3 citations · 6 across the 5 of their papers we have counts for
6 papers
Learning Multidimensional Urban Poverty Representation with Satellite Imagery
Sungwon Park, Sumin Lee, Jihee Kim +4
Recent advances in deep learning have enabled the inference of urban socioeconomic characteristics from satellite imagery. However, models relying solely on urbanization traits oft…
GeoReg: Weight-Constrained Few-Shot Regression for Socio-Economic Estimation using LLM
Kyeongjin Ahn, Sungwon Han, Seungeon Lee +6
Socio-economic indicators like regional GDP, population, and education levels, are crucial to shaping policy decisions and fostering sustainable development. This research introduc…
GeoSEE: Regional Socio-Economic Estimation With a Large Language Model
Sungwon Han, Donghyun Ahn, Seungeon Lee +5
Moving beyond traditional surveys, combining heterogeneous data sources with AI-driven inference models brings new opportunities to measure socio-economic conditions, such as pover…
Fine-Grained Socioeconomic Prediction from Satellite Images with Distributional Adjustment
Donghyun Ahn, Minhyuk Song, Seungeon Lee +5
While measuring socioeconomic indicators is critical for local governments to make informed policy decisions, such measurements are often unavailable at fine-grained levels like mu…
Learning Economic Indicators by Aggregating Multi-Level Geospatial Information
Sungwon Park, Sungwon Han, Donghyun Ahn +8
High-resolution daytime satellite imagery has become a promising source to study economic activities. These images display detailed terrain over large areas and allow zooming into…
Lightweight and Robust Representation of Economic Scales from Satellite Imagery
Sungwon Han, Donghyun Ahn, Hyunji Cha +3
Satellite imagery has long been an attractive data source that provides a wealth of information on human-inhabited areas. While super resolution satellite images are rapidly becomi…