Topic Editors
Democratizing 3D Mapping via Non-Conventional and Low-Cost LiDAR and Imaging Sensors
Topic Information
Dear Colleagues,
Over the past five years, the rapid integration of miniaturized and cost-efficient sensors into smartphones, tablets, action cameras, wearables, and other consumer-grade devices has created unprecedented opportunities for accessible data acquisition and 3D spatial analysis. The inclusion of low-cost LiDAR sensors and Artificial Intelligence is broadening the reach of high-resolution mapping systems. These technologies challenge the conventional reliance on expensive, specialized hardware by enabling affordable and scalable alternatives for metric data capture.
This Topic aims to investigate the performance, reliability, and cost efficiency of these non-conventional and low-cost sensing solutions, with a focus on their technical capabilities, operational constraints, and real-world applicability. Despite the rapid proliferation of applications for mobile-based 3D data acquisition—such as point clouds, meshes, and hybrid image–LiDAR products—there remains a substantial knowledge gap regarding their accuracy, limitations, and suitability for professional and scientific use.
While applications related to the built and natural environment represent a central focus, this Topic also welcomes contributions from a broad range of disciplines, including geosciences, civil engineering, architecture, cultural heritage, and robotics. We welcome original research articles and reviews addressing, but not limited to, the following topics:
- Performance evaluation of low-cost mobile LiDAR and imaging sensors for 3D mapping;
- Cost-efficient data acquisition and processing workflows using consumer-grade devices;
- Multi-sensor data fusion (LiDAR, RGB, depth, IMU, GNSS) for mobile and handheld mapping;
- Artificial Intelligence for 3D data processing and analysis;
- Dynamic scene analysis and temporal 3D data acquisition;
- XR-based visualization, interaction, and validation of 3D mapping results;
- Digital twins and spatial computing using low-cost sensing technologies;
- Applications in built and natural environments, cultural heritage, smart cities, and infrastructure.
Dr. Jesús Balado Frías
Dr. Lorenzo Teppati Losè
Dr. Zhouyan Qiu
Topic Editors
Keywords
- low-cost LiDAR
- mobile mapping
- cost-efficient sensing
- multi-sensor data fusion
- point cloud processing
- artificial intelligence
- deep learning
- extended reality (XR)
- SLAM
Participating Journals
| Journal Name | Impact Factor | CiteScore | Launched Year | First Decision (median) | APC | |
|---|---|---|---|---|---|---|
Drones
|
5.2 | 10.0 | 2017 | 21.1 Days | CHF 2600 | Submit |
Geomatics
|
3.7 | 4.6 | 2021 | 21.6 Days | CHF 1200 | Submit |
Heritage
|
2.6 | 4.3 | 2018 | 21.8 Days | CHF 1800 | Submit |
Remote Sensing
|
4.3 | 9.4 | 2009 | 22 Days | CHF 2700 | Submit |
Sensors
|
4.0 | 9.4 | 2001 | 17.8 Days | CHF 2600 | Submit |
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