IEEE Workshop on Machine Learning for Big Data Analytics in Remote Sensing
Introduction to Workshop
The purpose of the Workshop is to bring together researchers, developers, and practitioners in machine learning, data science, remote sensing, and computer vision communities to address the challenges of machine learning for analyzing big data in remote sensing.
The emergence of small Unmanned Aerial Vehicles (UAV) along with inexpensive sensors presents the opportunity to collect millions of images every day with high flexibility and easy maneuverability. Moreover, space agencies have deployed a large number of Earth observing satellites in the last few years. Despite the ease of data collection, data analysis of the big datasets collected by various sensors remains a significant barrier for scientists and analysts. While traditional analyses provide some insights into the data, the complexity, scale, and multi-disciplinary nature of the data necessitate advanced, intelligent solutions.
In recent years, the research community has witnessed advances in artificial intelligence (AI). Recent advances in deep neural networks (DNNs) and massive datasets have facilitated progress in AI tasks such as image classification, object detection, scene recognition, semantic segmentation, and natural language processing. Despite this progress, these algorithms are limited mainly to electro-optical data and hand-held cameras. There is a critical need to develop more advanced machine learning and deep learning algorithms for various sensors (Multi-spectral, Hyper-spectral, LiDAR, RADAR, Sonar, etc.) collected during remote sensing missions.
- Deep learning with scarce or low-quality annotated data in remote sensing
- Object detection and classification in UAV and satellite imagery
- Semantic segmentation in UAV and satellite imagery
- Remote sensing data fusion with deep learning
- Scene understanding for high resolution imagery
- Deep reinforcement learning in remote sensing
- Semi-supervised learning in remote sensing
- Unsupervised deep learning in remote sensing
- Active learning in remote sensing
- Large-scale datasets for training and testing deep learning solutions to remote sensing problems
- Deep learning for radar and sonar sensors
- Deep learning for multi-spectral and hyper-spectral sensors
- Deep learning for disaster response and management
- Processing of remote sensing time-series through deep recurrent networks
- Oct 15, 2019: Papers submission
- Nov 3, 2019: Notification of paper acceptance to authors
- Nov 18, 2019: Camera-ready of accepted papers
- Dec 11 , 2019: Workshop
Please submit a full-length paper (up to 10 page IEEE 2-column format) through the online submission system.
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to "formatting instructions" below).
Dr. Maryam Rahnemoonfar, Director of Computer Vision and Remote Sensing Laboratory, University of Maryland, Baltimore County
- Dr. Robin Murphy, Texas A&M University
- Dr. Ayman Habib, Purdue University
- Dr. Ning Lin, Princeton University
- Dr. Geoffrey Fox, Indiana University
- Dr. Shawn Newsam, University of California at Merced
- Dr. David Crandall, Indiana University
- Dr. John Paden, University of Kansas
- Dr. Jie Gong, Rutgers University
- Dr. Carl Salvaggio, Rochester Institute of Technology
- Dr. Abdullah Rahman, University of Texas at Rio Grande valley
- Dr. Nicolas Younan, Mississippi State University
- Dr. Grant Scott, University of Missouri
- Dr. Zhangyang (Atlas) Wang, Texas A&M University