Author name cluster

Jihee Kim

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

AAAI Conference 2026 Conference Paper

Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts

Sumin Lee
Sungwon Park
Jeasurk Yang
Jihee Kim
Meeyoung Cha

Satellite-based slum segmentation holds significant promise in generating global estimates of urban poverty. However, the morphological heterogeneity of informal settlements presents a major challenge, hindering the ability of models trained on specific regions to generalize effectively to unseen locations. To address this, we introduce a large-scale high-resolution dataset and propose GRAM (Generalized Region-Aware Mixture-of-Experts), a two-phase test-time adaptation framework that enables robust slum segmentation without requiring labeled data from target regions. We compile a million-scale satellite imagery dataset from 12 cities across four continents for source training. Using this dataset, the model employs a Mixture-of-Experts architecture to capture region-specific slum characteristics while learning universal features through a shared backbone. During adaptation, prediction consistency across experts filters out unreliable pseudo-labels, allowing the model to generalize effectively to previously unseen regions. GRAM outperforms state-of-the-art baselines in low-resource settings such as African cities, offering a scalable and label-efficient solution for global slum mapping and data-driven urban planning.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization

Hoyoon Byun
Gyeongdeok Seo
Joonseong Kang
Taero Kim
Jihee Kim
Kyungwoo Song

In-context learning (ICL), a nonparametric learning method based on the knowledge of demonstration sets, has become a de facto standard for large language models (LLMs). The primary goal of ICL is to select valuable demonstration sets to enhance the performance of LLMs. Traditional ICL methods choose demonstration sets that share similar features with a given query. However, we have found that the performance of these traditional ICL approaches is limited on out-of-distribution (OOD) datasets, where the demonstration set and the query originate from different distributions. To ensure robust performance in OOD datasets, it is essential to learn causal representations that remain invariant between the source and target datasets. Inspired by causal representation learning, we propose causal-aware in-context learning (CCL). CCL captures the causal representations of a given dataset and selects demonstration sets that share similar causal features with the query. To achieve this, CCL employs a novel VAE-based causal representation learning technique. We demonstrate that CCL improves the OOD generalization performance of LLMs both theoretically and empirically. Code is available at: \url{https: //github. com/MLAI-Yonsei/causal-context-learning}

PDF Details

AAAI Conference 2025 Conference Paper

Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model

Kyeongjin Ahn
Sungwon Han
Sungwon Park
Jihee Kim
Sangyoon Park
Meeyoung Cha

The increasing frequency and intensity of natural disasters call for rapid and accurate damage assessment. In response, disaster benchmark datasets from high-resolution satellite imagery have been constructed to develop methods for detecting damaged areas. However, these methods face significant challenges when applied to previously unseen regions due to the limited geographical and disaster-type diversity in the existing datasets. We introduce DAVI (Disaster Assessment with VIsion foundation model), a novel approach that addresses domain disparities and detects structural damage at the building level without requiring ground-truth labels for target regions. DAVI combines task-specific knowledge from a model trained on source regions with task-agnostic knowledge from an image segmentation model to generate pseudo labels indicating potential damage in target regions. It then utilizes a two-stage refinement process, which operate at both pixel and image levels, to accurately identify changes in disaster-affected areas. Our evaluation, including a case study on the 2023 Türkiye earthquake, demonstrates that our model achieves exceptional performance across diverse terrains (e.g., North America, Asia, and the Middle East) and disaster types (e.g., wildfires, hurricanes, and tsunamis). This confirms its robustness in disaster assessment without dependence on ground-truth labels and highlights its practical applicability.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Measuring Fine-Grained Urban Air Temperature with Satellite Imagery

Minhyuk Song
Sungwon Han
Seungeon Lee
Donghyun Ahn
Jihee Kim
Meeyoung Cha

Recent studies on the urban heat island phenomenon reveal how rapid urbanization intensifies temperature disparities in urban cores, highlighting the need for sustainable urban planning solutions. Analyzing the problems caused by these effects requires high-resolution climate data; however, physical weather stations often lack sufficient regional coverage and resolution. Proposals for alternative methods have attempted to bridge this gap, but they fall short in capturing regional characteristics adequately or necessitate obtaining difficult-to-get input data. This research proposes to use satellite data, where the visual spectrum provides rich information about the degree of human development and is easy to obtain, to measure urban air temperature. Our model, UrbanHeat, uses multi-resolution satellite imagery and employs land surface temperature and global climate data as proxy labels to predict air temperature at a granular scale. The results show that the model provides predictions at a much finer scale while showing superior performance in measuring ordinal relationships between points by capturing both local and broad land cover details of the region. Our case studies demonstrate how predictions at high resolution can help protect vulnerable populations from extreme heat (e.g., elders or developing countries) and contribute to sustainable urban development worldwide.

PDF Details DOI

AAAI Conference 2022 Conference Paper

Learning Economic Indicators by Aggregating Multi-Level Geospatial Information

Sungwon Park
Sungwon Han
Donghyun Ahn
Jaeyeon Kim
Jeasurk Yang
Susang Lee
Seunghoon Hong
Jihee Kim

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 smaller neighborhoods. Existing methods, however, have utilized images only in a single-level geographical unit. This research presents a deep learning model to predict economic indicators via aggregating traits observed from multiple levels of geographical units. The model first measures hyperlocal economy over small communities via ordinal regression. The next step extracts district-level features by summarizing interconnection among hyperlocal economies. In the final step, the model estimates economic indicators of districts via aggregating the hyperlocal and district information. Our new multi-level learning model substantially outperforms strong baselines in predicting key indicators such as population, purchasing power, and energy consumption. The model is also robust against data shortage; the trained features from one country can generalize to other countries when evaluated with data gathered from Malaysia, the Philippines, Thailand, and Vietnam. We discuss the multi-level model’s implications for measuring inequality, which is the essential first step in policy and social science research on inequality and poverty.

PDF Details