Walid al shaar (25 risultati)

Condizione: New.

Condizione: New.

Hardback. Condizione: New.

Hardback. Condizione: New.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condizione: new. Hardcover. The convergence of artificial intelligence (AI) and geospatial technologies has shifted how organizations monitor, assess, and manage environmental and societal risks. Natural disasters, climate change, pandemics, and human-induced hazards are increasing in frequency and intensity, causing unprecedented economic losses. Traditional risk management approaches, reliant on reactive measures and limited data processing capabilities, are often inadequate when addressing the complexity, scale, and velocity of current risks. Geospatial AI (GeoAI) represents a transformative advancement in risk management, enabling real-time hazard detection, predictive risk modelling, automated damage assessment, and intelligent decision support systems. Further research may bridge the gap between the technical aspects of GeoAI and practical applications in risk management across multiple hazard types and geographical contexts. Geospatial AI for Disaster Risk Intelligence and Monitoring examines GeoAI applications across the disaster risk management continuum. It explores cutting-edge methodologies including deep learning for hazard mapping, explainable AI for risk communication, edge computing for real-time monitoring, and digital twins for urban resilience. Covering topics such as early warning systems, environmental monitoring, and urban flood risk assessment, this book is a valuable resource for engineers, policymakers, disaster response personnel, researchers, and environmental scientists. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condizione: new. Hardcover. The twenty-first century faces a pressing paradox: while technological advancement accelerates, global food insecurity still threatens millions. Climate change, population growth, and resource constraints have exposed critical weaknesses in agricultural systems. Addressing these challenges requires a shift toward data-driven, resilient farming practices. Geospatial Artificial Intelligence (GeoAI), combining AI, machine learning, and geographic information science, enables the analysis of spatial data from satellites, drones, and IoT sensors. By providing real-time insights into soil, crops, water, and climate, GeoAI supports smarter decisions that improve productivity, reduce waste, and strengthen food system resilience. Transforming Global Food Security and Agricultural Sustainability With Geospatial AI (GeoAI) explores how GeoAI is redefining the agricultural landscape across scales, from local farms to global supply chains. Through interdisciplinary perspectives that blend data science, environmental policy, and sustainable development, this book bridges the gap between technological potential and real-world application. Covering topics such as plant disease detection, apple health monitoring, and precision crop mapping, this book is an excellent academic resource for graduate and doctoral students, agricultural research scientists, data scientists, AI engineers, policymakers, and more. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. The twenty-first century faces a pressing paradox: while technological advancement accelerates, global food insecurity still threatens millions. Climate change, population growth, and resource constraints have exposed critical weaknesses in agricultural systems. Addressing these challenges requires a shift toward data-driven, resilient farming practices. Geospatial Artificial Intelligence (GeoAI), combining AI, machine learning, and geographic information science, enables the analysis of spatial data from satellites, drones, and IoT sensors. By providing real-time insights into soil, crops, water, and climate, GeoAI supports smarter decisions that improve productivity, reduce waste, and strengthen food system resilience. Transforming Global Food Security and Agricultural Sustainability With Geospatial AI (GeoAI) explores how GeoAI is redefining the agricultural landscape across scales, from local farms to global supply chains. Through interdisciplinary perspectives that blend data science, environmental policy, and sustainable development, this book bridges the gap between technological potential and real-world application. Covering topics such as plant disease detection, apple health monitoring, and precision crop mapping, this book is an excellent academic resource for graduate and doctoral students, agricultural research scientists, data scientists, AI engineers, policymakers, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. The twenty-first century faces a pressing paradox: while technological advancement accelerates, global food insecurity still threatens millions. Climate change, population growth, and resource constraints have exposed critical weaknesses in agricultural systems. Addressing these challenges requires a shift toward data-driven, resilient farming practices. Geospatial Artificial Intelligence (GeoAI), combining AI, machine learning, and geographic information science, enables the analysis of spatial data from satellites, drones, and IoT sensors. By providing real-time insights into soil, crops, water, and climate, GeoAI supports smarter decisions that improve productivity, reduce waste, and strengthen food system resilience. Transforming Global Food Security and Agricultural Sustainability With Geospatial AI (GeoAI) explores how GeoAI is redefining the agricultural landscape across scales, from local farms to global supply chains. Through interdisciplinary perspectives that blend data science, environmental policy, and sustainable development, this book bridges the gap between technological potential and real-world application. Covering topics such as plant disease detection, apple health monitoring, and precision crop mapping, this book is an excellent academic resource for graduate and doctoral students, agricultural research scientists, data scientists, AI engineers, policymakers, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The twenty-first century faces a pressing paradox: while technological advancement accelerates, global food insecurity still threatens millions. Climate change, population growth, and resource constraints have exposed critical weaknesses in agricultural systems. Addressing these challenges requires a shift toward data-driven, resilient farming practices. Geospatial Artificial Intelligence (GeoAI), combining AI, machine learning, and geographic information science, enables the analysis of spatial data from satellites, drones, and IoT sensors. By providing real-time insights into soil, crops, water, and climate, GeoAI supports smarter decisions that improve productivity, reduce waste, and strengthen food system resilience. Transforming Global Food Security and Agricultural Sustainability With Geospatial AI (GeoAI) explores how GeoAI is redefining the agricultural landscape across scales, from local farms to global supply chains. Through interdisciplinary perspectives that blend data science, environmental policy, and sustainable development, this book bridges the gap between technological potential and real-world application. Covering topics such as plant disease detection, apple health monitoring, and precision crop mapping, this book is an excellent academic resource for graduate and doctoral students, agricultural research scientists, data scientists, AI engineers, policymakers, and more.…

Hardcover. Condizione: new. Hardcover. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The twenty-first century faces a pressing paradox: while technological advancement accelerates, global food insecurity still threatens millions. Climate change, population growth, and resource constraints have exposed critical weaknesses in agricultural systems. Addressing these challenges requires a shift toward data-driven, resilient farming practices. Geospatial Artificial Intelligence (GeoAI), combining AI, machine learning, and geographic information science, enables the analysis of spatial data from satellites, drones, and IoT sensors. By providing real-time insights into soil, crops, water, and climate, GeoAI supports smarter decisions that improve productivity, reduce waste, and strengthen food system resilience. Transforming Global Food Security and Agricultural Sustainability With Geospatial AI (GeoAI) explores how GeoAI is redefining the agricultural landscape across scales, from local farms to global supply chains. Through interdisciplinary perspectives that blend data science, environmental policy, and sustainable development, this book bridges the gap between technological potential and real-world application. Covering topics such as plant disease detection, apple health monitoring, and precision crop mapping, this book is an excellent academic resource for graduate and doctoral students, agricultural research scientists, data scientists, AI engineers, policymakers, and more.…

Paperback. Condizione: new. Paperback. The convergence of artificial intelligence (AI) and geospatial technologies has shifted how organizations monitor, assess, and manage environmental and societal risks. Natural disasters, climate change, pandemics, and human-induced hazards are increasing in frequency and intensity, causing unprecedented economic losses. Traditional risk management approaches, reliant on reactive measures and limited data processing capabilities, are often inadequate when addressing the complexity, scale, and velocity of current risks. Geospatial AI (GeoAI) represents a transformative advancement in risk management, enabling real-time hazard detection, predictive risk modelling, automated damage assessment, and intelligent decision support systems. Further research may bridge the gap between the technical aspects of GeoAI and practical applications in risk management across multiple hazard types and geographical contexts. Geospatial AI for Disaster Risk Intelligence and Monitoring examines GeoAI applications across the disaster risk management continuum. It explores cutting-edge methodologies including deep learning for hazard mapping, explainable AI for risk communication, edge computing for real-time monitoring, and digital twins for urban resilience. Covering topics such as early warning systems, environmental monitoring, and urban flood risk assessment, this book is a valuable resource for engineers, policymakers, disaster response personnel, researchers, and environmental scientists. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. The convergence of artificial intelligence (AI) and geospatial technologies has shifted how organizations monitor, assess, and manage environmental and societal risks. Natural disasters, climate change, pandemics, and human-induced hazards are increasing in frequency and intensity, causing unprecedented economic losses. Traditional risk management approaches, reliant on reactive measures and limited data processing capabilities, are often inadequate when addressing the complexity, scale, and velocity of current risks. Geospatial AI (GeoAI) represents a transformative advancement in risk management, enabling real-time hazard detection, predictive risk modelling, automated damage assessment, and intelligent decision support systems. Further research may bridge the gap between the technical aspects of GeoAI and practical applications in risk management across multiple hazard types and geographical contexts. Geospatial AI for Disaster Risk Intelligence and Monitoring examines GeoAI applications across the disaster risk management continuum. It explores cutting-edge methodologies including deep learning for hazard mapping, explainable AI for risk communication, edge computing for real-time monitoring, and digital twins for urban resilience. Covering topics such as early warning systems, environmental monitoring, and urban flood risk assessment, this book is a valuable resource for engineers, policymakers, disaster response personnel, researchers, and environmental scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. The convergence of artificial intelligence (AI) and geospatial technologies has shifted how organizations monitor, assess, and manage environmental and societal risks. Natural disasters, climate change, pandemics, and human-induced hazards are increasing in frequency and intensity, causing unprecedented economic losses. Traditional risk management approaches, reliant on reactive measures and limited data processing capabilities, are often inadequate when addressing the complexity, scale, and velocity of current risks. Geospatial AI (GeoAI) represents a transformative advancement in risk management, enabling real-time hazard detection, predictive risk modelling, automated damage assessment, and intelligent decision support systems. Further research may bridge the gap between the technical aspects of GeoAI and practical applications in risk management across multiple hazard types and geographical contexts. Geospatial AI for Disaster Risk Intelligence and Monitoring examines GeoAI applications across the disaster risk management continuum. It explores cutting-edge methodologies including deep learning for hazard mapping, explainable AI for risk communication, edge computing for real-time monitoring, and digital twins for urban resilience. Covering topics such as early warning systems, environmental monitoring, and urban flood risk assessment, this book is a valuable resource for engineers, policymakers, disaster response personnel, researchers, and environmental scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. The convergence of artificial intelligence (AI) and geospatial technologies has shifted how organizations monitor, assess, and manage environmental and societal risks. Natural disasters, climate change, pandemics, and human-induced hazards are increasing in frequency and intensity, causing unprecedented economic losses. Traditional risk management approaches, reliant on reactive measures and limited data processing capabilities, are often inadequate when addressing the complexity, scale, and velocity of current risks. Geospatial AI (GeoAI) represents a transformative advancement in risk management, enabling real-time hazard detection, predictive risk modelling, automated damage assessment, and intelligent decision support systems. Further research may bridge the gap between the technical aspects of GeoAI and practical applications in risk management across multiple hazard types and geographical contexts. Geospatial AI for Disaster Risk Intelligence and Monitoring examines GeoAI applications across the disaster risk management continuum. It explores cutting-edge methodologies including deep learning for hazard mapping, explainable AI for risk communication, edge computing for real-time monitoring, and digital twins for urban resilience. Covering topics such as early warning systems, environmental monitoring, and urban flood risk assessment, this book is a valuable resource for engineers, policymakers, disaster response personnel, researchers, and environmental scientists. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condizione: Neu. Geospatial AI for Disaster Risk Intelligence and Monitoring | Walid Al-Shaar (u. a.) | Taschenbuch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798260017050 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Hardcover. Condizione: new. Hardcover. The convergence of artificial intelligence (AI) and geospatial technologies has shifted how organizations monitor, assess, and manage environmental and societal risks. Natural disasters, climate change, pandemics, and human-induced hazards are increasing in frequency and intensity, causing unprecedented economic losses. Traditional risk management approaches, reliant on reactive measures and limited data processing capabilities, are often inadequate when addressing the complexity, scale, and velocity of current risks. Geospatial AI (GeoAI) represents a transformative advancement in risk management, enabling real-time hazard detection, predictive risk modelling, automated damage assessment, and intelligent decision support systems. Further research may bridge the gap between the technical aspects of GeoAI and practical applications in risk management across multiple hazard types and geographical contexts. Geospatial AI for Disaster Risk Intelligence and Monitoring examines GeoAI applications across the disaster risk management continuum. It explores cutting-edge methodologies including deep learning for hazard mapping, explainable AI for risk communication, edge computing for real-time monitoring, and digital twins for urban resilience. Covering topics such as early warning systems, environmental monitoring, and urban flood risk assessment, this book is a valuable resource for engineers, policymakers, disaster response personnel, researchers, and environmental scientists. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…