Bridging AI and Remote Sensing: Multimodal Learning for Advanced Semantic Understanding

The advanced semantic understanding of remote sensing data is the core to fully unleash the potential of remote sensing technology. It can provide core support for complex land cover pattern recognition, capturing urban dynamic changes, analyzing environmental evolution laws, and responding to various disaster events. However, traditional semantic analysis methods have always struggled to cope with the inherent complexity of remote sensing data: firstly, the fusion of multi-source data (such as optical data, synthetic aperture radar data, LiDAR data, and auxiliary geographic spatial information) faces enormous challenges in feature alignment and information fusion; Secondly, the ambiguity of semantic concepts in large-scale heterogeneous scenarios severely limits the accuracy of interpretation results; Thirdly, the urgent need for real-time and fine-grained analysis has far exceeded the capability boundaries of single modal methods or rule-based methods.

In recent years, the rapid advancement of artificial intelligence (AI), particularly multimodal learning, has opened up unprecedented opportunities to bridge the gap between AI and remote sensing. Multimodal learning techniques enable the synergistic integration of complementary information from diverse data sources, allowing models to capture richer contextual and structural features that single-modality methods cannot. This fusion not only enhances the robustness and accuracy of semantic understanding but also expands the scope of remote sensing applications—from precision agriculture and smart cities to climate change monitoring and national security. Yet, despite promising progress, critical challenges remain: how can we effectively handle the heterogeneity of remote sensing modalities, how can we design lightweight multimodal models adaptable to resource-constrained platforms, how can we ensure model interpretability in high-stakes applications, and how can we generalize models across different geographic regions and scenarios?

Yet, despite promising progress, critical challenges remain: how to effectively handle the heterogeneity of remote sensing modalities, how to design lightweight multimodal models adaptable to resource-constrained platforms, how to ensure model interpretability in high-stakes applications, and how to generalize models across different geographic regions and scenarios.

To address these challenges and showcase cutting-edge research at the intersection of artificial intelligence (AI) and remote sensing, we are pleased to announce the launch of a special issue entitled " Bridging AI and Remote Sensing: Multimodal Learning for Advanced Semantic Understanding ".​

This special issue aims to provide a premier platform for researchers and practitioners to share innovative methods, novel applications, and insights into multimodal learning-driven semantic understanding in remote sensing. We welcome high-quality original research papers, review articles, and technical notes that advance the state-of-the-art in this rapidly evolving field.

Topics of interest include, but are not limited to:

• Multimodal data fusion strategies for remote sensing semantic understanding (e.g., fusion of optical, Synthetic Aperture Radar (SAR), LiDAR, and remote sensing text data);​
• Novel multimodal model architectures tailored for remote sensing data (e.g., transformer-based, graph neural network-based, and diffusion model-based multimodal methods);​
• Few-shot, zero-shot, and transfer learning for multimodal remote sensing semantic understanding;​
• Interpretability and trustworthiness of multimodal remote sensing models;​
• Lightweight and edge-deployable multimodal models for real-time remote sensing applications;​
• Multimodal remote sensing semantic segmentation, object detection, and scene classification;​
• Applications of multimodal remote sensing semantic understanding (e.g., urban planning, crop yield estimation, disaster assessment, biodiversity monitoring);​
• Benchmark datasets and evaluation metrics for multimodal remote sensing semantic tasks;​
• Challenges and solutions for handling noisy, incomplete, or imbalanced multimodal remote sensing data.

Prof. Dr. Min Xia
Dr. Ligou Weng
Dr. Haifeng Lin
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.

• multimodal learning；remote sensing semantic understanding；multi-source remote sensing data Fusion
• deep learning