Rainfall data collected by various remote sensing instruments such as radars
or satellites has different space-time resolutions. This study aims to improve
the temporal resolution of radar rainfall products to help with more accurate
climate change modeling and studies. In this direction, we introduce a solution
based on EfficientNetV2, namely EfficientTempNet, to increase the temporal
resolution of radar-based rainfall products from 10 minutes to 5 minutes. We
tested EfficientRainNet over a dataset for the state of Iowa, US, and compared
its performance to three different baselines to show that EfficientTempNet
presents a viable option for better climate change monitoring.

通过利用 EfficientNetV2 技术，本文旨在提高雷达降雨数据的时间分辨率以帮助更准确的气候变化建模和研究。经对佛罗里达州数据集的测试，结果表明 EfficientNetV2 是更好地监测气候变化的可行选项。

EfficientTempNet：雷达降雨的时间超分辨率

EfficientTempNet: Temporal Super-Resolution of Radar Rainfall

This paper introduces EfficientNetV2, a new family of convolutional networks
that have faster training speed and better parameter efficiency than previous
models. To develop this family of models, we use a combination of
training-aware neural architecture search and scaling, to jointly optimize
training speed and parameter efficiency. The models were searched from the
search space enriched with new ops such as Fused-MBConv. Our experiments show
that EfficientNetV2 models train much faster than state-of-the-art models while
being up to 6.8x smaller.
Our training can be further sped up by progressively increasing the image
size during training, but it often causes a drop in accuracy. To compensate for
this accuracy drop, we propose to adaptively adjust regularization (e.g.,
dropout and data augmentation) as well, such that we can achieve both fast
training and good accuracy.
With progressive learning, our EfficientNetV2 significantly outperforms
previous models on ImageNet and CIFAR/Cars/Flowers datasets. By pretraining on
the same ImageNet21k, our EfficientNetV2 achieves 87.3% top-1 accuracy on
ImageNet ILSVRC2012, outperforming the recent ViT by 2.0% accuracy while
training 5x-11x faster using the same computing resources. Code will be
available at this https URL

本文介绍了 EfficientNetV2, 这是一种新的卷积网络家族，具有比以前的模型更快的训练速度和更好的参数效率，同时采用了神经结构搜索和缩放相结合的方法，以共同优化训练速度和参数效率。通过逐渐增加图像大小来训练，我们的 EfficientNetV2 在 ImageNet 和 CIFAR / Cars / Flowers 数据集上显著优于以前的模型。