Papers with urban planning
CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation (2025.emnlp-industry)
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| Challenge: | Existing models rely on rigid, hand-crafted rules to model nuanced behavior in urban environments. |
| Approach: | They propose an urban simulator that generates realistic daily schedules using a recursive value-driven approach that balances mandatory activities, personal habits, and situational factors. |
| Outcome: | The proposed urban simulator exhibits closer alignment with real humans than previous work. |
GeoIndia: A Seq2Seq Geocoding Approach for Indian Addresses (2024.emnlp-industry)
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| Challenge: | a new geocoding system for Indian addresses addresses is needed for logistics, urban planning and location-based services. |
| Approach: | They propose a geocoding system for Indian addresses using hierarchical H3-cell prediction using a Seq2Seq framework. |
| Outcome: | The proposed system outperforms existing geocoding platforms in accuracy and reliability across multiple Indian states. |
PlanGPT: Enhancing Urban Planning with a Tailored Agent Framework (2025.acl-industry)
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| Challenge: | Empirical tests demonstrate that PlanGPT framework has achieved advanced performance, providing comprehensive support that significantly enhances professional planning efficiency. |
| Approach: | They propose a specialized AI agent framework tailored for urban and spatial planning that integrates a customized local database retrieval system and domain-specific knowledge activation capabilities. |
| Outcome: | Empirical tests show that PlanGPT framework significantly improves planning efficiency . it integrates a customized database retrieval system, domain-specific knowledge activation capabilities, and advanced tool orchestration mechanisms. |
SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models (2026.acl-long)
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| Challenge: | Large language models (LLMs) are being used in urban planning but there is concern that they reproduce or amplify such biases. |
| Approach: | They propose a framework to evaluate spatial gender bias in large language models . they use a taxonomy of 62 urban micro-spaces, a prompt library and three diagnostic layers . |
| Outcome: | The proposed framework identifies structured gender-space associations that go beyond the public-private divide, forming nuanced micro-level mappings. |
PlanGPT-VL: Enhancing Urban Planning with Domain-Specific Vision-Language Models (2025.emnlp-industry)
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| Challenge: | Existing Vision-Language Models (VLMs) fail to analyze planning maps . specialized visual representations of land use zones, transportation networks, and development policies are needed to interpret complex planning maps. |
| Approach: | They propose a domain-specific VLM tailored for urban planning maps that employs three innovations: PlanAnno-V framework for high-quality VQA data synthesis, Critical Point Thinking (CPT) and PlanBench-V benchmark for systematic evaluation. |
| Outcome: | The new model outperforms general-purpose VLMs on planning map interpretation tasks. |
Similar Region Search using LLMs on Spatial Feature Space (2026.findings-eacl)
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| Challenge: | Existing similarity search methods fail to capture contextual richness of spatial data . existing methods fail in capturing regional characteristics, authors say . |
| Approach: | They propose a similar region search framework that ranks candidate regions based on their similarity to a query region using large language models. |
| Outcome: | The proposed similar region search framework outperforms state-of-the-art methods on real-world city datasets. |
CompassLLM: A Multi-Agent Approach toward Geo-Spatial Reasoning for Popular Path Query (2026.findings-acl)
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| Challenge: | Existing algorithms and machine learning methods require model training, parameter tuning, and retraining when accommodating data updates. |
| Approach: | They propose a multi-agent framework that leverages the reasoning capabilities of Large Language Models into the geo-spatial domain to solve the popular path query. |
| Outcome: | Experiments on real and synthetic datasets show that CompassLLM performs better than existing models while being cost-effective. |