activity
20172022
most citedClassification of volcanic ash particles using a convolutional neural network and probability

83 citations · 132 across the 6 of their papers we have counts for

collaborators

6 papers

astro-ph.EP2022★ 32 cited

The spatial distribution of impact craters on Ryugu

Naoyuki Hirata, Tomokatsu Morota, Yuichiro Cho +40

Asteroid 162173 Ryugu has numerous craters. The initial measurement of impact craters on Ryugu, by Sugita et al. (2019), is based on Hayabusa2 ONC images obtained during the first…

astro-ph.EP2022★ 10 cited

Rotational effect as the possible cause of the east-west asymmetric crater rims on Ryugu observed by LIDAR data

Naoyuki Hirata, Noriyuki Namiki, Fumi Yoshida +13

Asteroid 162173 Ryugu is a rubble-pile asteroid, whose top-shape is compatible with models of deformation by spin up. Rims of major craters on Ryugu have an east-west asymmetric pr…

astro-ph.EP2019★ 7 cited

Angle of repose of Martian wet sand using discrete element method: Implication for the seasonal cycle of recurring slope lineae(RSL) by relative humidity

Daigo Shoji, Shoko Imamura, Maya Nakamura +1

Seasonal cycle of Martian relative humidity shows that, in the southern and the northern RSL sites, humidity decreases when RSL increment while humidity is high on the fading seaso…

physics.geo-ph2018★ 83 cited

Classification of volcanic ash particles using a convolutional neural network and probability

Daigo Shoji, Rina Noguchi, Shizuka Otsuki +1

Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex s…

physics.geo-ph2017

New classification method of volcanic ash samples using statistically determined grain types

Rina Noguchi, Hideitsu Hino, Nobuo Geshi +2

We developed a method to classify volcanic ash samples by introducing statistically determined grain types. Using more than 10,000 numbers of automatically measured grain data (par…

physics.geo-ph2017

Shape recognition of volcanic ash by simple convolutional neural network

Daigo Shoji, Rina Noguchi

Shape analyses of tephra grains result in understanding eruption mechanism of volcanoes. However, we have to define and select parameter set such as convexity for the precise discr…