AnchorGK: Anchor-based Incremental and Stratified Graph Learning Framework for Inductive Spatio-Temporal Kriging

Spatio-temporal kriging is a fundamental problem in sensor networks, driven by the sparsity of deployed sensors and the resulting missing observations. Although recent approaches model spatial and temporal correlations, they often under-exploit two practical characteristics of real deployments: the sparse spatial distribution of locations and the heterogeneous availability of auxiliary features across locations. To address these challenges, we propose AnchorGK, an Anchor-based Incremental and Stratified Graph Learning framework for inductive spatio-temporal kriging. AnchorGK introduces anchor locations to stratify the data in a principled manner. Anchors are constructed according to feature availability, and strata are then formed around these anchors. This stratification serves two complementary roles. First, it explicitly represents and continuously updates correlations between unobserved regions and surrounding observed locations within a graph learning framework. Second, it enables the systematic use of all available features across strata via an incremental representation mechanism, mitigating feature incompleteness without discarding informative signals. Building on the stratified structure, we design a dual-view graph learning layer that jointly aggregates feature-relevant and location-relevant information, learning stratum-specific representations that support accurate inference under inductive settings. Extensive experiments on multiple benchmark datasets demonstrate that AnchorGK consistently outperforms state-of-the-art baselines for spatio-temporal kriging.

MMS-VPR: Multimodal Street-Level Visual Place Recognition Dataset and Benchmark

Existing visual place recognition (VPR) datasets predominantly rely on vehicle-mounted imagery, lack multimodal diversity and underrepresent dense, mixed-use street-level spaces, especially in non-Western urban contexts. To address these gaps, we introduce MMS-VPR, a large-scale multimodal dataset for street-level place recognition in complex, pedestrian-only environments. The dataset comprises 78,575 annotated images and 2,512 video clips captured across 207 locations in a ~70,800 $\mathrm{m}^2$ open-air commercial district in Chengdu, China. Each image is labeled with precise GPS coordinates, timestamp, and textual metadata, and covers varied lighting conditions, viewpoints, and timeframes. MMS-VPR follows a systematic and replicable data collection protocol with minimal device requirements, lowering the barrier for scalable dataset creation. Importantly, the dataset forms an inherent spatial graph with 125 edges, 81 nodes, and 1 subgraph, enabling structure-aware place recognition. We further define two application-specific subsets -- Dataset_Edges and Dataset_Points -- to support fine-grained and graph-based evaluation tasks. Extensive benchmarks using conventional VPR models, graph neural networks, and multimodal baselines show substantial improvements when leveraging multimodal and structural cues. MMS-VPR facilitates future research at the intersection of computer vision, geospatial understanding, and multimodal reasoning. The dataset is publicly available at https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR.