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Abstract This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts. 

IntroductionAI fairness seeks to improve the transparency and explainability of AI systems by ensuring that their outcomes genuinely reflect the best interests of users. Data augmentation, which involves generating synthetic data from existing datasets, has gained significant attention as a solution to data scarcity. In particular, diffusion models have become a powerful technique for generating synthetic data, especially in fields like computer vision. MethodsThis paper explores the potential of diffusion models to generate synthetic tabular data to improve AI fairness. The Tabular Denoising Diffusion Probabilistic Model (Tab-DDPM), a diffusion model adaptable to any tabular dataset and capable of handling various feature types, was utilized with different amounts of generated data for data augmentation. Additionally, reweighting samples from AIF360 was employed to further enhance AI fairness. Five traditional machine learning models—Decision Tree (DT), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF)—were used to validate the proposed approach. Results and discussionExperimental results demonstrate that the synthetic data generated by Tab-DDPM improves fairness in binary classification. 

Connected vehicle (CV) data are increasingly available and widely used in transportation engineering for traffic monitoring, safety analysis, and infrastructure planning. However, the representativeness of CV data in general urban mobility analysis remains underexplored, raising concerns about potential biases between observed mobility patterns in CV data and actual travel behaviors, particularly across different demographic and socioeconomic groups. This study compares Wejo connected vehicle (CV) data with SafeGraph mobile phone records covering boarder population to reveal the representativeness of connected vehicle data in the context of urban mobility analysis. Using entropy-based measures of destination income diversity and normalized inter-neighborhood interaction strength, we examine how each dataset reflects mobility structures across income groups in San Antonio, Texas. Results show that SafeGraph data capture more multimodal and socially integrative travel behaviors, particularly among low-income communities, while Wejo data primarily reflect routine, vehicle-based movements concentrated among higher-income users. Interaction patterns in the CV data are more spatially clustered, with stronger flows observed within affluent neighborhoods. These differences underscore the behavioral and demographic selectivity embedded in CV data and point to important limitations when using such data to analyze mobility-based segregation. The findings contribute to ongoing efforts to evaluate the representativeness of emerging mobility datasets and their implications for urban spatial analysis.