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- Spatial statistics for error analysis — xDEM
Performing error (or uncertainty) analysis of spatial variable, such as elevation data, requires joint knowledge from two scientific fields: spatial statistics and uncertainty quantification Spatial statistics, also referred to as geostatistics in geoscience, is a body of theory for the analysis of spatial variables
- Modeling spatial uncertainty - Stanford Earth
Spatial estimation = What is the best guess for the value at the location where no data was taken ? What is best? Consider a situation where you want to estimate the total amount of pollution of Pb at a specific location You have two methodologies do so Consider that you apply these methodologies to 10 sites Best ? Average square error is zero ?
- Uncertainty-guided sampling to improve digital soil maps
We evaluated the DSM approaches by using the presented pixel-wise uncertainty measure err var, which was normalized across the full spatial domain and its average ERR var ¯ as aggregated measure Moreover, we used the accuracy measures coefficient of determination ( R 2 ) and root mean squared error ( RMSE ) for observed sample sites
- Reliable imputation of spatial transcriptomes with uncertainty . . .
We derive a set of attributes that can accurately predict imputation uncertainty, enabling us to select reliably imputed genes In addition, we introduce a spatial autocorrelation metric as a regularization to avoid overestimating spatial patterns
- Managing Uncertainty of Large Spatial Databases - ACM SIGSPATIAL
In this paper, we investigate how to manage uncertainty in large spatial databases Particularly, we examine probabilistic models for uncertain spatial databases We discuss probabilistic spatial queries (or PSQ), which consider the models of the spatial data uncertainty (instead of just the data value reported), and augment prob-
- GitHub - dcjones maxspin: Quantifying spatial information in spatial . . .
SCVI is a convenient and versatile probabilistic model of sequencing experiments, from which we can sample from the posterior to get normalized point estimates with uncertainty Using Maxspin with SCVI looks something like this: import scvi import numpy as np from maxspin import spatial_information scvi model SCVI setup_anndata (adata)
- (PDF) Spatial Data Uncertainty - ResearchGate
Although not exhaustive, this article reviews fundamental concepts related to spatial data uncertainty and methods the geospatial research communities have developed to understand and represent
- NSVDNet: Normalized Spatial-Variant Diffusion Network for Robust Image . . .
To tackle the above problems, we propose the normalized spatial-variant diffusion network (NSVDNet) based on uncertainty-aware diffusion to enhance performance robustness to input corruption Specifically, the sparse depth is diffused with end-to-end learned spatially adaptive kernels that incorporate (1) the input depth uncertainty to avoid
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