Due to the many requests received by the chairs of the ICPRAI conference, mostly motivated by COVID-19 pandemic, they have decided to provide a
Deadline extension: January 31th, 2022
This special session of the ICPRAI’22 deals with the learning and analysis of multi-variate and/or multi-temporal and/or multi-resolution and/or multi-source remote sensing images (hereafter denoted as multi* remote sensing data).
The huge amount of data currently produced by modern remote sensing missions (aerial and satellite) has raised up new challenges for the remote sensing communities. These sensors are now able to offer (very) high spatial resolution images with revisit time frequencies never achieved before, considering different kind of signals, e.g., multi(hyper)-spectral optical, radar, LiDAR and digital surface models. In this context, pattern recognition and artificial intelligence (PRAI) techniques play a crucial role to deal with such an impressive amount of multi-source, multi-resolution and multi-temporal data. Learning techniques as unsupervised and supervised approaches, neural networks, deep learning, domain adaptation, time series analysis, etc. are used to tackle different challenging remote sensing oriented tasks such as semantic segmentation, classification, detection and tracking of objects for earth observation and environmental monitoring.
This special session will be an opportunity to give an overview of recent progress of PRAI research studies, in which new methodological, computational and practical achievements dedicated to multivariate remote sensing data will be presented. It also will be a renewed opportunity to gather PRAI and domain-experts researchers, in order to exchange, debate and draw short and long term research objectives around the exploitation, analysis and processing of multimodal remote sensing data coming from heterogeneous sensors. The aim is to stimulate concrete discussions to pave the way to new frameworks especially tailored in the domain.
Multiscale is a special session part of ICPRAI’22, which is the 3rd International Conference on Pattern Recognition and Artificial Intelligence. It is held in Paris from the 1st to the 3rd of June and follows the successful 2018 edition located in Montréal, Canada and the 2020 (virtual) edition held in Zhongshan City, China.
Call for Papers
We welcome contributions of both technical and perspective papers from a wide range of topics, including but not limited to the following topics of interest:
- Artificial intelligence applied to multi* remote sensing data
- Recognition of patterns, objects and targets from multi* remote sensing data
- 2D/3D remote sensing data analysis and processing
- Multi* remote sensing image classification, retrieval and semantic segmentation
- Machine learning, deep learning approaches to deal with multi* remote sensing data
- Analysis of multi-resolution remote sensing images
- Fusion of multi-source remote sensing data
- Multi-temporal remote sensing data analysis and classification
- Transfer Learning and domain adaptation for multi* remote sensing data
- Feature extraction and feature selection for multi* remote sensing data
- Multi-task learning from multi* remote sensing data
Paper submission guidelines
Accepted papers will be presented at the conference in a special session and will be published by Springer in the Lecture Notes in Computer Science. Articles should be prepared according to the LNCS author guidelines and templates and they should be at most twelve pages long. Submissions will be peer-reviewed by at least 3 reviewers and assessed based on their novelty, clarity significance, and relevance regarding the special session topics. Submissions that are already accepted or under review for another venue are not accepted.
All times are in Central European Time (CET)
- Paper submission deadline :
December 15th, 2021Deadline extension: January 31th, 2022
- Author notification : March 8th, 2022
- Camera ready deadline : March 22th, 2022
- Conference : June 1rst-3rd, 2022
Submissions can be submitted in easychair and select the track “SS - Analysis and learning of multi-variate, multi-temporal, multi-resolution and multi-source remote sensing data”.