Remote Sensing and Machine Learning in Conservation: Applications and Techniques
Autor Alireza Sharifien Limba Engleză Paperback – mar 2027
- Summarizes the latest advances in remote sensing and machine learning techniques for conservation
- Provides practical examples for how these technologies are used to address key conservation challenges
- Delivers step-by-step guidance on how to implement these technologies, including funding and policy recommendations
- Outlines the potential applications of these technologies in different conservation settings, from protected areas to urban environments
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Specificații
ISBN-13: 9780443445422
ISBN-10: 0443445427
Pagini: 305
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
ISBN-10: 0443445427
Pagini: 305
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Cuprins
1. Introduction
2. Overview of Remote Sensing Techniques
3. Overview of Machine Learning Techniques
4. Integrating Remote Sensing and Machine Learning for Conservation
5. Applications of Remote Sensing and Machine Learning in Conservation
6. Wildlife Monitoring with Emerging Technologies
7. Real-World Conservation Challenges and Solutions
8. Cloud Computing Platforms for Large-Scale Environmental Data Processing
9. Funding and Policy Recommendations
10. Conclusion
2. Overview of Remote Sensing Techniques
3. Overview of Machine Learning Techniques
4. Integrating Remote Sensing and Machine Learning for Conservation
5. Applications of Remote Sensing and Machine Learning in Conservation
6. Wildlife Monitoring with Emerging Technologies
7. Real-World Conservation Challenges and Solutions
8. Cloud Computing Platforms for Large-Scale Environmental Data Processing
9. Funding and Policy Recommendations
10. Conclusion
Notă biografică
Dr. Alireza Sharifi is Associate Professor of Remote Sensing within Shahid Beheshti University's Department of Surveying Engineering, Iran. He obtained his BSc in Surveying Engineering from Azad University and his MSc and PhD in Remote Sensing Engineering from the University of Tehran, Iran. He brings more than 15 years of experience in the applications of remote sensing and artificial intelligence to landscape biomass and ecological health monitoring. Since 2016, he has operated a consulting business delivering remote sensing and AI solutions for mapping, image processing, data analysis, visualization, and data management. He is a member of several scientific communities and editorial boards, including the International Committee of Space Research, the Iranian Society of Geoinformatics Artificial Intelligence, and the following journals: Spatial Information Research, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Big Data Research, Environmental Earth Sciences, and Information Science and Engineering.