2 citations · 7 across the 18 of their papers we have counts for
7 papers · 1 filter
Dual-Margin Embedding for Fine-Grained Long-Tailed Plant Taxonomy
Cheng Yaw Low, Heejoon Koo, Jaewoo Park +1
Taxonomic classification of ecological families, genera, and species underpins biodiversity monitoring and conservation. Existing computer vision methods typically address fine-gra…
Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts
Sumin Lee, Sungwon Park, Jeasurk Yang +2
Satellite-based slum segmentation holds significant promise in generating global estimates of urban poverty. However, the morphological heterogeneity of informal settlements presen…
Semi-supervised Shelter Mapping for WASH Accessibility Assessment in Rohingya Refugee Camps
Kyeongjin Ahn, YongHun Suh, Sungwon Han +3
Lack of access to Water, Sanitation, and Hygiene (WASH) services is a major public health concern in refugee camps, where extreme crowding accelerates the spread of communicable di…
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…
Classifying and Tracking International Aid Contribution Towards SDGs
Sungwon Park, Dongjoon Lee, Kyeongjin Ahn +4
International aid is a critical mechanism for promoting economic growth and well-being in developing nations, supporting progress toward the Sustainable Development Goals (SDGs). H…
Retrieval Augmented Time Series Forecasting
Sungwon Han, Seungeon Lee, Meeyoung Cha +2
Time series forecasting uses historical data to predict future trends, leveraging the relationships between past observations and available features. In this paper, we propose RAFT…