Skip to content

SGAP-Place: A Social Semantics-Augmented Representation Learning Framework for Urban Places

Aug 2026 · Journal of Geovisualization and Spatial Analysis · Vol 10 · 0 citations · 72 references

TL;DR

SGAP-Place provides an approach for incorporating higher-order social meanings into place representation and indicates that social semantics can serve as an explicit and useful information layer in place representation.

View source

Similar papers

Preprint Aug 2026

PlaceSeek: Human-Centered Geospatial Retrieval of Urban Outdoor Places via Semantic Grounding and Affective Alignment

People search for urban outdoor places not only by category or function, but also by what activities a place can support and how it is perceived. Existing geospatial retrieval remains largely POIcentric and metadata-driven, making it difficult to satisfy openended, affective, or activity-oriented needs. We present Plac...

Ziqi Cui, Shangyu Lou · 0 citations
#machine learning Preprint Aug 2026

MoRAX: Mobility-based Representation Augmentation for Geospatial Foundation Models

MoRAX, a lightweight framework for augmenting geospatial embeddings with functional structure derived from human mobility, is introduced and transfer results across countries further demonstrate that modulation conditioned on mobility flows provides a general mechanism for grounding geospatial foundations in the human...

Ya Wen, Jixuan Cai, Yu-Lun Zhou et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding

Mobility Stream-Structure Synergy (MoSS) is proposed, which derives complementary views from mobility data: a Sequence view that preserves each region's hourly inflow/outflow profile, and a Structure view that captures how regional connectivity emerges, persists, and dissolves over time.

Namwoo Kim, Jeeyun Chang, Kanghoon Lee et al. · 0 citations
Preprint Aug 2026

CoST: Semantic-Aware Urban Understanding via Spatial-Temporal Alignment

Geospatial representation learning from satellite imagery is a fundamental problem for large-scale urban analysis and real-world applications. Despite recent advances, current methods struggle with cross-region generalization and semantic interpretability due to their reliance on region-specific auxiliary data and the...

Yutian Jiang, Jiabo Liu, Xixuan Hao et al. · 1 citation
Book Open access Aug 2026

UrbanGraphEmbeddings: Learning and Evaluating Spatially Grounded Multimodal Embeddings for Urban Environments

This work proposes UGE, a two-stage training strategy that progressively and stably aligns images, text, and spatial structures by combining instruction-guided contrastive learning with graph-based spatial encoding, and introduces \dataset, a spatially grounded dataset that anchors street-view images to structured spat...

Jie Zhang, Xing-Tong Yu, Yuan Fang et al. · 0 citations
#machine learning Preprint Sep 2026

Plan-to-Synthesis: Cross-City Human Mobility Generation via Semantic Latent Flow Matching

SeMoFlow is proposed, a Semantic human Mobility generation framework based on latent Flow matching that achieves higher trajectory fidelity than existing baselines, preserves city-specific mobility motifs, and supports both joint multi-city generation and effective cross-city transfer.

Zhou-Fu Wang, Bao-Shen Guo, Zhi-Qing Hong et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.