cross-resolution spatial alignment

Remote sensing image fusion across heterogeneous satellite sensors (e.g., Landsat and Sentinel) remains challenging due to spectral response mismatches, temporal misalignment, and spatial resolution disparities. To address this, we propose the first end-to-end super-resolution framework designed explicitly for real-world multi-sensor data. Unlike conventional methods relying on synthetically degraded images, our approach directly aligns and reconstructs HLS30 imagery against high-resolution HLS10 reference data, explicitly modeling spectral–temporal inconsistencies. The framework integrates differentiable geometric registration with spectrum-aware reconstruction modules and is evaluated jointly via quantitative metrics (PSNR/SSIM) and qualitative assessment of structural fidelity. Experiments demonstrate that our method significantly improves spatial resolution consistency in HLS30 data, achieving an average PSNR gain of 2.1 dB while preserving spectral fidelity—establishing a new paradigm for practical super-resolution of heterogeneous remote sensing imagery.

To address scale misalignment and information loss in spatial data arising from aggregation or registration, this paper proposes a Bayesian decomposition framework that maps misaligned observations—including point patterns and aggregated counts—onto a continuous spatial domain, enabling uncertainty-aware inversion under four covariate scenarios. We introduce an INLA-driven iterative linearization integration algorithm and design three covariate field reconstruction strategies: Value Plugin, Joint Uncertainty, and Uncertainty Plugin—explicitly propagating uncertainty while maintaining robustness to model misspecification. The method integrates point processes, hierarchical modeling, and multi-source covariates (raster, polygon, and point). In landslide susceptibility mapping, it substantially improves spatial resolution and predictive reliability. Notably, even under covariate scarcity, the Uncertainty Plugin maintains high accuracy, outperforming conventional interpolation and deterministic inversion approaches.

This work addresses the limitations of existing climate data super-resolution methods, which often focus solely on single-frame spatial information while neglecting temporal dependencies and exhibiting high sensitivity to noise, thereby compromising reconstruction accuracy. To overcome these issues, we propose a temporally enhanced bidirectional alignment framework that, for the first time in climate super-resolution, incorporates a bidirectional temporal alignment mechanism. By employing paired latent-space mappings, our approach unifies spatiotemporal representations and effectively suppresses noise, enabling the exploitation of implicit temporal correlations. Departing from conventional strategies such as optical flow—ill-suited for climate data—our method leverages deep networks for end-to-end optimization, integrating forward–backward alignment with a super-resolution module. Extensive experiments on large-scale real-world climate datasets demonstrate that the proposed framework significantly improves both fine-detail recovery and spatiotemporal consistency.

Map-to-map matching faces three major challenges: absence of ground-truth correspondences, sparse node features, and poor scalability to large-scale maps. To address these, we propose the first fully unsupervised graph neural network framework. Our method introduces (1) pseudo-coordinate encoding to enrich node geometric representations and enable scale-invariant feature learning; (2) an adaptive feature-geometry similarity fusion mechanism jointly optimized with a geometric consistency loss to enhance robustness; and (3) a tiling-based overlapping partitioning strategy coupled with majority-voting post-processing to enable efficient parallel inference. Evaluated on real-world multi-source map datasets, our approach significantly outperforms existing supervised and unsupervised methods—achieving state-of-the-art accuracy, especially under high noise and at large scale. The results demonstrate its effectiveness, scalability, and practical utility for real-world map alignment tasks.

This work addresses non-uniform, nonlinear spatial misalignment between post-disaster orthoimagery from small Unmanned Aerial Systems (sUAS) and prior building vector polygons—a critical yet previously unquantified challenge. Method: We conduct the first large-scale quantitative analysis across 51 orthoimages from nine disasters and 21,600 buildings, revealing an average translational error of 82 pixels and mean IoU of only 0.65; remarkably low angular and distance variances (0.4° and 0.45 px) confirm the absence of spatial consistency—invalidating the common assumption that linear transformations suffice, as often assumed in satellite remote sensing. We propose a rigorous validation framework integrating geometric metrics, IoU-based assessment, and GIS overlay comparison, and introduce the first publicly available benchmark dataset with manually refined ground-truth annotations. Contribution/Results: Our findings expose significant bias risks for downstream AI systems and establish a reproducible, paradigm-shifting foundation for sUAS georegistration research.

This work addresses the challenge that current text-to-video generation models often fail to accurately represent dynamic spatial relationships described in input text, leading to spatially inconsistent or logically implausible outputs. To mitigate this issue, we propose SPATIALALIGN, a framework that fine-tunes models using Direct Preference Optimization (DPO) with zeroth-order regularization to enhance their understanding and generation of dynamic spatial relations. We introduce DSR-SCORE, a geometry-based metric to quantitatively assess alignment between dynamic spatial relationships in generated videos and reference text, and present the first text-video dataset specifically curated to capture diverse dynamic spatial configurations. Experimental results demonstrate that models fine-tuned with SPATIALALIGN significantly outperform existing baselines in aligning dynamic spatial relationships, thereby improving the spatial logical fidelity of generated videos.

This work addresses the limitation of existing remote sensing vision-language models, which predominantly rely on global image-text alignment and thus struggle with fine-grained semantic understanding. To overcome this, we propose GeoAlignCLIP, a novel framework that introduces, for the first time in remote sensing, a multi-granularity consistency learning mechanism. This approach jointly optimizes region-level text alignment and intra-modal consistency through contrastive learning. We also construct RSFG-100k, a new dataset comprising scene descriptions, region annotations, and hard negative samples to provide hierarchical supervisory signals. Extensive experiments demonstrate that our method significantly outperforms current state-of-the-art models across multiple remote sensing benchmarks, exhibiting superior fine-grained alignment capability and enhanced generalization across diverse tasks.

Existing methods for spatial pattern detection lack statistical consistency and struggle to scale to large-scale spatial omics data. This work unifies prevailing approaches within a quadratic form statistical framework, establishing—for the first time—a theoretical foundation for consistent spatial pattern detection. We derive general conditions under which such methods achieve consistency and propose a scalable, corrected algorithm that efficiently and robustly handles datasets with millions of spatial locations. The method’s effectiveness is validated on real single-cell lineage tracing data, demonstrating substantial improvements in both reliability and scalability for large-scale spatial omics analysis.

This study addresses the challenge that existing automated map generalization methods struggle to jointly preserve spatial similarity and cartographic legibility across multiple scales, often treating similarity assessment, constraint modeling, and parameter optimization in isolation. To overcome this limitation, the authors propose a unified similarity-driven framework that formulates map generalization as a constrained multi-scale similarity optimization problem. For the first time, geometric, structural, and learned similarity measures are integrated into the objective function, while cartographic constraints—including legibility, smoothness, and geometric validity—are incorporated through line simplification algorithms. Experimental results demonstrate that the approach adaptively and consistently optimizes parameter configurations across diverse algorithms and scales, achieving high-quality map abstraction that maintains spatial similarity while significantly improving the interpretability and generalizability of parameter control.

To address the low alignment accuracy and high computational cost in unsupervised image alignment, this paper proposes the Dense Cross-Scale Alignment Model (DCAM). Methodologically: (1) a full-spatial correlation module is designed to model pixel-level long-range dependencies, enhancing robustness to misalignment; (2) a Just-Noticeable Difference (JND)-aware mechanism is introduced to guide the model toward human-perceptually salient distortion regions; and (3) a cross-scale feature fusion architecture is developed to enable flexible trade-offs between accuracy and efficiency. Extensive experiments on multiple benchmark datasets demonstrate that DCAM consistently outperforms state-of-the-art methods, achieving simultaneous improvements in alignment accuracy, visual quality, and inference speed. These results validate both the effectiveness and practicality of the proposed approach.