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Found 5 papers, 3 papers with code
Learning Geospatial Region Embedding with Heterogeneous Graph
In this paper, we present GeoHG, an effective heterogeneous graph structure for learning comprehensive region embeddings for various downstream tasks.
UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation
Urbanization challenges underscore the necessity for effective satellite image-text retrieval methods to swiftly access specific information enriched with geographic semantics for urban applications.
UrbanVLP: Multi-Granularity Vision-Language Pretraining for Urban Region Profiling
Urban region profiling aims to learn a low-dimensional representation of a given urban area while preserving its characteristics, such as demographics, infrastructure, and economic activities, for urban planning and development.
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e. g., geographical, traffic, social media, and environmental data) and modalities (e. g., spatio-temporal, visual, and textual modalities).
UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web
To answer the questions, we leverage the power of Large Language Models (LLMs) and introduce the first-ever LLM-enhanced framework that integrates the knowledge of textual modality into urban imagery profiling, named LLM-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining (UrbanCLIP).
