Conference Agenda

WebGIS platforms have revolutionized geospatial data dissemination, yet their adoption remains constrained by the steep learning curve of mapping library APIs. Frontend libraries like Leaflet, OpenLayers, and platforms such as Tianditu contain hundreds of classes and methods, requiring substantial programming expertise. This technical barrier prevents domain experts—urban planners, environmental scientists, public health officials—from independently creating the visualizations they need for analysis and decision-making.While Large Language Models (LLMs) have revolutionized code generation, they struggle with domain-specific, low-resource APIs common in geospatial applications. When applied to specialized geospatial APIs, these models exhibit critical failures: they frequently "hallucinate" non-existent functions, misuse parameters, or generate syntactically plausible but semantically incorrect code. This unreliability stems from the underrepresentation of domain-specific libraries in LLMs' training corpora, creating a "last mile" problem that renders them unsuitable for professional geospatial development. This study proposes a ReAct-based agent framework for automated web map generation from natural language instructions. The framework constructs a stateful, cyclic workflow and enables human–AI interactive WebGIS code generation based on the Tianditu JavaScript API. Its effectiveness and generality are validated through multi-model evaluation (GPT-4, Claude 3, Llama 3, Qwen-Max), demonstrating robust performance across diverse application scenarios. Experimental results show that the framework achieves professional-grade quality in both directive-driven and data-driven geospatial visualization tasks.

With the proliferation of online geospatial services, Geospatial Application Programming Interfaces (GeoAPIs) have become the backbone of modern spatial data interoperability. However, the high technical barriers of GeoAPIs, characterized by complex RESTful syntax and deterministic parameter requirements, create a significant "digital divide" for non-expert users. To bridge the gap between intuitive human spatial intent and technical service execution, this study proposes Text2GeoAPI, a novel conceptual framework for the automatic invocation and composition of geospatial services via natural language. We introduce the Intent-Entity-Operation (IEO) model to formalize spatial tasks, decoupling high-level semantic goals from atomic technical operations. We developed a modular prototype leveraging Large Language Models (LLMs) as cognitive engines to perform structured intent parsing, dynamic workflow planning, and multi-source result synthesis. Experimental evaluations using 100 diverse spatial queries demonstrate an overall task success rate of 86%, with the system effectively orchestrating multi-hop service chains (e.g., Geocoding → Isochrone Analysis → POI Search). The results confirm that Text2GeoAPI significantly lowers the threshold for accessing professional geospatial analysis, shifting the GIS paradigm from "tool-centric" to "intent-centric" intelligence.

Winter accessibility poses critical challenges in cold-climate cities such as Québec, where snow and ice accumulation restrict the mobility of people with disabilities. This study presents an AI-driven multimodal framework designed to detect, classify, and

map winter barriers affecting pedestrian accessibility in Québec City. Building upon the SNOWMAN project, synthetic image and textual datasets were developed to represent seven major snow- and ice-related obstacle categories, including icy ruts, deep

snow, and uncleared sidewalks. The visual modality employed a self-supervised SimCLR model for snow-barrier classification (F1-score = 0.93), while the textual modality used a fine-tuned BERT classifier, achieving a perfect F1-score = 1.00 on validated

synthetic descriptions. Canonical Correlation Analysis (CCA) aligned the two modalities into a shared latent space, enabling spatial fusion of visual and semantic embeddings for integrated analysis within the MobiliSIG Winter Mobility platform. The fused data

produced dynamic accessibility maps revealing clusters of recurring winter hazards in known high-risk zones. The results confirm the feasibility of using synthetic multimodal data to simulate pedestrian-scale winter conditions and demonstrate the potential of

multimodal AI for inclusive, data-driven mobility management in cold-climate cities.

As China’s urban development shifts toward stock-based optimisation, identifying inefficient residential land has become important for urban regeneration. Existing approaches often rely on subjective weighting, linear analytical structures, or homogeneous treatment of different residential types, which weakens robustness and transferability. To address these limitations, this study proposes a data-driven framework that integrates mobile-phone signaling and other multi-source spatiotemporal big data in Shenzhen. Two dominant residential forms—formal residential communities and urban villages—are evaluated separately through a four-dimensional framework covering built form, activity vitality, economic efficiency, and environmental livability. Principal component analysis is used to estimate intrinsic dimensionality and initialize a parametric autoencoder. A spatially constrained Gaussian mixture model is then employed to identify inefficient residential clusters while preserving local coherence. The clustering results are interpreted using a random forest model and TreeSHAP, and externally validated by street-view imagery interpretation and limited field surveys. PCA retained five components for urban villages and six for formal residential communities, and the BIC selected six and five clusters for the two residential types, respectively. The results indicate that inefficient formal residential communities show scattered and island-like spatial patterns, whereas inefficient urban villages tend to form more continuous clusters along the edges of larger village agglomerations. Random forest and TreeSHAP further reveal that inefficient urban villages are more strongly associated with deficiencies in service accessibility and local socioeconomic conditions, whereas inefficient formal residential communities are more closely associated with lower residential vitality and relatively high development intensity. External validation indicates acceptable agreement with observed residential conditions.

This paper investigates the long-term consistency and reliability of paid geospatial crowdsourcing on the online platform Microworkers.com. Over a five-month period, we conducted three crowdsourcing campaigns, each representing a task typical for remote sensing, i.e., pixel classification, point selection, and geometric outline acquisition, to assess whether repeated worker participation enhances data quality and reproducibility. Beyond individual task performance, we examine the broader question of whether crowdsourcing campaigns can yield reproducible results over extended periods. Despite the large and heterogeneous workforce of Microworkers.com, a substantial share of tasks was completed by recurring workers who consistently outperformed one-time participants. Furthermore, across all campaigns, data quality remained largely stable, with only minor variability between epochs. Additionally performed statistical analyses confirm that reproducible outcomes are achievable, highlighting the potential of reliable and reproducible crowdsourcing results for geospatial data acquisition.

This abstract presents a comprehensive research framework examining the interplay between colonial trade networks and the spatial form of port cities during the early modern era. Firstly, the study constructs a geographic database of nearly 300 colonial port cities, using intercity trade data from East India Company archives as network edges to analyze their structural and morphological evolution. Secondly, it processes historical maps of colonial ports through a fine-tuned multimodal large language model to extract and classify spatial morphological features, establishing a systematic typology of urban form patterns. Thirdly, the research develops regression models to reveal correlations between network status and morphological patterns. Preliminary findings highlight Batavia's dominant yet volatile role within the network and reveal a trend toward decentralization over the 18th century. The research contributes to both urban historical studies and digital humanities by offering a scalable, comparative approach to interpreting colonial port cities as spatial manifestations of global economic and political forces, while establishing empirical relationships between network status and urban form characteristics. It further provides a refined framework for contextualizing their cultural heritage significance within trans-colonial networks.

This study explores the application of Graph Convolutional Networks (GCNs), specifically the GraphSAGE model, to the cartographic generalization of hydrographic networks in the state of Santa Catarina, Brazil. The generalization of river segments is critical for transitioning from detailed (1:25,000) to generalized (1:100,000) scales. It's traditionally a manual, rule-based process. By modeling drainage systems as graphs and training deep learning models with data from the Brazilian Army's Geospatial Database (BDGEx), this research evaluates how geometric and semantic attributes influence generalization outcomes. This data follows Brazilian Technical Specifications of the Geospatial Vector Data Structure (ET-EDGV), therefore they figure as a systematic data from Brazilian institutions.

GraphSAGE model was trained four times, each incorporating varying combinations of attributes such as segment length, sinuosity, polygon containment, and river flow regime. The model trained with all attributes achieved the highest accuracy (99.98%). Even models using geometric features surpassed 93% accuracy. These results highlight the effectiveness of GCNs in capturing structural patterns.

This study compares GraphSAGE model outputs to those generated by the GeoData Loader for Mapserver (GDLMS), the current operational system for generalization, developed and used by the Geographic Service of the Brazilian Army. It also compares those generalization to reference data acquired by manual generalization using the same 1:25.000 scale input. Visual analysis in GIS environments reveals that GCNs can be an alternative for generalization tasks.

This research demonstrates the viability of using GeoAI methods for automating complex cartographic processes, offering a scalable and data-driven solution aligned with national geospatial data standards.