Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities
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 …
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 …
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 …
difficulties, which occur in the modelling of atmospheric processes, their nature and possible …
Loosely conditioned emulation of global climate models with generative adversarial networks
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 …
to be conducted on its future under alternative assumptions of how human-driven climate …
Towards lifelong self-supervision for unpaired image-to-image translation
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 …
which self-supervised learning (SSL) has recently been very popular and successful at …
Conditional generation of cloud fields
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 …
climate models and projections. This uncertainty stems from the coarse nature of current …