用多模态图像和AI预测县区经济，速度快精度高。
CrossVIT-augmented Geospatial-Intelligence Visualization System for Tracking Economic Development Dynamics
- 融合遥感、街景与夜间灯光数据，通过跨注意力机制建模经济动态。
- 县区级经济预测R²达0.8363，分布式计算将处理时间减半至23分钟。
- 前端可视化友好，适合政策制定者与研究者快速获取经济态势。
及时准确的经济数据对有效决策至关重要。当前数据时效性与空间分辨率问题可通过多模态感知与分布式计算的进步解决。我们提出Senseconomic，一个基于Transformer框架的可扩展系统，通过多模态图像与深度学习追踪经济动态。该系统利用跨注意力机制融合遥感图像与街景图像，并以夜间灯光数据作为弱监督信号。在县级经济预测中，模型达到0.8363的R-squared值，结合分布式计算后处理时间缩短至23分钟。系统采用Vue3构建前端界面，集成百度地图实现可视化；后端基于Python自动化完成图像下载与预处理任务。Senseconomic为政策制定者与研究人员提供高效工具，支持资源分配与经济规划。
原文摘要 · Abstract (English)
Timely and accurate economic data is crucial for effective policymaking. Current challenges in data timeliness and spatial resolution can be addressed with advancements in multimodal sensing and distributed computing. We introduce Senseconomic, a scalable system for tracking economic dynamics via multimodal imagery and deep learning. Built on the Transformer framework, it integrates remote sensing and street view images using cross-attention, with nighttime light data as weak supervision. The system achieved an R-squared value of 0.8363 in county-level economic predictions and halved processing time to 23 minutes using distributed computing. Its user-friendly design includes a Vue3-based front end with Baidu maps for visualization and a Python-based back end automating tasks like image downloads and preprocessing. Senseconomic empowers policymakers and researchers with efficient tools for resource allocation and economic planning.
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