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/.

Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the 8th century. Over 3 millions books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts. Thus there has been a great deal of interest in using Machine Learning to automatically recognize these historical texts and transcribe them into modern Japanese characters. Nevertheless, several challenges in Kuzushiji recognition have made the performance of existing systems extremely poor. To tackle these challenges, we propose KuroNet, a new end-to-end model which jointly recognizes an entire page of text by using a residual U-Net architecture which predicts the location and identity of all characters given a page of text (without any pre-processing). This allows the model to handle long range context, large vocabularies, and non-standardized character layouts. We demonstrate that our system is able to successfully recognize a large fraction of pre-modern Japanese documents, but also explore areas where our system is limited and suggest directions for future work.

Following the increasing adoption of online social networks, the users of those services can now easily collect and transmit various types of data instantaneously from real-world locations (e.g., geo-referenced pictures, videos, messages, etc.). This paper argues that one can take advantage of the information that is spread throughout social networks like Twitter, in order to provide a realistic solution for situational awareness that can be useful for decision makers during particular events. Through the generation of English captions assigned to geo-referenced photographs extracted from tweets, and by using these captions to infer important features of those photographs, social monitoring approaches based on the images can produce prompt contextual information that helps in disaster monitoring and response. We specifically describe the use of the Show and Tell model for this purpose. We managed to obtain 100% of precision, 94.42% of accuracy, and 93.93% of recall, in the task of classifying geo-referenced Twitter images depicting the accumulation of snow, useful for applications related to snow removal.

A pixel is not an "atom". Most image processing algorithms basically assume that a pixel is not dividable; in other words, its inside is homogeneous consisting of only one classification class. This assumption is, however, not true for images, in particular remote sensing images, taken by a sensor with coarse resolution. In these images even a single image pixel may contain more data than two classification classes, which means that a pixel has an underlying heterogeneous structure internally. This paper discusses the statistical properties of these heterogeneous pixels, namely mixels. The difference between our work and previous work comes from the formulation of the problem-we focus on overall statistical properties that a set of pixels will show in the image intensity space. This formulation introduces our new stochastic model, mixel density, which also provides a proper interpretation for so-called long-tail density.