"Impact of Natural Disasters on Industrial Agglomeration: A Case of the 1923 Great Kanto Earthquake"

One of the main implications of the New Economic Geography, developed since the 1990s, is that a temporary shock can create a persistent impact on the geographic distribution of economic activities because of multiple equilibria. This paper investigates the long-run impact of a temporary shock on the geographic distribution of industries in Tokyo Prefecture, Japan, using the Great Kanto Earthquake in 1923 as a natural experiment. It is revealed that the temporary shock from the Great Kanto Earthquake was basically dissipated by 1936, just before the full-scale war with China broke out. On the other hand, through industry-level investigation, it is found that with respect to the machinery and metal industry, the impact was persistent and remained even in 1936. These findings suggest the importance of the industrial structure and transaction networks within industry in the mechanism determining geographic distribution of industries.

Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery

Thanks to recent advances in CNNs, solid improvements have been made in semantic segmentation of high resolution remote sensing imagery. However, most of the previous works have not fully taken into account the specific difficulties that exist in remote sensing tasks. One of such difficulties is that objects are small and crowded in remote sensing imagery. To tackle with this challenging task we have proposed a novel architecture called local feature extraction (LFE) module attached on top of dilated front-end module. The LFE module is based on our findings that aggressively increasing dilation factors fails to aggregate local features due to sparsity of the kernel, and detrimental to small objects. The proposed LFE module solves this problem by aggregating local features with decreasing dilation factor. We tested our network on three remote sensing datasets and acquired remarkably good results for all datasets especially for small objects