Papers Spatial Interpolation
“Spatial Interpolation” 태그가 달린 논문 60편 · 필터 해제
Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning
Evaluating foundation models for crystallographic reasoning requires benchmarks that isolate generalization behavior while enforcing physical constraints. This work introduces a multiscale multicrystal dataset with two p…
HallucinationSpatial InterpolationMODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling
Accurate acquisition of high-resolution surface meteorological conditions is critical for forecasting and simulating meteorological variables. Directly applying spatial interpolation methods to derive meteorological valu…
Spatial InterpolationProbabilistic Spatial Interpolation of Sparse Data using Diffusion Models
The large underlying assumption of climate models today relies on the basis of a "confident" initial condition, a reasonably plausible snapshot of the Earth for which all future predictions depend on. However, given the …
ImputationSpatial InterpolationStochastic Processes with Modified Lognormal Distribution Featuring Flexible Upper Tail
Asymmetric, non-Gaussian probability distributions are often observed in the analysis of natural and engineering datasets. The lognormal distribution is a standard model for data with skewed frequency histograms and fat …
Spatial InterpolationTime Series ForecastingSatellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution
Accurate acquisition of surface meteorological conditions at arbitrary locations holds significant importance for weather forecasting and climate simulation. Due to the fact that meteorological states derived from satell…
Spatial InterpolationWeather ForecastingAdaptively Weighted Averaging Over-the-Air Computation and Its Application to Distributed Gaussian Process Regression
This paper introduces a noise-tolerant computing method for over-the-air computation (AirComp) aimed at weighted averaging, which is critical in various Internet of Things (IoT) applications such as environmental monitor…
Federated LearningGPRregressionSpatial InterpolationT-GMSI: A transformer-based generative model for spatial interpolation under sparse measurements
Generating continuous environmental models from sparsely sampled data is a critical challenge in spatial modeling, particularly for topography. Traditional spatial interpolation methods often struggle with handling spars…
Generative Adversarial NetworkSpatial InterpolationKnowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing Image Segmentation
Fine-grained remote sensing image segmentation is essential for accurately identifying detailed objects in remote sensing images. Recently, vision transformer models (VTMs) pre-trained on large-scale datasets have demons…
Domain AdaptationImage SegmentationSemantic SegmentationSpatial Interpolation+2GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction
Spatial prediction is a fundamental task in geography. In recent years, with advances in geospatial artificial intelligence (GeoAI), numerous models have been developed to improve the accuracy of geographic variable pred…
Conformal PredictionPredictionSpatial InterpolationA Generalized Unified Skew-Normal Process with Neural Bayes Inference
In recent decades, statisticians have been increasingly encountering spatial data that exhibit non-Gaussian behaviors such as asymmetry and heavy-tailedness. As a result, the assumptions of symmetry and fixed tail weight…
Gaussian ProcessesSpatial InterpolationSpatial Transformers for Radio Map Estimation
Radio map estimation (RME) involves spatial interpolation of radio measurements to predict metrics such as the received signal strength at locations where no measurements were collected. The most popular estimators nowad…
Spatial InterpolationCommunity search signatures as foundation features for human-centered geospatial modeling
Aggregated relative search frequencies offer a unique composite signal reflecting people's habits, concerns, interests, intents, and general information needs, which are not found in other readily available datasets. Tem…
Community SearchMissing ValuesSpatial InterpolationCross-Country Comparative Analysis of Climate Resilience and Localized Mapping in Data-Sparse Regions
Climate resilience across sectors varies significantly in low-income countries (LICs), with agriculture being the most vulnerable to climate change. Existing studies typically focus on individual countries, offering limi…
Spatial InterpolationReservoir Static Property Estimation Using Nearest-Neighbor Neural Network
This note presents an approach for estimating the spatial distribution of static properties in reservoir modeling using a nearest-neighbor neural network. The method leverages the strengths of neural networks in approxim…
Spatial InterpolationUncertainty QuantificationA Hybrid Framework for Spatial Interpolation: Merging Data-driven with Domain Knowledge
Estimating spatially distributed information through the interpolation of scattered observation datasets often overlooks the critical role of domain knowledge in understanding spatial dependencies. Additionally, the feat…
Spatial InterpolationBoosting House Price Estimations with Multi-Head Gated Attention
Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relatio…
Spatial InterpolationAID: Attention Interpolation of Text-to-Image Diffusion
Conditional diffusion models can create unseen images in various settings, aiding image interpolation. Interpolation in latent spaces is well-studied, but interpolation with specific conditions like text or poses is less…
Spatial InterpolationThermal Earth Model for the Conterminous United States Using an Interpolative Physics-Informed Graph Neural Network (InterPIGNN)
This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop national temperature-at-depth maps for the conterminous United States. The model was train…
Graph Neural NetworkSpatial InterpolationEnsemble learning for predictive uncertainty estimation with application to the correction of satellite precipitation products
Predictions in the form of probability distributions are crucial for effective decision-making. Quantile regression enables such predictions within spatial prediction settings that aim to create improved precipitation da…
Decision MakingEnsemble LearningFeature Engineeringquantile regression+2NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization
Head-related transfer functions (HRTFs) are important for immersive audio, and their spatial interpolation has been studied to upsample finite measurements. Recently, neural fields (NFs) which map from sound source direc…
Spatial Interpolation