Spatial Interpolation
15 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Improving trajectory calculations using deep learning inspired single image superresolution
In a test setup using the Lagrangian particle dispersion model FLEXPART and reduced-resolution wind fields, we demonstrate that absolute horizontal transport deviations of calculated trajectories from "ground-truth" trajectories calculated with undegraded 0. 5{\deg} winds are reduced by at least 49. 5% (21. 8%) after 48 hours relative to trajectories using linear interpolation of the wind data when training on 2{\deg} to 1{\deg} (4{\deg} to 2{\deg}) resolution data.
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning
In our previous work, we designed a systematic policy to prioritize sampling locations to lead significant accuracy improvement in spatial interpolation by using the prediction uncertainty of Gaussian Process Regression (GPR) as "attraction force" to deployed robots in path planning.
Geographic ratemaking with spatial embeddings
This paper presents a method based on data (instead of smoothing historical insurance claim losses) to construct a geographic ratemaking model.
Wind Field Reconstruction with Adaptive Random Fourier Features
In particular, random Fourier features is compared to a set of benchmark methods including Kriging and Inverse distance weighting.
Centralized Information Interaction for Salient Object Detection
Our approach can cooperate with various existing U-shape-based salient object detection methods by substituting the connections between the bottom-up and top-down pathways.
Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions
Previous research has evaluated the potential of image classification methods for detecting road snow coverage by processing images from roadside cameras installed in RWIS (Road Weather Information System) stations.
Bayesian deep learning for mapping via auxiliary information: a new era for geostatistics?
Here we demonstrate the power of feature learning in a geostatistical context, by showing how deep neural networks can automatically learn the complex relationships between point-sampled target variables and gridded auxiliary variables (such as those provided by remote sensing), and in doing so produce detailed maps of chosen target variables.
Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors
Current air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment.
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data
The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services.
Example-Guided Image Synthesis across Arbitrary Scenes using Masked Spatial-Channel Attention and Self-Supervision
Example-guided image synthesis has recently been attempted to synthesize an image from a semantic label map and an exemplary image.
