CFP
EDLML-2021: Earth Observation Data Analytics Using Machine And Deep Learning |
Website | https://sites.google.com/view/earthdlml-bookchapter |
Submission link | https://easychair.org/conferences/?conf=edlml2021 |
Abstract registration deadline | December 15, 2021 |
Submission deadline | December 15, 2021 |
Abstract Notification | December 30, 2021 |
Submission of Full Chapter | January 30, 2022 |
Review Notification | March 15, 2022 |
Camera Ready Submission | March 30, 2022 |
EDLML-2021 is Earth Observation Data Analytics Using Machine & Deep Learning
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
- Full papers describing basic properties, features and models for very specific Earth observation (EO) cases recorded by very high-resolution (VHR) multispectral, hyperspectral, Synthetic Aperture Radar (SAR), and multi-temporal observations. It will cover pre-processing methods applied on satellite images and various deep learning techniques for various applications such as identifying land cover features, object detection, cloud detection, crop classification, oil spill detection, target recognition, land subsidence, etc.We will look for the solutions to build pre-trained deep neural networks for: (A) various earth resource applications, (B) pre-processing techniques and (C) annotating objects on earth surface. Well-labelled training images are still key to the quality of any algorithms built upon satellite imagery. Annotating any areas on earth requires a good image interpretation and field visit. We will discuss several automated methods for spatial annotation. Thus, compiling existing knowledge with direct applicability of the technology will open new avenues in this area.
List of Topics
- Introduction and Fundamentals of Geospatial Technology and Deep Learning
- Infusing Geospatial technologies with Artificial Intelligence
- Framework for Remote Sensing Image Pre-processing Using Deep Learning Techniques
- Deep Learning in Classification of Agricultural Remote Sensing Applications
- Applying ML/DL Techniques on Image Fusion for Remote Sensing Applications
- Deep Learning Neural Networks for Land Use Land Cover Mapping
- Wildfires, Volcanoes and Climate Change Monitoring from Satellite Images Using Deep Neural Network
- Mapping of Hyperspectral AVIRIS Data Using Machine Learning and Deep Learning Algorithms
- Automatic Target Detection and Recognition Based on ML & DL for Orthoimagery
- Geospatial Database Management Systems, Analysis & Modeling
- Bottlenecks in Earth Observation Data Analysis
- Conclusions and Future Scope of Deep Learning with Remote Sensing
Committees
Organizing committee
- Dr. Sanjay Garg
- Dr. Swati Jain
- Dr. Nitant Dube
- Er. Nebu Varghese
Publication
EDLML-2021 proceedings will be published in SciTech Publishing (an imprint of the IET)
by IET - Institution of Engineering and Technology
Contact
All questions about submissions should be emailed to queries.iet.earthdlml@gmail.com