Accepted Papers

Papers

• Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment

  • Donghoon Shin (Seoul National University)*; Sachin Grover (Carnegie Mellon University); Kenneth Holstein (Carnegie Mellon University); Adam Perer (Carnegie Mellon University)

• Synthetic weather radar using hybrid quantum-classical machine learning

  • Graham Enos (Rigetti Computing)*

• Mapping Access to Water and Sanitation in Colombia using Publicly Accessible Satellite Imagery, Crowd-sourced Geospatial Information, and Random Forests

  • Niccolo Dejito (Thinking Machines Data Science)*

• Deep Learning Methods for Daily Wildfire Danger Forecasting

  • Ioannis Prapas (National Observatory of Athens)*; Spyros Kondylatos (National Observatory of Athens); IOANNIS PAPOUTSIS (National Observatory of Athens); Gustau Camps-Valls (Universitat de València); Michele Ronco (Universitat de València); Miguel-Ángel Fernández-Torres (Universitat de València); Maria Piles Guillem (Universitat de València); Nuno Carvalhais (Max Planck Institute for Biogeochemistry)

• NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks

  • Jie Wei (City College of New York)*

• Damage Estimation and Localization from Sparse Aerial Imagery

  • Rene A Garcia Franceschini (MIT)*; Jeffrey Liu (MIT Lincoln Laboratory); Saurabh Amin (MIT)

• Creating A Coefficient of Change in the Built Environment After a Natural Disaster

  • Karla Saldana Ochoa (ETH)*

• A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles

  • Haoran Su (New York University)*; Yaofeng Desmond Zhong (Siemens); Biswadip Dey (Siemens Corporation, Technology); Chakraborty Amit (Siemens)

• Building Damage Mapping with Self-Positive Unlabeled Learning

  • Junshi Xia (RIKEN)*; Naoto  YOKOYA (University of Tokyo); Bruno Adriano (RIKEN Center for Advanced Intelligence Project (AIP))

• On pseudo-absence generation and machine learning for locust breeding ground prediction in Africa

  • Ibrahim Yusuf (InstaDeep); Kale-ab Tessera (InstaDeep); Thomas Tumiel (InstaDeep); Sella Nevo (Google Research); Arnu Pretorius (InstaDeep)*

• Disaster mapping from satellites: damage detection with crowdsourced point labels

  • Danil Kuzin (Lancaster University)*; Olga Isupova (University of Oxford); Brooke Simmons (Lancaster University); Steven Reece (University of Oxford)

• Fully convolutional Siamese neural networks for buildings damage assessment from satellite images

  • Eugene Khvedchenya (ODS.ai)*; Tatiana Gabruseva

• Unsupervised change detection of extreme events using ML on-board

  • Vít Růžička (University of Oxford)*; Anna Vaughan (Univeristy of Cambridge); Daniele De Martini (University of Oxford); James Fulton (University of Edinburgh); Valentina Salvatelli (IQVIA); Christopher Bridges (University of Surrey); Gonzalo Mateo-Garcia (University of Valencia); Valentina Zantedeschi (INRIA, UCL)

Retrospectives

• Automated Labeling of Civil Air Patrol Imagery for Hurricane Ida

  • Katherine R Picchione (MIT Lincoln Laboratory)*; Jeffrey Liu (MIT Lincoln Laboratory); Chad Counciul (MIT Lincoln Laboratory)

• Retrospectives on the Deployment of a Flood Segmentation Deep Learning Model Into a Near-Real-Time Monitoring Service

  • Edoardo Nemni (United Nations Satellite Centre (UNOSAT))*; Joseph Bullock (UN Global Pulse); Samir Belabbes (United Nations Satellite Centre (UNOSAT)); Khaled Mashfiq (United Nations Satellite Centre (UNOSAT)); Sami Tabbara (United Nations Operational Satellite Application Programme (UNITAR-UNOSAT)); Jakrapong Tawala (United Nations Satellite Centre (UNOSAT)); Teodoro Hunger (United Nations Operational Satellite Application Programme (UNITAR-UNOSAT)); Wittawat Bunnasarn (United Nations Operational Satellite Application Programme (UNITAR-UNOSAT)); Suwanan Sateiandee (United Nations Satellite Centre (UNOSAT)); Jialin Yan (Wuhan University); Haojun Cai (ETH Zurich); Fei  Liu (Technical University of Munich); Sofia Vallecorsa (CERN); Miguel Luengo-Oroz (UNGP); Lars Bromley (UNOSAT); Luca Dell'Oro (United Nations Operational Satellite Application Programme (UNITAR-UNOSAT))

Challenges

• Small Unmanned Aerial Systems for Wildfire Response

  • Andrew Jong (Carnegie Mellon University)

• Multi-Modal Machine Learning for HADR

  • Lexie Yang, Debraj De, Gautam Thakur, Melanie Laverdiere (Oak Ridge National Laboratory)

• LiDAR for Building and Debris Pile Estimation

  • Robin Murphy (Texas A&M University), Bill Basener (University of Virginia, Rochester Institute of Technology)