CrowdCommand is an AI-powered real-time decision system designed to manage large-scale crowd movement in high-density environments. It predicts congestion, detects high-risk zones, and recommends corrective actions to make spaces safer, more efficient, and environmentally conscious.
This work investigates the use of large language models (LLMs) for smart city tasks, leveraging remote sensing imagery to characterize the built environment across multiple spatial scales. The findings highlight the potential of integrating remote sensing with LLMs to assist smart cities and decision-making.
Local AI applies artificial intelligence techniques in specific geographic areas like cities or neighborhoods to develop customized solutions for community needs. By leveraging local data, AI algorithms analyze patterns and provide insights for decision-making, ultimately improving residents' quality of life.