Fundamentals Of Capturing And Processing Drone Imagery And Data
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Fundamentals of Capturing and Processing Drone Imagery and Data
Unmanned aircraft systems (UAS) are rapidly emerging as flexible platforms for capturing imagery and other data across the sciences. Many colleges and universities are developing courses on UAS-based data acquisition. Fundamentals of Capturing and Processing Drone Imagery and Data is a comprehensive, introductory text on how to use unmanned aircraft systems for data capture and analysis. It provides best practices for planning data capture missions and hands-on learning modules geared toward UAS data collection, processing, and applications. FEATURES Lays out a step-by-step approach to identify relevant tools and methods for UAS data/image acquisition and processing. Provides practical hands-on knowledge with visual interpretation, well-organized and designed for a typical 16-week UAS course offered on college and university campuses. Suitable for all levels of readers and does not require prior knowledge of UAS, remote sensing, digital image processing, or geospatial analytics. Includes real-world environmental applications along with data interpretations and software used; exercises in chapters 8 through 19 have support materials for free download. Combines the expertise of a wide range of UAS researchers and practitioners across the geospatial sciences. This book provides a general introduction to drones along with a series of hands-on exercises that students and researchers can engage with to learn to integrate drone data into real-world applications. No prior background in remote sensing, GIS, or drone knowledge is needed to use this book. Readers will learn to process different types of UAS imagery for applications (such as precision agriculture, forestry, urban landscapes) and apply this knowledge in environmental monitoring and land-use studies.
Key Methods in Geography
Author: Nicholas Clifford
language: en
Publisher: SAGE Publications Limited
Release Date: 2023-04-13
Key Methods in Geography is the perfect introductory companion, providing an overview of qualitative and quantitative methods for human and physical geography. The fourth edition of this essential and accessible primer covers the breadth of the discipline and offer critical and contextual perspectives on research methods. New coverage takes account of newer technologies and practice, and 9 new chapters bring greater diversity of positionality and perspective to the volume, including decolonial methods, predicting, visualizing and modelling climate and environmental change, and writing up research. Case study examples, summaries and exercises have been included in each chapter to enable learning. This is vital reading for any student undertaking a Geography Methods module as well as a valuable resource for any student embarking on independent research as part of their degree.
Artificial Intelligence in Remote Sensing for Disaster Management
Invest in Artificial Intelligence in Remote Sensing for Disaster Management to gain invaluable insights into cutting-edge AI technologies and their transformative role in effectively monitoring and managing natural disasters. Artificial Intelligence in Remote Sensing for Disaster Management examines the involvement of advanced tools and technologies such as Artificial Intelligence in disaster management with remote sensing. Remote sensing offers cost-effective, quick assessments and responses to natural disasters. In the past few years, many advances have been made in the monitoring and mapping of natural disasters with the integration of AI in remote sensing. This volume focuses on AI-driven observations of various natural disasters including landslides, snow avalanches, flash floods, glacial lake outburst floods, and earthquakes. There is currently a need for sustainable development, near real-time monitoring, forecasting, prediction, and management of natural resources, flash floods, sea-ice melt, cyclones, forestry, and climate changes. This book will provide essential guidance regarding AI-driven algorithms specifically developed for disaster management to meet the requirements of emerging applications.