CrossVIT-augmented Geospatial-Intelligence Visualization System for Tracking Economic Development Dynamics

TL;DR

Senseconomic is a scalable, deep learning-based geospatial visualization system that integrates multimodal imagery to accurately track economic development dynamics and assist policymakers.

Contribution

The paper introduces Senseconomic, a novel system combining multimodal sensing, deep learning, and distributed computing for real-time economic data analysis.

Findings

Achieved R-squared of 0.8363 in county-level economic prediction

Halved processing time to 23 minutes with distributed computing

Integrated remote sensing and street view images using cross-attention

Abstract

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.