Remote Sensing And Gis Technologies For Monitoring And Prediction Of Disasters
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Remote Sensing and GIS Technologies for Monitoring and Prediction of Disasters
Author: Shailesh Nayak
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-07-16
Lessons learned in the last several years have given clear indications that the prediction and efficient monitoring of disasters is one of the critical factors in decision-making process. In this respect space-based technologies have the great potential of supplying information in near real time. Earth observation satellites have already demonstrated their flexibility in providing data to a wide range of applications: weather forecasting, person and vehicle tracking, alerting to disaster, forest fire and flood monitoring, oil spills, spread of desertification, monitoring of crop and forestry damages. This book focuses on a wider utilisation of remote sensing in disaster management. The discussed aspects comprise data access/delivery to the users, information extraction and analysis, management of data and its integration with other data sources (airborne and terrestrial imagery, GIS data, etc.), data standardization, organisational and legal aspects of sharing remote sensing information.
AI and Robotics in Disaster Studies
This book promotes a meaningful and appropriate dialogue and cross-disciplinary partnerships on Artificial Intelligence (AI) in governance and disaster management. The frequency and the cost of losses and damages due to disasters are rising every year. From wildfires to tsunamis, drought to hurricanes, floods to landslides combined with chemical, nuclear and biological disasters of epidemic proportions has increased human vulnerability and ecosystem sustainability. Life is not as it used to be and governance to manage disasters cannot be a business as usual. The quantum and proportion of responsibilities with the emergency services has increased many times to strain them beyond their human capacities. Its time that the struggling disaster management services get supported and facilitated by new technology of combining Artificial Intelligence (AI) and Machine Learning (ML) with Data Analytics Technologies (DAT)to serve people and government in disaster management. AI and ML have advanced to a state where they could be utilized for many operations in disaster risk reduction. Even though many disasters cannot be prevented and a number of them are blind natural disasters yet through an appropriate application of AI and ML quick predictions, vulnerability identification and classification of relief and rescue operations could be achieved.
Geospatial Techniques in Urban Hazard and Disaster Analysis
Author: Pamela S. Showalter
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-11-11
This book is the second in a series that examines how geographic information te- nologies (GIT) are being implemented to improve our understanding of a variety of hazard and disaster situations. The main types of technologies covered under the umbrella of GIT, as used in this volume, are geographic information systems, remote sensing (not including ground-penetrating or underwater systems), and global po- tioning systems. Our focus is on urban areas, broadly de ned in order to encompass rapidly growing and densely populated areas that may not be considered “urban” in the conventional sense. The material presented here is also unabashedly applied – our goal is to provide GIT tools to those seeking more ef cient ways to respond to, recover from, mitigate, prevent, and/or model hazard and disaster events in urban settings. Therefore, this book was created not only with our colleagues in the academic world in mind, but also for hazards professionals and practitioners. We also believe graduate students will nd the material presented here of interest, as may upper division undergraduate students.