3D Scene Perception and Reconstruction of Remote Sensing Imagery
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 January 2026 | Viewed by 16
Special Issue Editors
Interests: point cloud; 3D reconstruction; registration; Remote sensing; computer vision; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: 3D modeling; 3D understanding; 3D deep learning
Interests: 3D point cloud; 3D reconstruction; registration; computer vision
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Three-dimensional scene perception and reconstruction represent fundamental research challenges in modern remote sensing, with critical applications spanning geographical surveys, urban planning, infrastructure monitoring, and disaster management. These complex tasks typically require the integration and processing of multimodal data from diverse sources, including satellite imagery, aerial LiDAR, UAV photogrammetry, and multi-spectral sensors. However, achieving accurate 3D scene interpretation and reconstruction from such heterogeneous datasets presents several key technical challenges:
- Multi-modal data fusion: This entails the effective integration of disparate data formats with varying spatial resolutions, temporal frequencies, and coordinate systems.
- Robust structural reconstruction and perception: This involves the ability to handle noisy inputs, severe occlusions, and complex geometric distortions inherent in real-world remote sensing data and to recover inherent semantic structures.
- Annotation dependency: This involves overcoming the heavy reliance on densely annotated training data for deep learning approaches.
This Special Issue seeks to showcase cutting-edge innovations in both theoretical advances and practical applications of 3D scene perception and reconstruction for remote sensing. We particularly welcome contributions that
- Constitute novel methodologies for cross-modal data fusion and alignment;
- Outline robust algorithms for handling imperfect or incomplete remote sensing data;
- Advance innovative learning paradigms that reduce annotation requirements;
- Undertake semantic and structural reconstruction for large-scale outdoor scenes;
- Benchmark datasets and evaluation frameworks;
- Present transformative applications in real-world scenarios.
This is not an exclusive list, and we encourage submissions addressing these challenges across all domains of remote sensing, and we have particular interest in solutions that bridge the gap between theoretical development and practical implementation.
Prof. Dr. Jun Xiao
Dr. Haiyong Jiang
Dr. Lupeng Liu
Dr. Zhengda Lu
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
- multi-modal fusion
- structural reconstruction
- weak supervision and semi-supervision
- remote sensing
- annotation efficiency
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