Multi-hazard and spatial transferability of a cnn for automated building damage assessment
Automated classification of building damage in remote sensing images enables the rapid
and spatially extensive assessment of the impact of natural hazards, thus speeding up …
and spatially extensive assessment of the impact of natural hazards, thus speeding up …
[PDF][PDF] Early warning systems and their role in disaster risk reduction
In this chapter, we introduce early warning systems (EWS) in the context of disaster risk
reduction, including the main components of an EWS, the roles of the main actors and the …
reduction, including the main components of an EWS, the roles of the main actors and the …
The changing face of accountability in humanitarianism: Using artificial intelligence for anticipatory action
Over the past two decades, humanitarian conduct has been drifting away from the classical
paradigm. This drift is caused by the blurring of boundaries between development aid and …
paradigm. This drift is caused by the blurring of boundaries between development aid and …
Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities
Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data
analysis in business, healthcare, and technology. These methods have innovated the …
analysis in business, healthcare, and technology. These methods have innovated the …
Leveraging data driven approaches for enhanced tsunami damage modelling: Insights from the 2011 Great East Japan event
This study aims at develo** an empirical, multi-variable tsunami damage model for
buildings, based on machine-learning algorithms which leverage about 250.000 ex-post …
buildings, based on machine-learning algorithms which leverage about 250.000 ex-post …
Auditing geospatial datasets for biases: Using global building datasets for disaster risk management
CM Gevaert, T Buunk… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
The presence of biases has been demonstrated in a wide range of machine learning
applications; however, it is not yet widespread in the case of geospatial datasets. This study …
applications; however, it is not yet widespread in the case of geospatial datasets. This study …
[PDF][PDF] The legitimacy, accountability, and ownership of an impact-based forecasting model in disaster governance
S Bierens, K Boersma… - Politics and …, 2020 - research.vu.nl
The Legitimacy, Accountability, and Ownership of an Impact-Based Forecasting Model in
Disaster Governance Page 1 VU Research Portal The legitimacy, accountability, and ownership …
Disaster Governance Page 1 VU Research Portal The legitimacy, accountability, and ownership …
[HTML][HTML] Machine learning models to predict myocardial infarctions from past climatic and environmental conditions
Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low
temperatures (ie heat and cold) may increase the risk of MI. The relationship between health …
temperatures (ie heat and cold) may increase the risk of MI. The relationship between health …
Towards a global impact-based forecasting model for tropical cyclones
MK Forooshani, M van den Homberg… - … Hazards and Earth …, 2024 - search.proquest.com
Tropical cyclones (TCs) produce strong winds and heavy rains accompanied by consecutive
events such as landslides and storm surges, resulting in losses of lives and livelihoods …
events such as landslides and storm surges, resulting in losses of lives and livelihoods …
Towards a global impact-based forecasting model for tropical cyclones
M Kooshki Forooshani… - … Hazards and Earth …, 2024 - nhess.copernicus.org
Tropical cyclones (TCs) produce strong winds and heavy rains accompanied by consecutive
events such as landslides and storm surges, resulting in losses of lives and livelihoods …
events such as landslides and storm surges, resulting in losses of lives and livelihoods …