3rd International Workshop on Artificial Intelligence for 3D Big Spatial Data Processing - AI3D2020
September 14 - 17, 2020
Bratislava, Slovakia
http://www.dexa.org/ai3d2020
email: ai3d2020@easychair.org, ai3d2020-ml@aist.go.jp
Papers submission: https://easychair.org/conferences/?conf=ai3d2020
Bratislava, Slovakia
http://www.dexa.org/ai3d2020
email: ai3d2020@easychair.org, ai3d2020-ml@aist.go.jp
Papers submission: https://easychair.org/conferences/?conf=ai3d2020
Authors will be allowed to present their work virtually if needed to ensure both the safety of participants and wide dissemination of their work.
We will announce further instructions regarding this in the due time.
We will announce further instructions regarding this in the due time.
IMPORTANT DATES:
Paper submission: March 16, 2020 March 26, 2020 April 6, 2020) April 14, 2020 23:95 GMT (FINAL)
Notification of acceptance:May 20, 2020 June 1, 2020
Camera-ready copies due: June 20, 2020
Notification of acceptance:
Camera-ready copies due: June 20, 2020
PUBLICATION
All accepted papers will be published by Springer in "Communications in Computer and Information Science".
SCOPE
The AI3D 2020 workshop is intended to provide a common forum for researchers, scientists, engineers, and practitioners throughout the world to present their latest research findings, developments, and applications in the area of Artificial Intelligence for 3D Big Spatial Data Processing. Recent advances in laser technology has resulted in the generation of a massive amount of 3D spatial data. The 3D spatial data offer a useful source of information for natural resource management, urban panning, autonomous driving, etc. Artificial intelligence (AI) has the potential to take the 3D spatial data processing into hyper-growth. Recently, 3D indoor mapping systems, terrestrial mobile mapping systems (MMS) and airborne LiDAR systems are capable of collecting terabytes of data (including images and point clouds) in a single scan or on a single trip. Existing approaches and tools lack the efficient management, processing and analysis of 3D spatial data. Many of these issues can be solved by using Artificial Intelligence and Deep Learning to redefine the work flow. This workshop focuses on the use of Artificial Intelligence and Deep Learning to improve the processing, management and analysis of 3D Big Spatial data. The workshop aims at providing a platform to the group of researchers working in this direction to share their work, exchange ideas, and solve research problems.
TOPICS OF INTEREST
A list of topics of interest includes but is not limited to:
- AI and the 3D scanning technologies and devices
- Use of AI in 3D view registration and surface modeling
- Intelligent LiDAR data processing, management and analysis
- Point cloud data processing and analysis
- 3D modeling and depth image processing
- Spatio-temporal data processing and analysis
- Geo spatial data processing and analysis
- Distributed, parallel and peer-to-peer approaches to index, search and \item process 3D Big spatial data
- AI based object detection from point cloud and images
- Supervised/Unsupervised object annotation in 3D data
- Point cloud data indexing and querying
- 3D Big spatial data architectures
- 3D Big spatial data visualization and analytics
- 3D Big spatial data cleaning, compression and integration
- Geographic Information Retrieval
- Indoor and outdoor mapping
- 3D mapping
- Urban Planning
- Spatial data applications
- User Interfaces and Visualization
SUBMISSION GUIDELINES
Authors are invited to electronically submit original research contributions or experience reports in English. AI3D 2020 will accept submissions of both short (up to 10 pages) and full papers (up to 15 papers). The submitted manuscript should closely reflect the final paper as it will appear in the Proceedings. AI3D reserves the right to accept papers only as short papers, in which papers describe interesting and innovative ideas which still require further technical development. Any submission that significantly exceeds length limits or deviates from formatting requirements may be rejected without review..
Formatting guidelines: http://www.dexa.org/formatting_guidelines
Online Papers Submission: https://easychair.org/conferences/?conf=ai3d2020
REVIEW PROCESS
Submitted papers will be carefully evaluated based on originality, significance, technical soundness, and clarity of exposition. Duplicate submissions are not allowed and will be rejected immediately without further review. Authors are expected to agree to the following terms: "I understand that the submission must not overlap substantially with any other paper that I am a co-author of or that is currently submitted elsewhere. Furthermore, previously published papers with any overlap are cited prominently in this submission." Questions about this policy or how it applies to a specific paper should be directed to the PC Co-chairs.
The AI3D 2020 workshop is intended to provide a common forum for researchers, scientists, engineers, and practitioners throughout the world to present their latest research findings, developments, and applications in the area of Artificial Intelligence for 3D Big Spatial Data Processing. Recent advances in laser technology has resulted in the generation of a massive amount of 3D spatial data. The 3D spatial data offer a useful source of information for natural resource management, urban panning, autonomous driving, etc. Artificial intelligence (AI) has the potential to take the 3D spatial data processing into hyper-growth. Recently, 3D indoor mapping systems, terrestrial mobile mapping systems (MMS) and airborne LiDAR systems are capable of collecting terabytes of data (including images and point clouds) in a single scan or on a single trip. Existing approaches and tools lack the efficient management, processing and analysis of 3D spatial data. Many of these issues can be solved by using Artificial Intelligence and Deep Learning to redefine the work flow. This workshop focuses on the use of Artificial Intelligence and Deep Learning to improve the processing, management and analysis of 3D Big Spatial data. The workshop aims at providing a platform to the group of researchers working in this direction to share their work, exchange ideas, and solve research problems.
TOPICS OF INTEREST
A list of topics of interest includes but is not limited to:
- AI and the 3D scanning technologies and devices
- Use of AI in 3D view registration and surface modeling
- Intelligent LiDAR data processing, management and analysis
- Point cloud data processing and analysis
- 3D modeling and depth image processing
- Spatio-temporal data processing and analysis
- Geo spatial data processing and analysis
- Distributed, parallel and peer-to-peer approaches to index, search and \item process 3D Big spatial data
- AI based object detection from point cloud and images
- Supervised/Unsupervised object annotation in 3D data
- Point cloud data indexing and querying
- 3D Big spatial data architectures
- 3D Big spatial data visualization and analytics
- 3D Big spatial data cleaning, compression and integration
- Geographic Information Retrieval
- Indoor and outdoor mapping
- 3D mapping
- Urban Planning
- Spatial data applications
- User Interfaces and Visualization
SUBMISSION GUIDELINES
Authors are invited to electronically submit original research contributions or experience reports in English. AI3D 2020 will accept submissions of both short (up to 10 pages) and full papers (up to 15 papers). The submitted manuscript should closely reflect the final paper as it will appear in the Proceedings. AI3D reserves the right to accept papers only as short papers, in which papers describe interesting and innovative ideas which still require further technical development. Any submission that significantly exceeds length limits or deviates from formatting requirements may be rejected without review..
Formatting guidelines: http://www.dexa.org/formatting_guidelines
Online Papers Submission: https://easychair.org/conferences/?conf=ai3d2020
REVIEW PROCESS
Submitted papers will be carefully evaluated based on originality, significance, technical soundness, and clarity of exposition. Duplicate submissions are not allowed and will be rejected immediately without further review. Authors are expected to agree to the following terms: "I understand that the submission must not overlap substantially with any other paper that I am a co-author of or that is currently submitted elsewhere. Furthermore, previously published papers with any overlap are cited prominently in this submission." Questions about this policy or how it applies to a specific paper should be directed to the PC Co-chairs.
ACCEPTED PAPERS
All accepted papers will be published by Springer in "Communications in Computer and Information Science (CCIS). Authors of all accepted papers must sign a Springer copyright release form. Papers are accepted with the understanding that at least one author will register for the conference to present the paper. All published papers will be indexed appropriately in all major indexes.
All accepted papers will be published by Springer in "Communications in Computer and Information Science (CCIS). Authors of all accepted papers must sign a Springer copyright release form. Papers are accepted with the understanding that at least one author will register for the conference to present the paper. All published papers will be indexed appropriately in all major indexes.
Organization Committee
General Co-Chairs:
General Co-Chairs:
- Prof. Dr. Sisi Zlatanova, UNSW, Australia
- Dr. Kyoung-Sook Kim, AIRC, AIST, Japan
Program Committte Co-Chairs:
- Dr. Salman Ahmed Shaikh, AIRC, AIST, Japan
- Dr. Sung-Hwan Kim, Pusan National University, South Korea
Program Committee Members:
- Akiyoshi Matono, National Institute of Advanced Industrial Science and Technology (AIST), Japan
- Xin Liu, National Institute of Advanced Industrial Science and Technology (AIST), Japan
- Takashi Fuse, The University of Tokyo, Japan
- Franz Rottensteiner, University of Hannover, Institute of Photogrammetry and GeoInformation, Germany
- Kourosh Khoshelham, The University of Melbourne, Australia
- Maria Antonia, Brovelli Politecnico di Milano, Italy
- Martin Kada, TU Berlin, Germany
- Yuyang Dong, NEC, Japan
- Giorgio Agugiaro, Delft University of Technology, The Netherlands
- Adam Jatowt, Kyoto University, Japan
- Chidchanok Choksuchat, Prince of Songkla University, Thailand
- Lucía Díaz Vilariño, Universidad de Vigo, Spain
- Claire Ellul, University College London, UK
- Marguerite Madden, University of Georgia, USA
- Suzana Dragicevic, Simon Fraser University, Canada
- Wei Li, The University of New South Wales, Australia
- Hugo Ledoux, Delft University of Technology, The Netherlands
- Liangliang Nan, Delft University of Technology, The Netherlands
- Songnian Li, Ryerson University, Canada
- Savong Bou, Toyota Technological Institute, Japan
- Rolan Billen, University of Liege, Belgium
- Umit Isikdag, Mimar Sinan Fine Arts University, Turkey
- Bisheng Yang, Wuhan University, China
For further inquiries please contact PC chairs/co-Chairs (ai3d2020@easychair.org) or (ai3d2020-ml@aist.go.jp)
