Natural disasters are one of the oldest threats to both individuals and the societies they co-exist in. As a result, humanity has ceaselessly sought way to provide assistance to people in need after disasters have struck. Further, natural disasters are but a single, extreme example of the many possible humanitarian crises. Disease outbreak, famine, and oppression against disadvantaged groups can pose even greater dangers to people that have less obvious solutions. In this proposed workshop, we seek to bring together the Artificial Intelligence (AI) and Humanitarian Assistance and Disaster Response (HADR) communities in order to bring AI to bear on real-world humanitarian crises. Through this workshop, we intend to establish meaningful dialogue between the communities.
By the end of the workshop, the NeurIPS research community can come to understand the practical challenges of aiding those who are experiencing crises, while the HADR community can understand the landscape that is the state of art and practice in AI. Through this, we seek to begin establishing a pipeline of transitioning the research created by the NeurIPS community to real-world humanitarian issues.
| Introduction | |
| Retrospectives on the Deployment of a Flood Segmentation Deep Learning Model Into a Near-Real-Time Monitoring Service (Retrospective) | |
| Automated Labeling of Civil Air Patrol Imagery for Hurricane Ida (Retrospective) | |
| Jonathan Stock - Director, United State Geological Survey Innovation Center (Invited Talk) | |
| David Merrick - Director, Emergency Management and Homeland Security Program at FSU (Invited Talk) | |
| Break | |
| A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles (Oral) | |
| Disaster Mapping From Satellites: Damage Detection with Crowdsourced Point Labels (Oral) | |
| Creating a Coefficient of Change in the Built Environment After a Natural Disaster (Oral) | |
| Lunch (Break) | |
| Emily Aiken - Graduate Student Researcher, University of California, Berkeley (Invited Talk) | |
| Tomas Svoboda - Professor, Czech Technical University in Prague (Invited Talk) | |
| Damage Estimation and Localization from Sparse Aerial Imagery (Oral) | |
| NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks (Oral) | |
| Synthetic Weather Radar Using Hybrid Quantum-Classical Machine Learning (Oral) | |
| Unsupervised Change Detection of Extreme Events Using ML On-Board (Oral) | |
| Break | |
| Poster Session | |
| Small Unmanned Aerial Systems for Wildfire Response (Challenge Discussion) | |
| Multi-Modal Data Fusion and Machine Learning for Disaster Response (Challenge Discussion) | |
| Autonomous Debris Pile Estimation (Challenge Discussion) | |
| Challenge Discussion (GatherTown Discussion) | |
| Building Damage Mapping with Self-Positive Unlabeled Learning (Poster) | |
| Deep Learning Methods for Daily Wildfire Danger Forecasting (Poster) | |
| Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment (Poster) | |
| Damage Estimation and Localization from Sparse Aerial Imagery (Poster) (Poster) | |
| Mapping Access to Water and Sanitation in Colombia using Publicly Accessible Satellite Imagery, Crowd-sourced Geospatial Information, and Random Forests (Poster) | |
| A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles (Poster) (Poster) | |
| Disaster Mapping From Satellites: Damage Detection with Crowdsourced Point Labels (Poster) (Poster) | |
| Unsupervised Change Detection of Extreme Events Using ML On-Board (Poster) (Poster) | |
| NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks (Poster) (Poster) | |
| Creating a Coefficient of Change in the Built Environment After a Natural Disaster (Poster) (Poster) | |
| Synthetic Weather Radar Using Hybrid Quantum-Classical Machine Learning (Poster) (Poster) | |
| Fully Convolutional Siamese Neural Networks for Buildings Damage Assessment from Satellite Images (Poster) | |
| On Pseudo-Absence Generation and Machine Learning for Locust Breeding Ground Prediction in Africa (Poster) | |