activity
20242026
collaborators

9 papers

cs.AI2026

Thermodynamic-Inspired Explainable GeoAI: Uncovering Regime-Dependent Mechanisms in Heterogeneous Spatial Systems

Sooyoung Lim, Zhenlong Li, Zi-Kui Liu

Modeling spatial heterogeneity and associated critical transitions remains a fundamental challenge in geography and environmental science. While conventional Geographically Weighte…

cs.AI2026

From Questions to Queries: An AI-powered Multi-Agent Framework for Spatial Text-to-SQL

Ali Khosravi Kazazi, Zhenlong Li, M. Naser Lessani +1

The complexity of SQL and the spatial semantics of PostGIS create barriers for non-experts working with spatial data. Although large language models can translate natural language…

cs.AI2026

Towards Intelligent Geospatial Data Discovery: a knowledge graph-driven multi-agent framework powered by large language models

Ruixiang Liu, Zhenlong Li, Ali Khosravi Kazazi

The rapid growth in the volume, variety, and velocity of geospatial data has created data ecosystems that are highly distributed, heterogeneous, and semantically inconsistent. Exis…

stat.AP2026

Nationwide Hourly Population Estimating at the Neighborhood Scale in the United States Using Stable-Attendance Anchor Calibration

Huan Ning, Zhenlong Li, Manzhu Yu +3

Traditional population datasets are largely static and therefore unable to capture the strong temporal dynamics of human presence driven by daily mobility. Recent smartphone-based…

stat.ME2026

M-SGWR: Multiscale Similarity and Geographically Weighted Regression

M. Naser Lessani, Zhenlong Li, Manzhu Yu +2

The first law of geography is a cornerstone of spatial analysis, emphasizing that nearby and related locations tend to be more similar, however, defining what constitutes "near" an…

cs.SI2025

Estimating Hourly Neighborhood Population Using Mobile Phone Data in the United States

Huan Ning, Zhenlong Li, Manzhu Yu +2

Traditional population estimation techniques often fail to capture the dynamic fluctuations inherent in urban and rural population movements. Recognizing the need for a high spatio…