S
Shuhei Hikosaka
Publications - 13
Citations - 517
Shuhei Hikosaka is an academic researcher. The author has contributed to research in topics: Satellite imagery & Convolutional neural network. The author has an hindex of 7, co-authored 12 publications receiving 320 citations.
Papers
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Proceedings ArticleDOI
Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
TL;DR: Li et al. as discussed by the authors proposed a novel architecture called Local Feature Extraction (LFE) module attached on top of dilated front-end module, which is based on their findings that aggressively increasing dilation factors fails to aggregate local features due to sparsity of the kernel, and detrimental to small objects.
Proceedings ArticleDOI
Damage detection from aerial images via convolutional neural networks
TL;DR: The experimental results show that the CNN-based washed-aways detection system achieves 94–96% classification accuracy across all conditions, indicating the promising applicability of CNNs for washed-away building detection.
Proceedings ArticleDOI
Building Detection from Satellite Imagery using Ensemble of Size-Specific Detectors
Ryuhei Hamaguchi,Shuhei Hikosaka +1 more
TL;DR: A simple, but effective multi-task model that learns multiple detectors each of which is dedicated to a specific size of buildings, and implicitly utilizes context information by simultaneously training road extraction task along with building detection task.
Proceedings ArticleDOI
Building change detection via a combination of CNNs using only RGB aerial imageries
TL;DR: The proposed methodology for automatically capturing building changes from remote sensing imageries is developed and it is concluded that use of CNNs enables accurate detection of building changes without employing 3-D information.
Journal ArticleDOI
CNN-based generation of high-accuracy urban distribution maps utilising SAR satellite imagery for short-term change monitoring
TL;DR: In this article, the authors proposed a method to generate high-accuracy urban distribution maps for urban change detection via SAR satellite images based using a convolutional neural network (CNN).