FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation

CF Liu, L Huang, K Yin, S Brody, A Mostafavi - arXiv preprint arXiv …, 2024 - arxiv.org
Near-real time estimation of damage to buildings and infrastructure, referred to as damage
nowcasting in this study, is crucial for empowering emergency responders to make informed …

[HTML][HTML] A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features

H Farahmand, Y Xu, A Mostafavi - Scientific Reports, 2023 - nature.com
Flood nowcasting refers to near-future prediction of flood status as an extreme weather
event unfolds to enhance situational awareness. The objective of this study was to adopt …

Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event

C Robinson, SF Nsutezo, A Ortiz… - Proceedings of the …, 2023 - openaccess.thecvf.com
Rapid and accurate building damage assessments from high-resolution satellite imagery
following a natural disaster is essential to inform and optimize first responder efforts …

[HTML][HTML] Rapid identification of damaged buildings using incremental learning with transferred data from historical natural disaster cases

J Ge, H Tang, N Yang, Y Hu - ISPRS Journal of Photogrammetry and …, 2023 - Elsevier
The accurate extraction of building damage after destructive natural disasters is critical for
disaster rescue and assessment. To achieve a rapid disaster response, training a model …

[HTML][HTML] Scalable approach to create annotated disaster image database supporting AI-driven damage assessment

SH Ro, J Gong - Natural Hazards, 2024 - Springer
As coastal populations surge, the devastation caused by hurricanes becomes more
catastrophic. Understanding the extent of the damage is essential as this knowledge helps …

Change-centric building damage assessment across multiple disasters using deep learning

A Asif, H Rafique, K Jadoon, M Zakwan… - International Journal of …, 2024 - Springer
Natural catastrophes such as floods, earthquakes, and hurricanes often result in widespread
destruction, making post-disaster building damage assessment a challenging task. The xBD …

Building damage detection in post-event high-resolution imagery using deep transfer learning

G Abdi, M Esfandiari, S Jabari - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
One of the most important disaster management requirements is accurate damage map
generation to support rescue and reconstruction efforts. In this application, remote sensing …

Smart flood resilience: harnessing community-scale big data for predictive flood risk monitoring, rapid impact assessment, and situational awareness

A Mostafavi, F Yuan - EGU General Assembly Conference …, 2022 - ui.adsabs.harvard.edu
Background and objective: The fields of urban resilience to flooding and data science are on
a collision course giving rise to the emerging field of smart resilience. The objective of this …

Knowledge graphs to support real-time flood impact evaluation

JM Johnson, T Narock, J Singh-Mohudpur, D Fils… - AI Magazine, 2022 - ojs.aaai.org
A digital map of the built environment is useful for a range of economic, emergency
response, and urban planning exercises such as helping find places in app driven …

Automatic Quantification of Settlement Damage using Deep Learning of Satellite Images

L Lu, W Guo - 2021 IEEE International Smart Cities Conference …, 2021 - ieeexplore.ieee.org
Humanitarian disasters and political violence cause significant damage to our living space.
The reparation cost to homes, infrastructure, and the ecosystem is often difficult to quantify in …