Yoshihisa Maruyama

Orcid: 0000-0001-8320-7207

According to our database1, Yoshihisa Maruyama authored at least 11 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of six.
  • Erdős number3 of five.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Estimation of Landslides Due to the Combined Disaster of Earthquake and Heavy Rainfall Using Multi-Temporal Lidar Data.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023

2022
Damaged Building Extraction Using Modified Mask R-CNN Model Using Post-Event Aerial Images of the 2016 Kumamoto Earthquake.
Remote. Sens., 2022

Automated Road-Marking Segmentation via a Multiscale Attention-Based Dilated Convolutional Neural Network Using the Road Marking Dataset.
Remote. Sens., 2022

Assessment of Aqueduct Bridge Failure in Wakayama City, Japan, Based on Uav Surveying Flights and High-Resolution Sar Data.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

2021
Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images.
Remote. Sens., 2021

Detection of Collapsed Bridges from Multi-Temporal SAR Intensity Images by Machine Learning Techniques.
Remote. Sens., 2021

Simultaneous Extraction of Road and Centerline from Aerial Images Using a Deep Convolutional Neural Network.
ISPRS Int. J. Geo Inf., 2021

Damage Assessment of Bridges Due to the 2020 July Flood in Japan Using ALOS-2 Intensity Images.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021

2019
Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data.
Remote. Sens., 2019

Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data.
Remote. Sens., 2019

Mapping the inundated area caused by the July 2018 Western Japan torrential rain using multi-temporal ALOS-2 data.
Proceedings of the Joint Urban Remote Sensing Event, 2019


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