6 citations · 10 across the 13 of their papers we have counts for
5 papers · 1 filter
CoST: Semantic-Aware Urban Understanding via Spatial-Temporal Alignment
Yutian Jiang, Jiabo Liu, Xixuan Hao +1
Geospatial representation learning from satellite imagery is a fundamental problem for large-scale urban analysis and real-world applications. Despite recent advances, current meth…
Geospatial Representation Learning: A Survey from Deep Learning to The LLM Era
Xixuan Hao, Yutian Jiang, Xingchen Zou +6
The ability to transform location-centric geospatial data into meaningful computational representations has become fundamental to modern spatial analysis and decision-making. Geosp…
Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity
Hanqi Jiang, Xixuan Hao, Yuzhou Huang +4
This paper introduces an innovative approach to Medical Vision-Language Pre-training (Med-VLP) area in the specialized context of radiograph representation learning. While conventi…
UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation
Siru Zhong, Xixuan Hao, Yibo Yan +3
Urbanization challenges underscore the necessity for effective satellite image-text retrieval methods to swiftly access specific information enriched with geographic semantics for…
UrbanVLP: Multi-Granularity Vision-Language Pretraining for Urban Socioeconomic Indicator Prediction
Xixuan Hao, Wei Chen, Yibo Yan +4
Urban socioeconomic indicator prediction aims to infer various metrics related to sustainable development in diverse urban landscapes using data-driven methods. However, prevalent…