Leveraging Large Language Models for Automated Assessment and Mapping in Participatory Urban Planning

From Natural Language to Structured Queries An LLM-Powered Chatbot for a Smart Tourism Observatory

UrbanAgent: A Tool-Augmented Agent for Cross-System Urban Tasks

Modern cities rely on an increasing number of digital services to operate, but residents'daily needs are still difficult to meet. Services are fragmented and have little interoperability, placing a heavy operational burden on users. Existing digital platforms, urban foundation models, and intelligent assistants each address only isolated aspects of an urban task. But they struggle to reliably convert complex natural-language requests into executable cross-system workflows. We propose Urban-Agent, a tool-augmented agent framework for cross-system urban tasks. It couples the cognitive and reasoning capabilities of a large language model with a tool-set supporting code execution, API calls, and Model Context Protocol. Through one adaptive closed loop, it clarifies missing information before acting, grounds tool use in live observations, and aligns the final response with observed evidence and task constraints. To address the evaluation gap, we introduce Urban-Eval, a benchmark specifically designed for cross-system urban request. Unlike prior benchmarks that assess either general tool use or urban knowledge and reasoning, Urban-Eval evaluates both task results and execution quality, including required tool coverage, dependency validity, and evidence traceability. Experimental results indicate that Urban-Agent reaches a 71% task success rate, 10 points above the strongest baseline. This lead holds across GPT-5-mini, Gemini-2.5-flash, DeepSeek-V4-flash, and Qwen3-235B-A22B.

RAFE-XAI: A Retrieval-Augmented Feature Engineering and Explainable NLP Framework for Urban Infrastructure Risk Classification

This study introduces RAFE-XAI, a retrieval-augmented feature engineering and explainable natural language processing framework for urban infrastructure risk classification that incorporates semantic sentence embeddings, retrieval-based evidence, neighborhood-derived label distributions, domain-specific risk indicators, infrastructure asset cues, location indicators, and evidence-based explainability.

LifePlanner: Evaluating LLM Agents for Geo-spatial Planning with Social Media Data

Results show that failures mainly stem from incomplete evidence acquisition from such a large multimodal database, imprecise tool use and weak constraint integration rather than model size or reasoning length, suggesting that future progress requires effective grounded planning instead of scaling alone.

Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling

Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring. Existing methods typically formulate this task as multimodal representation learning, fusing heterogeneous urban data—e.g., satellite imagery, points of interest, textual descriptions, and 3D building information—into latent embeddings for prediction. However, these approaches are largely correlation-driven, assume cross-modal consistency, and rely on static pipelines, which limit their robustness in heterogeneous or unseen urban regions. We propose UrbanAgent, an agentic framework that reframes urban region profiling as a reasoning-driven inference problem. UrbanAgent instantiates an independent agent for each data modality and performs structured multi-agent collaborative reasoning to explicitly address crossmodal inconsistencies rather than absorbing them into a single representation. In addition, UrbanAgent extends indicator prediction as a closed-loop process of active evidence acquisition and iterative reasoning, enabling agents to verify uncertain inferences through tool-augmented retrieval of external knowledge optimized via reinforcement learning. Extensive experiments on global urban datasets for Carbon emissions, GDP, and Population estimation show that UrbanAgent consistently outperforms existing baselines, achieving an average improvement of 8.1% in R2, and exhibiting strong generalization performance in unseen-city settings.

Leveraging large language models (LLMs) for GeoAI-enabled digital agro-advisory

Digital agriculture has undergone a profound transformation driven by rapid advances in Earth observation, unmanned aerial vehicles and advanced data processing techniques. Despite this progress, climate-smart agriculture management faces a critical challenge: a farmer-centric agro-advisory system. Farmers and extension workers have limited access to GeoAI-based agro-advisory services, primarily due to limited data access and insufficient technical support. This study synthesises the potential of Large Language Models to address this challenge by translating complex GeoAI data into actionable and human-centric advice. The review examines gaps, recent advancements, and the model architectures required to adapt general-purpose LLMs for remote-sensing-based crop monitoring and management. We distinguish between experimentally validated capabilities of LLMs and the broader prospective applications proposed for agricultural remote sensing, noting that many current advances are primarily driven by multimodal foundation models, computer vision, and GeoAI systems rather than standalone LLMs. The study highlights the importance of advanced Retrieval-Augmented Generation and Supervised Fine-Tuning in agronomic science and mitigating the risk of misinterpretation. Further, we examine the emerging capabilities of multimodal approaches, which can seamlessly integrate visualisation and textual reasoning to support stakeholders in assessing crop health conditions and biophysical anomalies. The representative case studies, spanning multiple geographies and including voice-based advisory systems, demonstrate the shift from static advisory tools to dynamic, interactive recommendation systems. We highlight current challenges, such as the need for region-specific fine-tuning, data governance, and operation in low-connectivity environments, and advocate for a “human-in-the-loop” approach to decision-making. Through this, the LLMs will function as co-pilots assisting multiple stakeholders rather than as autonomous decision-makers vulnerable to biased output or hallucination problems. The review concludes by outlining a forward-looking research scope and closed-loop systems that iteratively learn from field outcomes.