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Fifth Anniversary of “AI Remote Sensing” Section

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

Deadline for manuscript submissions: 15 October 2025 | Viewed by 150

Special Issue Editors


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Guest Editor
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: remote sensing data analysis; processing with a special focus on deep learning methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The year 2025 marks the fifth anniversary of the Section “AI Remote Sensing” in the journal Remote Sensing. Over the last five years, this Section published more than 1,000 papers. We are extremely grateful to all scholars who have contributed to the Section's success over the years for their time and effort. To mark this anniversary milestone, we are pleased to announce the launch of a Special Issue entitled “Fifth Anniversary of “AI Remote Sensing” Section”.

We invite submissions of original papers that look ahead to the next 5 years, as well as articles highlighting the most significant recent achievements and influential technologies in the field of “AI remote sensing” applications.

This Special Issue covers a wide range of hot topics related to AI remote sensing and invites researchers to submit manuscripts sharing their latest findings and insights into the field, including but not limited to the following topics:

  • Machine learning and deep learning for remote sensing;
  • Optical/multispectral/hyperspectral/SAR image processing;
  • Target detection, anomaly detection, and change detection;
  • Multi-modal remote sensing.

Prof. Dr. Lefei Zhang
Prof. Dr. Yushi Chen
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

  • machine learning and deep learning for remote sensing
  • optical/multispectral/hyperspectral/SAR image processing
  • target detection, anomaly detection, and change detection
  • multi-modal remote sensing

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Published Papers (1 paper)

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Research

18 pages, 1566 KiB  
Article
Synthesizing Remote Sensing Images from Land Cover Annotations via Graph Prior Masked Diffusion
by Kai Deng, Siyuan Wei, Shiyan Pang, Huiwei Jiang and Bo Su
Remote Sens. 2025, 17(13), 2254; https://doi.org/10.3390/rs17132254 - 30 Jun 2025
Abstract
Semantic image synthesis (SIS) in remote sensing aims to generate high-fidelity satellite imagery from land use/land cover (LULC) labels, supporting applications such as map updating, data augmentation, and environmental monitoring. However, the existing methods typically focus on pixel-level semantic-to-image translation, neglecting the spatial [...] Read more.
Semantic image synthesis (SIS) in remote sensing aims to generate high-fidelity satellite imagery from land use/land cover (LULC) labels, supporting applications such as map updating, data augmentation, and environmental monitoring. However, the existing methods typically focus on pixel-level semantic-to-image translation, neglecting the spatial and semantic relationships among land cover objects, which hinders accurate scene structure modeling. To address this challenge, we propose GMDiT, an enhanced conditional diffusion model that extends the masked DiT architecture with graph-prior modeling. By jointly incorporating relational graph structures and semantic labels, GMDiT explicitly captures the object-level spatial and semantic dependencies, thereby improving the contextual coherence and structural fidelity of the synthesized images. Specifically, to effectively capture inter-object dependencies, we first encode the semantics of each node using CLIP and then employ a simple yet effective graph transformer to model the spatial interactions among nodes. Additionally, we design a scene similarity sampling strategy for the reverse diffusion process, improving contextual alignment while maintaining generative diversity. Experiments on the OpenEarthMap dataset show that GMDiT achieves superior performance in terms of FID and other metrics, demonstrating its effectiveness and robustness in the generation of structured remote sensing images. Full article
(This article belongs to the Special Issue Fifth Anniversary of “AI Remote Sensing” Section)
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