Earth Observation Data Analytics Using Machine and Deep Learning

Sanjay Garg is a professor in the Department of Computer Science and Engineering at the Jaypee University of Engineering and Technology, India. His research interests include data science, algorithms and pattern recognition. He has over 30 years of academic and research experience. He has completed six funded research projects sponsored by ISRO under the RESPOND scheme and GUJCOST as principal investigator in the field of earth observation data analytics and image processing. He has supervised 10 doctoral dissertations in the same field. He is a fellow of the Institution of Engineers (India), a senior member of IEEE and a Senior member of ACM. Swati Jain is an associate professor in the Department of Computer Science and Engineering at the Institute of Technology, Nirma University, India. She works in the areas of machine learning, data analytics, and deep learning. She is currently working on four funded research projects, two of them under BRNS Department of Atomic Energy and two by ISRO (Indian Space Research Organization). She has completed one funded research project under ISRO-RESPOND as a CO-PI. She is actively working on establishing a Center of Excellence in-Data Science in association with Binghamton University, USA. She received her PhD degree from Nirma University in the areas of machine learning and image processing. Nitant Dube is the group director of MOSDAC Research at the joint Space Applications Centre (SAC) and ISRO, India. His research fields include satellite image processing, big data analytics, AI/ML and its applications for Earth observation data, geo-intelligence and web-based processing. He is involved in the design and development of software for meteorological and oceanographic applications. He has been responsible for the design and development of data products and information processing systems for Indian remote sensing satellites and has contributed towards the development and operationalization of data processing systems at Indian and International ground stations. He is an ISRO nominated member for the CEOS Working Group on Information System and Services (WGISS). He holds a PhD degree in Computer Science from Nirma University, Ahmedabad (GJ), India. Nebu Varghese is an assistant manager (GIS) in the Land and Municipal Service at Dholera Industrial City Development Limited (DICDL), India. He works in the areas of GIS systems analysis and prepare design for new GIS methodologies, land use mapping, land cover mapping, urban land use analysis, spatial data management, satellite image processing and analysis, machine learning and deep learning. Currently, he is working on regional and city-level planning projects, where he employs the most cutting-edge technologies for building information model (BIM) to GIS Integration with micro-level asset information of all Infrastructure in city development. He has been involved in various government DST, ISRO, and IIMA funded projects and was also involved in the innovation hub Malawi project with the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), India. He is a member of ISPRS. He holds a master's degree in remote sensing & GIS from Sam Higginbottom University of Agriculture, Technology & Sciences (SHUATS), Prayagraj (UP), India.

Chapter 1: Introduction
Part I: Clustering and classification of Earth Observation data

Chapter 2: Deep learning method for crop classification using remote sensing data
Chapter 3: Using optical images to demarcate fields in L band SAR images for effective deep learning based crop classification and crop cover estimation
Chapter 4: Leveraging twin networks for land use land cover classification
Chapter 5: Exploiting artificial immune networks for enhancing RS image classification
Chapter 6: Detection and segmentation of aircrafts in UAV images with a deep learning-based approach



Part II: Rare event detection using Earth Observation data

Chapter 7: A transfer learning approach for hurricane damage assessment using satellite imagery
Chapter 8: Wildfires, volcanoes and climate change monitoring from satellite images using deep neural networks
Chapter 9: A comparative study on torrential slide shortcoming zones and causative factors using machine learning techniques: a case study of an Indian state
Chapter 10: Machine learning paradigm for predicting reservoir property: an exploratory analysis



Part III: Tools and technologies for Earth Observation data

Chapter 11: The application of R software in water science
Chapter 12: Geospatial big data analysis using neural networks
Chapter 13: Software framework for spatiotemporal data analysis and mining of earth observation data
Chapter 14: Conclusion