GeoReg Estimates Socio-Economic Indicators Using Satellite Data and Large Language Models

Estimating crucial socio-economic indicators such as regional GDP and education levels often proves challenging, particularly in data-scarce regions of the developing world, yet these figures are vital for effective policy decisions and sustainable development. Kyeongjin Ahn, Sungwon Han, and Seungeon Lee, along with colleagues from institutions including the Korea Advanced Institute of Science and Technology and the Max Planck Institute, present GeoReg, a novel regression method that tackles this problem by intelligently combining diverse data sources like satellite imagery and web-based geospatial information. The team leverages the power of large language models (LLMs) to overcome the lack of labelled data, effectively using the LLM to identify and categorise relationships between data features and target indicators, and then applying tailored weight constraints to improve estimation accuracy. Through experiments across three countries at varying stages of development, the researchers demonstrate that GeoReg consistently outperforms existing methods, offering a significant advancement in the ability to estimate socio-economic indicators even where data is limited.

Satellite Data Estimates for Local Development

Estimating socio-economic indicators like regional GDP, population levels, and educational attainment is crucial for effective policy-making and tracking sustainable development, yet remains a significant challenge, particularly in developing countries. Accurate data collection demands substantial resources and consistent administrative systems, often lacking in regions where detailed, localized information is most needed. This scarcity hinders efforts to understand inequalities, identify vulnerabilities, and implement targeted interventions to improve living standards. Consequently, researchers are increasingly interested in leveraging alternative data sources and innovative analytical techniques to overcome these limitations.

Recent advancements explore the use of satellite imagery and web-based geospatial data as viable alternatives to traditional data collection methods. These sources offer extensive geographical coverage and the potential for frequent updates, providing valuable insights into regional characteristics and dynamics. However, relying solely on these data sources presents challenges, as many AI-driven approaches require large volumes of labeled data for training, a resource often unavailable in data-scarce regions. To address these limitations, researchers have introduced GeoReg, a new approach that utilizes the power of large language models (LLMs) to function as a ‘data engineer’.

GeoReg extracts meaningful information from diverse data sources, even when labeled data is limited, by leveraging the LLM’s pre-existing knowledge. The system identifies relevant data features and their relationships to target indicators, categorizing these correlations to guide the estimation process. By incorporating these relationships into a linear regression model, GeoReg ensures that the LLM’s knowledge acts as a guiding principle, reducing errors and improving accuracy. This innovative approach offers several key advantages, including scalability and interpretability. The LLM’s pre-trained knowledge allows it to readily incorporate new data sources and predict a wide range of socio-economic indicators. The use of a linear model provides a clear explanation of each data feature’s contribution, fostering trust and facilitating communication with researchers and policymakers. Experiments conducted across three countries, South Korea, Vietnam, and Cambodia, demonstrate that GeoReg outperforms existing methods, achieving an average success rate of 87.2% and offering a promising pathway to alleviate social issues, particularly in low-income countries.

GeoReg, Method

GeoReg is a novel methodology designed to estimate crucial socio-economic indicators, such as regional GDP, population levels, and educational attainment, even in regions where data is scarce. Unlike traditional approaches that rely on large volumes of labelled data, GeoReg leverages the power of large language models (LLMs) to function as a ‘data engineer’, extracting meaningful information from diverse sources. This innovative approach is particularly valuable for developing countries where comprehensive data collection is often challenging. Central to GeoReg is a two-stage process that begins with defining ‘modules’ to transform raw data, including satellite imagery and geospatial attributes, into interpretable features.

The LLM then intelligently selects the most relevant modules for predicting a specific indicator, uncovering the relationships between these features and the target variable. This process moves beyond simple data analysis by categorising correlations as positive, negative, mixed, or irrelevant, providing a nuanced understanding of the underlying factors. The second stage employs a linear regression model trained on these selected modules, but with a crucial difference: the model’s weights are constrained by the LLM’s identified correlations. This ensures the LLM’s pre-existing knowledge acts as a guiding principle during training, reducing the risk of overfitting and improving the model’s generalizability.

Furthermore, the system actively identifies meaningful interactions between features, integrating them alongside nonlinear transformations to capture complex patterns. This methodology offers significant advantages over existing techniques, notably its scalability and interpretability. The LLM’s pre-trained knowledge allows it to readily incorporate new data sources, expanding the range of indicators that can be estimated. Crucially, the linear model provides a clear explanation of each module’s contribution, fostering trust in the findings and facilitating communication with researchers and policymakers. Experiments conducted across South Korea, Vietnam, and Cambodia demonstrate that GeoReg consistently outperforms established methods in estimating socio-economic indicators.

Language Models Improve Regional Indicator Estimation

GeoReg represents a significant advance in estimating crucial socio-economic indicators, particularly in regions where data is scarce. This research introduces an approach that combines the power of large language models with traditional regression techniques to accurately predict indicators like regional GDP, population levels, and educational attainment, even with limited ground truth data. The system effectively leverages pre-existing knowledge embedded within the language model to intelligently extract relevant features from diverse data sources, including satellite imagery and geospatial information. A key innovation lies in how GeoReg categorizes these extracted features, assessing their relationship to the target indicator as positively, negatively, or not correlated.

The language model understands how those features relate to the indicator being predicted, allowing for a more nuanced and accurate model. This categorization process guides the subsequent regression analysis, applying appropriate weight constraints to ensure the model reflects these understood relationships. Furthermore, the system identifies meaningful interactions between features, and incorporates non-linear transformations, allowing it to capture complex patterns that simpler models would miss. In testing across three countries at varying stages of development, GeoReg consistently outperformed existing methods in estimating socio-economic indicators.

This improvement is particularly pronounced in low-income countries where reliable data is often limited, demonstrating the system’s potential to support more informed policy decisions and sustainable development initiatives. The ability to achieve high accuracy with minimal training data represents a substantial leap forward, offering a practical solution for regions historically underserved by data-driven analysis. By intelligently combining the strengths of language models and regression, GeoReg provides a robust and adaptable framework for understanding and addressing socio-economic challenges globally.

Estimating Socioeconomic Indicators With Limited Data

This research introduces GeoReg, a regression model designed to estimate crucial socio-economic indicators in regions where data is limited. GeoReg integrates diverse data sources, including satellite imagery and web-based information, and leverages the prior knowledge of large language models to effectively estimate indicators even with scarce labeled data. The model functions by identifying relationships between data features and the target indicator, categorising these correlations, and then applying tailored weight constraints during the estimation process. Importantly, GeoReg also explores interactions between features, capturing complex patterns beyond simple attributes and improving estimation accuracy. Experiments across three countries at varying stages of development demonstrate that GeoReg outperforms existing methods, particularly in low-income countries with limited data availability. The authors acknowledge that the model’s performance is influenced by the quality and relevance of the input data, and further research could explore methods for automatically assessing and improving data quality.