Multimodal large language models (MLLMs) have demonstrated promising potential in addressing real-world vision-language tasks. However, existing MLLMs exhibit relatively limited capabilities in understanding street view images, struggling to achieve multi-level semantic understanding and spatial reasoning for region-level fine-grained inference in street view images, which to some extent limits their potential in geospatial artificial intelligence (GeoAI). This study constructs an instruction fine-tuning dataset to guide MLLMs in learning spatial knowledge and scene features from street-view images, called SVGPT-Instruct. This large-scale dataset for street view understanding encompasses 430K instruction samples and 110K street view images. Ultimately, we propose the StreetViewGPT, an MLLM-based method for street view image scene perception and reasoning. The StreetViewGPT is capable of uniformly handling various street view image tasks, including time estimation (TE), weather recognition (WR), scene classification (SC), spatial distance estimation (SDE), and scene graph generation (SGG). The experimental results show that in the TE, WR, and SC tasks, the accuracy of StreetViewGPT improved by 1.33%, 21.68%, and 18.83% respectively compared to the suboptimal model. In the SDE task, the accuracy of predictions falling within the three error tolerance intervals of [75%, 125%], [50%, 150%], and [25%, 175%] reached 88.3%, 97.7%, and 99.5%, respectively, which improved by 78.2%, 78.7%, and 58.6%. In the SGG task, Recall and Mean Recall reached 16.09% and 6.84%, respectively, which improved by 12.49% and 5.4% compared to Qwen2-VL. It demonstrates that StreetViewGPT possesses multi-level semantic understanding and spatial reasoning capabilities, ranging from global semantic perception at the image level to fine-grained reasoning at the region level. The proposed method enhances the recognition and cognition capabilities for spatial scenes in street view images, offering positive implications for smart city perception and autonomous driving navigation research.

Participatory systems have been widely adopted in citizen science, environmental monitoring, urban governance, and public collaborative decision-making. Traditional usability theory focuses on individual task performance and user satisfaction, which cannot adequately explain or support voluntary collective participation, participant recruitment, and long-term engagement. To address this gap, this study introduces the new concept participatability and develops a dedicated assessment framework for participatory systems. Based on a systematic review of usability criteria and the unique socio-technical features of participatory systems, this study defines five core evaluation dimensions: salience, adaptability, congruence, privacy safeguarding, and interactive engagement. Two complementary case studies, including a mature citizen science platform and a newly developed campus participatory planning system, are conducted to validate the framework. Empirical results show that participatability is significantly associated with user acceptance, participation willingness, data contribution quality, and long-term system sustainability. Users in collective participation scenarios prioritize participatability over conventional usability. This study provides a theoretically sound and practically applicable framework for understanding, evaluating, and designing participatory systems. The proposed concept and criteria address critical limitations of existing theories and offer practical guidance for system developers and practitioners to improve participation effectiveness.