geocoding and validation

This paper identifies and systematically analyzes common challenges in spatial data science across mainstream programming languages—R, Python, and Julia—including inconsistent spherical geometry modeling, ambiguous spatial/temporal semantics, conflation of intensive and extensive attributes, poor interoperability between data cube and vector formats, complex cross-package dependencies, and a persistent divide between GIS and physical modeling communities. Through multi-language ecosystem surveys, cross-community comparative analysis, and software engineering abstraction, we propose, for the first time, a cross-language semantic framework for spatial operations. The framework formally defines support types (point vs. block), specifies attribute-type constraints on operation validity, and refactors spherical Simple Features logic. We distill five foundational insights that establish a methodological basis and practical guidance for tool interoperability, pedagogical alignment, and open-source governance in spatial computing.

This paper addresses the geocoding challenge posed by compositional location references (e.g., “the Starbucks northwest of Zhongguancun Metro Station”). We propose a novel two-stage LLM-based approach: first decoupling spatial knowledge from logical reasoning capabilities, then jointly optimizing both via prompt engineering and supervised fine-tuning. Our key innovation is a knowledge-reasoning separation architecture, enabling lightweight fine-tuned models (e.g., 7B-parameter variants) to achieve accuracy on par with billion-parameter general-purpose LLMs on compositional referencing tasks. Experiments across multiple real-world datasets demonstrate an average 12.6% improvement in geocoding accuracy over baselines—including rule-based systems and end-to-end fine-tuned models—validating the efficacy and feasibility of domain-specialized small models for geographic semantic parsing.

Current large language models often generate GIS code that violates spatial rules—such as geographic semantics, topological relationships, coordinate reference systems (CRS), and units—leading to unreliable outputs. This work proposes GeoContra, a novel framework that formalizes geographic constraints into executable geographic contracts and integrates static checking, runtime verification, and semantic validation to establish a geography-aware, closed-loop repair mechanism. By embedding natural language understanding, CRS metadata, spatial predicates, and topological rules directly into the LLM generation pipeline, GeoContra significantly enhances spatial correctness across 7,079 real-world tasks: achieving up to 81.5% accuracy with proprietary models and yielding an average improvement of 26.6% across eleven open-source models.

Contemporary digital gazetteers face critical challenges—including heterogeneous data sources, absence of standardized encoding schemes, weak multidimensional semantic representation, and inadequate support for dynamic evolution—thereby limiting location retrieval capabilities grounded in physical, social, and cultural attributes. To address these, this study systematically reviews the state of gazetteer database technologies and proposes an integrated framework unifying GIS, VGI quality control, textual toponym recognition, multi-source data fusion, and toponym matching algorithms. It innovatively introduces a unified modeling approach for multidimensional toponymic features—spatial, functional, cultural, and temporal—to enhance toponym disambiguation, identity resolution, and dynamic evolutionary representation. The work provides theoretical foundations and technical pathways for overcoming standardization bottlenecks, enriching semantic expressivity, and enabling evolution-aware reasoning. Collectively, it establishes a systematic basis for next-generation intelligent gazetteer knowledge bases.

Unstructured, heterogeneous, and inconsistently spelled location descriptions in disaster databases (e.g., EM-DAT) impede subnational geocoding. Method: We propose the first fully automated, GPT-4o–driven geocoding workflow: large language models perform text cleaning and semantic parsing; cross-validated geographic matching integrates GADM, OpenStreetMap, and Wikidata to generate subnational coordinates with reliability scores. Contribution/Results: The method enables flexible, multi-hazard, cross-administrative mapping and introduces the first LLM-powered, multi-source trustworthy geolocation framework. Applied to EM-DAT records from 2000–2024, it successfully geocoded 14,215 disaster events and 17,948 unique locations at subnational resolution, achieving high precision. This significantly enhances spatial comparability, interoperability, and analytical utility of disaster data.

This work proposes the first end-to-end geocoding framework based on large language models, reframing coordinate prediction as a text generation task. In contrast to traditional multi-stage approaches—which suffer from complex pipelines, error propagation, and heavy reliance on structured geographic knowledge bases—the proposed method eliminates the need for external databases by integrating chain-of-thought reasoning to enhance spatial relationship inference. Furthermore, it employs a reinforcement learning mechanism with a distance-bias-aware reward function to refine coordinate prediction accuracy. The framework not only achieves high precision in mapping explicit addresses to point coordinates but also effectively interprets ambiguous relative location descriptions and demonstrates strong generalization capabilities for non-point, region-based queries.

Existing evaluation methods struggle to effectively assess the dynamic execution capabilities of large language models (LLMs) in complex, multi-step geospatial analysis tasks and lack support for runtime feedback and the multimodal nature of spatial outputs. To address this, this work introduces a dynamic, interactive benchmark tailored for tool-augmented GIS agents, encompassing 117 atomic GIS tools and 53 representative tasks. It proposes the Parameter Execution Accuracy (PEA) metric, a “Last-Try Alignment” strategy, and a vision-language model–based mechanism for validating spatial outputs. Furthermore, the study designs a Plan-and-React agent architecture that decouples global planning from local reactive execution. Experimental results demonstrate that this architecture significantly outperforms baseline approaches across seven mainstream LLMs, achieving both logical rigor in multi-step reasoning and robustness in error recovery.

This work addresses the challenges of fragile entity and event extraction from unstructured data, heavy reliance on costly ontology engineering in knowledge graph construction, and limited cross-domain generalization. To overcome these limitations, the authors propose an end-to-end multidimensional information extraction framework that leverages spatiotemporal context as a universal anchor. The approach employs large language models (e.g., GPT-4o-mini, Qwen3-8B) for context-aware entity and event extraction, enhanced by document-level memory, geocoding correction, and quality validation mechanisms. It further supports user-defined analytical dimensions and interactive exploration, including clustering, burst detection, and entity network analysis. Evaluated on a public health benchmark, the method achieves F1 score improvements of 4.37% and 3.60% for spatial and temporal entity extraction, respectively. The code and an online demo platform are publicly released.

This study addresses the prevalence of topological errors in building footprints generated by deep learning models, which hinder their direct integration into GIS databases. To tackle this issue, the authors propose a multi-domain GeoAI quality control framework that fuses 24-dimensional features encompassing geometric, spatial contextual, and spectral-textural attributes. By integrating geometric regularization and spatial mutual exclusion constraints, the framework enables object-level automated quality inspection and purification. Initial masks are produced using U-Net (ResNet-34) and SAM-LoRA (ViT-B), followed by boundary deformation and duplicate object detection via decision tree classifiers. Experimental results demonstrate that the framework achieves 95.31% accuracy, 91.06% F1-score, and 0.880 Matthews correlation coefficient on an independent test area, with an 87.34% error footprint detection rate. This approach significantly enhances building database purity to 95.38% and reduces relative error by 83.09%, offering a robust and transferable quality assurance mechanism for automated GIS production.