can ask natural language questions about geographical features, land use, vegetation types, or environmental conditions, and VLLMs can generate contextually relevant and accurate responses.
- Geospatial reasoning and spatial analysis through natural language interfaces, allowing users to interact with complex remote sensing data for decision-making in environmental monitoring, disaster management, and urban planning.
This Special Issue seeks to foster an in-depth discussion of how large visual language models are transforming remote sensing practices, presenting new opportunities for automated analysis, improved decision-making, and enhanced scientific understanding. We welcome contributions that demonstrate the potential of these models to handle complex tasks such as data interpretation, trend prediction, and the automation of time-consuming processes that have traditionally required expert intervention.
We welcome original research papers, reviews, and case studies that provide insights into the design, development, and application of large visual language models in the context of remote sensing.
Prof. Dr. Fan Liu
Dr. Tianjin Huang
Prof. Dr. Jun Zhou
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 100 words) can be sent to the Editorial Office for announcement on this website.
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
- Vision–Language Models (VLMs)
- remote sensing
- multimodal data fusion
- AI-driven mapping
- land use classification
- visual Question Answering
- geospatial reasoning and spatial analysis
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