spatial stratified partitioning

Traditional cross-validation in spatial prediction suffers from biased risk estimation due to distributional mismatches between validation and deployment tasks, including covariate shift and task difficulty shift. This work proposes Target-Weighted Cross-Validation (TWCV), which for the first time incorporates task distribution alignment into spatial prediction by calibrating weights to match the target-domain distribution and enhancing task difficulty diversity through spatial buffering-based resampling. By integrating importance-weighted risk estimation with task descriptor modeling, TWCV substantially reduces estimation bias. Empirical evaluations on both synthetic data and real-world environmental pollution mapping demonstrate that the method yields more accurate and nearly unbiased estimates of deployment risk compared to existing spatial cross-validation approaches.

In spatial prediction tasks—such as weather forecasting and pollution modeling—the validation and prediction locations are fixed and non-overlapping, violating the i.i.d. assumption underlying conventional validation methods (including those correcting for covariate shift), which presume stochastic sampling rather than deterministic spatial sampling. This work formally introduces the notion of *validation consistency*: as the density of validation locations tends to infinity, the validation error must converge arbitrarily closely to the true prediction error. Building upon this principle, we propose the first theoretically guaranteed consistent spatial validation framework, integrating spatial sampling theory with weighted density estimation to accommodate both gridded and irregularly spaced observational structures. We prove its consistency under mild regularity conditions. Empirical evaluation on meteorological and air pollution datasets demonstrates that our method significantly outperforms standard cross-validation and importance-weighting baselines, achieving an average 37% reduction in estimation error.

Species distribution models (SDMs) suffer from spatial autocorrelation (SAC), leading to overly optimistic performance estimates under standard random cross-validation (CV) in spatiotemporal extrapolation. To address this, we systematically evaluate multiple CV strategies—spatial blocking and environmental clustering (ENV)—combined with distinct training paradigms (LAST FOLD vs. RETRAIN), and propose a novel spatiotemporal CV framework explicitly aligned with the intrinsic spatiotemporal structure of SDM data. We demonstrate for the first time that optimizing spatial blocking distance and applying ENV clustering significantly mitigate SAC-induced bias, while the LAST FOLD paradigm prevents SAC recurrence in validation sets, substantially reducing estimation error compared to conventional approaches. Empirical results show SP-422 and ENV achieve Spearman and Pearson correlations of 0.485 and 0.548, respectively, confirming that CV strategy must reflect the underlying spatiotemporal dependence structure. This work establishes a methodological benchmark for robust SDM evaluation.

This paper identifies and systematically analyzes common challenges in spatial data science across mainstream programming languages—R, Python, and Julia—including inconsistent spherical geometry modeling, ambiguous spatial/temporal semantics, conflation of intensive and extensive attributes, poor interoperability between data cube and vector formats, complex cross-package dependencies, and a persistent divide between GIS and physical modeling communities. Through multi-language ecosystem surveys, cross-community comparative analysis, and software engineering abstraction, we propose, for the first time, a cross-language semantic framework for spatial operations. The framework formally defines support types (point vs. block), specifies attribute-type constraints on operation validity, and refactors spherical Simple Features logic. We distill five foundational insights that establish a methodological basis and practical guidance for tool interoperability, pedagogical alignment, and open-source governance in spatial computing.

This work addresses the critical issue of data leakage and hidden stratification in spatiotemporal domains—such as aerial surveillance, precision agriculture, and medical imaging—where conventional random data splits lead to distorted model evaluation. To mitigate these problems, the authors propose a unified training and evaluation framework that integrates Structure-Aware Stratified Partitioning (SASP) with Curriculum Distributionally Robust Optimization (CDRO). SASP constructs rigorously separated validation sets based on spatiotemporal structure to minimize leakage while preserving class balance, whereas CDRO enhances model robustness to distributional shifts through curriculum-based optimization. Empirical results across multiple benchmarks demonstrate substantial improvements in both generalization performance and confidence calibration, while also uncovering failure modes previously obscured by standard evaluation protocols.

This study addresses the limitations of conventional cross-validation methods in accurately capturing the complex spatial relationships between training data and target prediction regions, which often leads to biased model performance estimates. To bridge the methodological gap between random and spatial cross-validation, the authors propose a novel paradigm termed “prediction-domain adaptive evaluation.” This framework dynamically tailors the cross-validation strategy to align with the actual prediction scenario by integrating spatial statistics with machine learning evaluation techniques, thereby enabling an adaptive validation workflow. Extensive simulations demonstrate the robustness of the approach across a continuum from interpolation to extrapolation settings. Empirical results show that the proposed method consistently yields more reliable and accurate estimates of predictive accuracy under diverse data distributions.

This study addresses the lack of systematic best practices in large-scale Earth observation (EO) mapping, which often introduces errors during data preprocessing, model training, inference deployment, and validation, thereby compromising the reliability and scientific credibility of map products. To remedy this, we propose the first end-to-end best practice framework for EO mapping, encompassing the entire workflow from satellite data acquisition to operational map delivery. The framework integrates six core components: EO data infrastructure, preprocessing, machine learning dataset construction, uncertainty quantification, map production and dissemination, and independent validation. Emphasizing the interdependence of these stages, it embeds uncertainty quantification and independent validation as integral elements. By synergizing machine learning, distributed computing, and geospatial validation techniques, the framework establishes a reproducible and scalable mapping pipeline that substantially enhances the quality, consistency, and scientific rigor of EO-derived maps, supported by open-source resources to foster community adoption.

该研究通过消除几何学框架探讨局部最优对象能否由共享部署规则实现，分析信息、架构等因素对缺陷修复的影响。

This study addresses the failure of exchangeability assumptions in spatially dependent data, which leads to inadequate coverage and instability in conformal prediction intervals. To overcome this, we propose a sequential conditioning framework that calibrates residuals through sequential whitening to eliminate spatial heterogeneity. This approach is extended to large-scale networks via nearest-neighbor approximation, with theoretical bounds established for coverage loss under covariance misspecification. The method achieves distribution-free, finite-sample exact coverage and asymptotic oracle efficiency under arbitrary spatial designs. Empirical evaluations demonstrate that the proposed framework yields narrower and more stable prediction intervals. In a PM2.5 application, it effectively identifies regions at risk of undercoverage, significantly outperforming existing global and local methods.

This study addresses the cumbersome deployment of remote sensing deep learning models within Geographic Information Systems (GIS) caused by format incompatibilities and computational disparities. We propose an open-source geospatial inference system featuring a novel decoupled architecture that separates multi-source heterogeneous models from arbitrary computing backends via unified interfaces to abstract underlying differences. Integrated as a QGIS plugin, the system enables automated image tiling, result reassembly, and vision-language model prompt injection, facilitating zero-code interactive real-time inference. Experimental results demonstrate that the system successfully executes parallel comparative evaluations of three heterogeneous models across cloud APIs, remote GPUs, and local CPUs, substantially improving efficiency for tasks such as agricultural parcel segmentation.