Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities

Z Ma, G Mei, N Xu - Artificial Intelligence Review, 2024 - Springer
Data mining and analysis are critical for preventing or mitigating natural hazards. However,
data availability in natural hazard analysis is experiencing unprecedented challenges due to …

Soil moisture estimation using Sentinel-1/-2 imagery coupled with cycleGAN for time-series gap filing

N Efremova, MEA Seddik… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Fast soil moisture content (SMC) mapping is necessary to support water resource
management and to understand crop growth, quality, and yield. Therefore, earth observation …

Deeplearning-based approach to improving numerical weather forecasts

АY Doroshenko, VM Shpyg… - PROBLEMS IN …, 2023 - pp.isofts.kiev.ua
This paper briefly describes the history of numerical weather prediction development. The
difficulties, which occur in the modelling of atmospheric processes, their nature and possible …

Loosely conditioned emulation of global climate models with generative adversarial networks

A Ayala, C Drazic, B Hutchinson, B Kravitz… - arXiv preprint arXiv …, 2021 - arxiv.org
Climate models encapsulate our best understanding of the Earth system, allowing research
to be conducted on its future under alternative assumptions of how human-driven climate …

Towards lifelong self-supervision for unpaired image-to-image translation

V Schmidt, MN Sreedhar, M ElAraby, I Rish - arXiv preprint arXiv …, 2020 - arxiv.org
Unpaired Image-to-Image Translation (I2IT) tasks often suffer from lack of data, a problem
which self-supervised learning (SSL) has recently been very popular and successful at …

Conditional generation of cloud fields

N Mahfouz, Y Ming, K Smith - IGARSS 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
Processes related to cloud physics constitute the largest remaining scientific uncertainty in
climate models and projections. This uncertainty stems from the coarse nature of current …