18 citations · 52 across the 11 of their papers we have counts for
11 papers
Coarse-to-fine spatial GLMM for scalable prediction and multiscale analysis
Daisuke Murakami, Alexis Comber, Takahiro Yoshida +3
We develop a coarse-to-fine generalized linear mixed model (CF-GLMM) for scalable spatial prediction of exponential-family responses. CF-GLMM extends coarse-to-fine spatial modelin…
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…
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…
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…
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…
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…
A Bayesian Inference Approach for Reducing Inter-Investigator Variability in Sampling-Based Land Cover Classification
Narumasa Tsutsumida, Akira Kato
Land cover classification faces persistent challenges with inter-investigator variability and salt-and-pepper noise. Although cloud platforms such as Google Earth Engine have made…
A linearization for stable and fast geographically weighted Poisson regression
Daisuke Murakami, Narumasa Tsutsumida, Takahiro Yoshida +3
Although geographically weighted Poisson regression (GWPR) is a popular regression for spatially indexed count data, its development is relatively limited compared to that found fo…
gwpcorMapper: an interactive mapping tool for exploring geographically weighted correlation and partial correlation in high-dimensional geospatial datasets
Joseph Emile Honour Percival, Narumasa Tsutsumida, Daisuke Murakami +2
Exploratory spatial data analysis (ESDA) plays a key role in research that includes geographic data. In ESDA, analysts often want to be able to visualize observations and local rel…
Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernels
Daisuke Murakami, Narumasa Tsutsumida, Takahiro Yoshida +2
Although a number of studies have developed fast geographically weighted regression (GWR) algorithms for large samples, none of them has achieved linear-time estimation, which is c…
Investigating Spatial Error Structures in Continuous Raster Data
Narumasa Tsutsumida, Pedro Rodríguez-Veiga, Paul Harris +2
The objective of this study is to investigate spatial structures of error in the assessment of continuous raster data. The use of conventional diagnostics of error often overlooks…