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Climate change has increased the intensity, frequency, and duration of extreme weather events and natural disasters across the world.
xbd: A dataset for assessing building damage from satellite imagery
Gupta, R.; Hosfelt, R.; Sajeev, S.; Patel, N.; Goodman, B.; Doshi, J.; Heim, E.; Choset, H.; and Gaston, M. 2019 · 1911
Earlier work this paper cites.
Disaster Management Cycle-a theoretical approach
Khan, H.; Vasilescu, L. G.; Khan, A.; et al. 2008 · 2008
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EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts
Requena-Mesa, C.; Benson, V.; Denzler, J.; Runge, J.; and Reichstein, M. 2020 · 2012
Earlier work this paper cites.
Optical satellite imagery for quantifying spatio-temporal dimension of physical exposure in disaster risk assessments
Ehrlich, D.; and Tenerelli, P. 2013 · 2013
Earlier work this paper cites.
Flood hazard and flood risk assessment using a time series of satellite images: A case study in Namibia
Skakun, S.; Kussul, N.; Shelestov, A.; and Kussul, O. 2014 · 2014
Earlier work this paper cites.
A multitask learning view on the earth system model ensemble
Gonçalves, A. R.; Von Zuben, F. J.; and Banerjee, A. 2015 · 2015
Earlier work this paper cites.
Rapid and near real-time assessments of population displacement using mobile phone data following disasters: The 2015 Nepal earthquake
Wilson, R.; zu Erbach-Schoenberg, E.; Albert, M.; Power, D.; Tudge, S.; Gonzalez, M.; Guthrie, S.; Chamberlain, H.; Brooks, C.; Hughes, C.; et al. 2016 · 2015
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The Multimedia Satellite Task at MediaEval 2017
Bischke, B.; Helber, P.; Schulze, C.; Srinivasan, V.; Dengel, A.; and Borth, D. 2017 · 2017
Earlier work this paper cites.
Damage Assessment from Social Media Imagery Data during Disasters
Nguyen, D. T.; Ofli, F.; Imran, M.; and Mitra, P. 2017 · 2017
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PMLB: a large benchmark suite for machine learning evaluation and comparison
Olson, R. S.; La Cava, W.; Orzechowski, P.; Urbanowicz, R. J.; and Moore, J. H. 2017 · 2017
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Crisismmd: Multimodal twitter datasets from natural disasters
Alam, F.; Ofli, F.; and Imran, M. 2018 · 2018
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Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science
Bender, E. M.; and Friedman, B. 2018 · 2018
Earlier work this paper cites.
The Multimedia Satellite Task at MediaEval 2018 Emergency Response for Flooding Events
Bischke, B.; Helber, P.; Zhao, Z.; de Bruijn, J.; and Borth, D. 2018 · 2018
Earlier work this paper cites.
Chen, S. A.; Escay, A.; Haberland, C.; Schneider, T.; Staneva, V.; and Choe, Y. 2018 · 2018
Earlier work this paper cites.
Damage Identification in Social Media Posts using Multimodal Deep Learning
Mouzannar, H.; Rizk, Y.; and Awad, M. 2018 · 2018
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Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment
Resch, B.; Usländer, F.; and Havas, C. 2018 · 2018
Cited alongside, same era.
Papers with code - the latest in machine learning
Robert; Ross; Marcin; Elvis; Guillem; Andrew; and Thomas. 2018 · 2018
Cited alongside, same era.
Big data in natural disaster management: a review
Yu, M.; Yang, C.; and Li, Y. 2018 · 2018
Cited alongside, same era.
The multimedia satellite task at MediaEval 2019
Bischke, B.; Helber, P.; Brugman, S.; Basar, E.; Zhao, Z.; Larson, M. A.; and Pogorelov, K. 2019 · 2019
Cited alongside, same era.
ClimateNet: Bringing the power of Deep Learning to weather and climate sciences via open datasets and architectures
Kashinath, K.; Mudigonda, M.; Mahesh, A.; Chen, J.; Yang, K.; Greiner, A.; and Prabhat, M. 2019 · 2019
Cited alongside, same era.
Floodnet: A high resolution aerial imagery dataset for post flood scene understanding
Rahnemoonfar, M.; Chowdhury, T.; Sarkar, A.; Varshney, D.; Yari, M.; and Murphy, R. R. 2021 · 2021
Later among the works it cites.
Active learning for event detection in support of disaster analysis applications
Said, N.; Ahmad, K.; Conci, N.; and Al-Fuqaha, A. 2021 · 2021
Later among the works it cites.
Hephaestus: A large scale multitask dataset towards InSAR understanding
Bountos, N. I.; Papoutsis, I.; Michail, D.; Karavias, A.; Elias, P.; and Parcharidis, I. 2022 · 2022
Closest in time.
Chowdhury, T.; Murphy, R.; and Rahnemoonfar, M. 2022 · 2022
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FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection
Dewangan, A.; Pande, Y.; Braun, H.-W.; Vernon, F.; Perez, I.; Altintas, I.; Cottrell, G. W.; and Nguyen, M. H. 2022 · 2022
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Deep learning benchmarks and datasets for social media image classification for disaster response
Alam, F.; Ofli, F.; Imran, M.; Alam, T.; and Qazi, U. 2020 · 2020
Cited alongside, same era.
Utility is in the Eye of the User: A Critique of NLP Leaderboards
Ethayarajh, K.; and Jurafsky, D. 2020 · 2020
Cited alongside, same era.
Which tasks should be learned together in multi-task learning?
Standley, T.; Zamir, A.; Chen, D.; Guibas, L.; Malik, J.; and Savarese, S. 2020 · 2020
Cited alongside, same era.
Detecting natural disasters, damage, and incidents in the wild
Weber, E.; Marzo, N.; Papadopoulos, D. P.; Biswas, A.; Lapedriza, A.; Ofli, F.; Imran, M.; and Torralba, A. 2020 · 2020
Cited alongside, same era.
Automated Machine Learning Approaches for Emergency Response and Coordination via Social Media in the Aftermath of a Disaster: A Review
Dwarakanath, L.; Kamsin, A.; Rasheed, R. A.; Anandhan, A.; and Shuib, L. 2021 · 2021
Cited alongside, same era.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Gehrmann, S.; Adewumi, T.; Aggarwal, K.; Ammanamanchi, P. S.; Aremu, A.; Bosselut, A.; Chandu, K. R.; Clinciu, M.-A.; Das, D.; Dhole, K.; Du, W.; Durmus, E.; Dušek, O.; Emezue, C. C.; Gangal, V.; Garbacea, C.; Hashimoto, T.; Hou, Y.; Jernite, Y.; Jhamtani, H.; Ji, Y.; Jolly, S.; Kale, M.; Kumar, D.; Ladhak, F.; Madaan, A.; Maddela, M.; Mahajan, K.; Mahamood, S.; Majumder, B. P.; Martins, P. H.; McMillan-Major, A.; Mille, S.; van Miltenburg, E.; Nadeem, M.; Narayan, S.; Nikolaev, V.; Niyongabo Rubungo, A.; Osei, S.; Parikh, A.; Perez-Beltrachini, L.; Rao, N. R.; Raunak, V.; Rodriguez, J. D.; Santhanam, S.; Sedoc, J.; Sellam, T.; Shaikh, S.; Shimorina, A.; Sobrevilla Cabezudo, M. A.; Strobelt, H.; Subramani, N.; Xu, W.; Yang, D.; Yerukola, A.; and Zhou, J. 2021 · 2021
Cited alongside, same era.
DeepAg: Deep Learning Approach for Measuring the Effects of Outlier Events on Agricultural Production and Policy
Gurrapu, S.; Batarseh, F. A.; Wang, P.; Sikder, M. N. K.; Gorentala, N.; and Gopinath, M. 2021 · 2021
Cited alongside, same era.
Closest in time.
Curating flood extent data and leveraging citizen science for benchmarking machine learning solutions
Gahlot, S.; Ramasubramanian, M.; Gurung, I.; Hansch, R.; Molthan, A.; and Maskey, M. 2022 · 2022
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SpaceNet 8-The Detection of Flooded Roads and Buildings
Hänsch, R.; Arndt, J.; Lunga, D.; Gibb, M.; Pedelose, T.; Boedihardjo, A.; Petrie, D.; and Bacastow, T. M. 2022 · 2022
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Visual sentiment analysis from disaster images in social media
Hassan, S. Z.; Ahmad, K.; Hicks, S.; Halvorsen, P.; Al-Fuqaha, A.; Conci, N.; and Riegler, M. 2022 · 2022
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Climate change 2022: Impacts, adaptation and vulnerability
Pörtner, H.-O.; Roberts, D. C.; Adams, H.; Adler, C.; Aldunce, P.; Ali, E.; Begum, R. A.; Betts, R.; Kerr, R. B.; Biesbroek, R.; et al. 2022 · 2022
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Tackling climate change with machine learning
Rolnick, D.; Donti, P. L.; Kaack, L. H.; Kochanski, K.; Lacoste, A.; Sankaran, K.; Ross, A. S.; Milojevic-Dupont, N.; Jaques, N.; Waldman-Brown, A.; et al. 2022 · 2022
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VIDI: A Video Dataset of Incidents
Sesver, D.; Gençoğlu, A. E.; Yıldız, Ç. E.; Günindi, Z.; Habibi, F.; Yazıcı, Z. A.; and Ekenel, H. K. 2022 · 2022
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Mobile phone usage data for disaster response
Smallwood, T. R.; Lefebvre, V.; and Bengtsson, L. 2022 · 2022
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Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents
Weber, E.; Papadopoulos, D. P.; Lapedriza, A.; Ofli, F.; Imran, M.; and Torralba, A. 2022 · 2022
Closest in time.
Msnet: A multilevel instance segmentation network for natural disaster damage assessment in aerial videos
Zhu, X.; Liang, J.; and Hauptmann, A. 2021 · 2032
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