Remote Sensing with LiDAR Point Clouds: From Semantic Segmentation to 3D Scene Understanding
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 129
Special Issue Editors
Interests: environmental perception; point cloud semantic segmentation; object detection
Interests: remote sensing image intelligent interpretation and application; computer vision and pattern recognition; deep learning and lightweight design
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; multisource remote sensing; point cloud image processing; classification
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
LiDAR (Light Detection and Ranging) technology has become a hotspot of modern remote sensing, providing dense, accurate 3D point clouds that capture the geometric structure of environments. The transition from processing raw point cloud data to achieving intelligent semantic segmentation is of great significance, enabling the automated identification and classification of objects within complex 3D spaces. This progression towards holistic 3D scene understanding is a fundamental driver of innovation in geospatial science, computer vision, and artificial intelligence. It forms the critical link between data acquisition and actionable insight, powering the development of smart cities, digital twins, and advanced autonomous systems.
This Special Issue will collect advanced research on LiDAR point clouds, covering the full spectrum from basic semantic labeling to comprehensive 3D scene interpretation. We encourage submissions covering varied topics, including foundational methods for point-level or instance-level segmentation, as well as approaches for scene understanding, reconstruction, and modeling. Topics of interest include, but are not limited to, the integration of LiDAR with multimodal data (e.g., optical, thermal, or SAR), scalable algorithms for large-scale urban or natural environments, and innovative applications in areas such as autonomous systems, digital twins, smart infrastructure, and environmental monitoring. Studies proposing novel deep learning architectures, benchmark datasets, robust evaluation frameworks, or insightful reviews on the evolution of 3D scene parsing are also welcome.
Articles may address, but are not limited, to the following topics:
- Advanced deep learning architectures for 3D point cloud semantic segmentation and instance segmentation.
- Weakly-supervised, self-supervised, and few-shot learning for point cloud understanding.
- Fusion of multi-modal data (e.g., imagery, SAR, hyperspectral) with LiDAR point clouds.
- Three-dimensional object detection, reconstruction, and change detection from dynamic point cloud sequences.
- Large-scale scene parsing, semantic mapping, and the creation of hierarchical scene graphs.
- Benchmarks, datasets, and performance evaluation for 3D scene understanding tasks.
Dr. Kaiqi Liu
Dr. Wenshuai Hu
Dr. Junjie Wang
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
- point clouds
- semantic segmentation
- environment perception
- deep learning
- datasets
- three-dimensional understanding
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