7 papers
Coarse-to-fine spatial GLMM for scalable prediction and multiscale analysis
Daisuke Murakami, Alexis Comber, Takahiro Yoshida +3
We develop CF-GLMM, a scalable and covariance-free framework for spatial generalized linear mixed models with exponential-family responses, by extending coarse-to-fine spatial mode…
Coarse-to-fine spatial modeling: A scalable, machine-learning-compatible spatial model
Daisuke Murakami, Alexis Comber, Takahiro Yoshida +3
This study proposes coarse-to-fine spatial modeling (CFSM) as a scalable and machine learning-compatible alternative to conventional spatial process models. Unlike conventional cov…
SALPA: Spaceborne LiDAR Point Adjustment for Enhanced GEDI Footprint Geolocation
Narumasa Tsutsumida, Rei Mitsuhashi, Yoshito Sawada +1
Spaceborne Light Detection and Ranging (LiDAR) systems, such as NASA's Global Ecosystem Dynamics Investigation (GEDI), provide forest structure for global carbon assessments. Howev…
Automated flood detection from Sentinel-1 GRD time series using Bayesian analysis for change point problems
Narumasa Tsutsumida, Tomohiro Tanaka, Nifat Sultana
Current Synthetic Aperture Radar (SAR)-based flood detection methods face critical limitations that hinder operational deployment. Supervised learning approaches require extensive…
Sensor-Adaptive Flood Mapping with Pre-trained Multi-Modal Transformers across SAR and Multispectral Modalities
Tomohiro Tanaka, Narumasa Tsutsumida
Floods are increasingly frequent natural disasters causing extensive human and economic damage, highlighting the critical need for rapid and accurate flood inundation mapping. Whil…
A Scalable k-Medoids Clustering via Whale Optimization Algorithm
Huang Chenan, Narumasa Tsutsumida
Unsupervised clustering has emerged as a critical tool for uncovering hidden patterns in vast, unlabeled datasets. However, traditional methods, such as Partitioning Around Medoids…