Digital Typhoon: Long-term Satellite Image Dataset for the
  Spatio-Temporal Modeling of Tropical Cyclones
Asanobu Kitamoto Jared Hwang Bastien Vuillod Lucas Gautier Yingtao Tian Tarin Clanuwat
This paper presents the official release of the Digital Typhoon dataset, the
longest typhoon satellite image dataset for 40+ years aimed at benchmarking
machine learning models for long-term spatio-temporal data. To build the
dataset, we developed a workflow to create an infrared typhoon-centered image
for cropping using Lambert azimuthal equal-area projection referring to the
best track data. We also address data quality issues such as inter-satellite
calibration to create a homogeneous dataset. To take advantage of the dataset,
we organized machine learning tasks by the types and targets of inference, with
other tasks for meteorological analysis, societal impact, and climate change.
The benchmarking results on the analysis, forecasting, and reanalysis for the
intensity suggest that the dataset is challenging for recent deep learning
models, due to many choices that affect the performance of various models. This
dataset reduces the barrier for machine learning researchers to meet
large-scale real-world events called tropical cyclones and develop machine
learning models that may contribute to advancing scientific knowledge on
tropical cyclones as well as solving societal and sustainability issues such as
disaster reduction and climate change. The dataset is publicly available at
http://agora.ex.nii.ac.jp/digital-typhoon/dataset/ and
https://github.com/kitamoto-lab/digital-typhoon/.