IEEE Workshop on Machine Learning for Big Data Analytics in Remote Sensing

Introduction to Workshop

The purpose of the 2nd Workshop on Machine Learning for Big Data Analytics in Remote Sensing 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 multidisciplinary nature of the data necessitate advanced, intelligent solutions.

In recent years, the research community has achieved significant 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. Lately, advances in AI for remote sensing big data have seen some success when transitioning algorithms from the laboratory setting to real-world application scenarios. 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.

Topics

• 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 hyperspectral sensors

• Deep learning for disaster response and management

• Processing of remote sensing time-series through deep recurrent networks

Selected high-quality workshop papers will be invited to submit extended versions to a peer reviewed journal special issue on Machine Learning for Big Data Analytics in Remote Sensing.

Important Dates

• Oct 20, 2020: Papers submission

• Nov 10, 2020: Notification of paper acceptance to authors

• Nov 15, 2020: Registration

• Nov 20, 2020: Camera-ready of accepted papers

• Nov 23, 2020: Video-recording submission

• Dec 10 , 2020: Workshop

Submission Guidelines

Please submit a full-length paper (up to 10 pages IEEE 2-column format).

The online submission system is available here.

Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to "formatting instructions" below).

Formatting Instructions

8.5" x 11" (DOC, PDF)

LaTex Formatting Macros

Program ChairS

Dr. Maryam Rahnemoonfar, Computer Vision and Remote Sensing Laboratory, University of Maryland, Baltimore County

Dr. Grant Scott, Center for Geospatial Intelligence, University of Missouri

Program Committee

• Dr. Geoffrey Fox, Indiana University

• Dr. Shawn Newsam, University of California at Merced

• Dr. John Paden, University of Kansas

• Dr. Jie Gong, Rutgers University

• Dr. Carl Salvaggio, Rochester Institute of Technology

• Dr. Nicolas Younan, Mississippi State University

• Dr. Derek Anderson, University of Missouri

• Dr. Saurabh Prasad, University of Houston