CALL FOR BOOK CHAPTERS

Earth Observation Data Analytics Using Machine And Deep Learning


About the book

This book will cover 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.


Topics covered

It solicits high-quality and original contribution and will feature thematic content across the following topics of interest, but not limited to:


  • Introduction To Geospatial Technologies.

  • Introduction To Machine And Deep Learning.

  • Infusion Geospatial Technologies With Artificial Intelligence.

  • Framework For Remote Sensing Images 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 Networks.

  • Mapping of Hyperspectral Satellite/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.

Important dates

  • Abstract Submission: February 15, 2022 (Extended)

  • Abstract Notification: February 30, 2022

  • Submission of Full Chapter: March 30, 2022

  • Review Notification: April 15, 2022

  • Camera Ready Submission: April 30, 2022

submission procedure


  • The published book will be submitted to Scopus and WOS for Indexing by the Publisher.

(There is no Publication Fee for this Book).


Dr. Sanjay Garg

Pro-Vice Chancellor,

Indrashil University,

At & Po: Rajpur,

Taluka: Kadi,

Mehsana-382715,

Gujarat, India


Dr. Swati Jain

Associate Professor,

Institute of Technology,

Nirma University,

S G Highway, Gota, Ahmedabad-382481,

Gujarat, India


Dr. Nitant Dube

Group Director,

MOSDAC Research,

Space Applications Centre, ISRO, Bopal Campus,

Ahmedabad-380058,

Gujarat, India


Er. Nebu Varghese

Senior Researcher,

Institute of Technology,

Nirma University,

S G Highway , Gota,

Ahmedabad-382481,

Gujarat, India