Building a Pipeline for Dynamic Economic Activity Indicator Extraction and Automated Investment Opportunity Discovery Using Satellite/Street View Images and LLMs

This article proposes an innovative pipeline construction method that combines satellite and street view image data with Large Language Models (LLMs) to extract near real-time economic activity indicators and discover automated investment opportunities based on them. It's a game-changing solution that no longer relies on past data but directly interprets changes in the physical world to read market trends.

1. The Challenge / Context

Traditional economic indicators suffer from critical time lags between their release and the actual data collection, which acts as a key disadvantage in investment decisions. Core economic activities such as stock prices, real estate, and consumption patterns fluctuate in real-time, yet we have always relied on lagging indicators. Furthermore, micro-level changes that are difficult to capture with structured data alone, such as the degree of commercial district revitalization in a specific area, changes in factory operating rates, or the status of new building construction, were almost impossible to analyze with traditional methods. This information gap ultimately led to information asymmetry, remaining an area of advanced analytical techniques accessible only to a few. However, the development of Large Language Models (LLMs) and high-resolution satellite/street view image data is overcoming these limitations, laying the technical foundation for anyone to gain faster and deeper insights. Now is the time to introduce a new paradigm that reads changes in the physical world to predict the future of the economy.