Urban downscaling is a link to transfer the knowledge from coarser climate
information to city scale assessments. These high-resolution assessments need
multiyear climatology of past data and future projections, which are complex
and computationally expensive to generate using traditional numerical weather
prediction models. The city of Austin, Texas, USA has seen tremendous growth in
the past decade. Systematic planning for the future requires the availability
of fine resolution city-scale datasets. In this study, we demonstrate a novel
approach generating a general purpose operator using deep learning to perform
urban downscaling. The algorithm employs an iterative super-resolution
convolutional neural network (Iterative SRCNN) over the city of Austin, Texas,
USA. We show the development of a high-resolution gridded precipitation product
(300 m) from a coarse (10 km) satellite-based product (JAXA GsMAP). High
resolution gridded datasets of precipitation offer insights into the spatial
distribution of heavy to low precipitation events in the past. The algorithm
shows improvement in the mean peak-signal-to-noise-ratio and mutual information
to generate high resolution gridded product of size 300 m X 300 m relative to
the cubic interpolation baseline. Our results have implications for developing
high-resolution gridded-precipitation urban datasets and the future planning of
smart cities for other cities and other climatic variables.

使用深度学习算法进行城市降尺度处理，生成高分辨率 300m x 300m 时空分辨率的降水数据，提供智慧城市规划与未来气候变量研究的数据基础。

基于深度学习的城市降水下缩方法：以美国德克萨斯州奥斯汀市为例的智慧城市应用

Urban precipitation downscaling using deep learning: a smart city application over Austin, Texas, USA

In this paper, we propose novel generative models for creating adversarial
examples, slightly perturbed images resembling natural images but maliciously
crafted to fool pre-trained models. We present trainable deep neural networks
for transforming images to adversarial perturbations. Our proposed models can
produce image-agnostic and image-dependent perturbations for both targeted and
non-targeted attacks. We also demonstrate that similar architectures can
achieve impressive results in fooling classification and semantic segmentation
models, obviating the need for hand-crafting attack methods for each task.
Using extensive experiments on challenging high-resolution datasets such as
ImageNet and Cityscapes, we show that our perturbations achieve high fooling
rates with small perturbation norms. Moreover, our attacks are considerably
faster than current iterative methods at inference time.

该研究提出了新型生成模型，用于制造近似自然图像但又能欺骗先前训练好的模型的略微扰动的对抗性样本。通过在具有挑战性的高分辨率数据集上的实验，它证明了这种扰动具有高弄虚率和较小的扰动规模，并且比当前的迭代方法更快。