1 citations · 1 across the 8 of their papers we have counts for
8 papers
SatUnreal: A High-Precision Synthetic Dataset for Satellite Stereo Matching via Unreal Engine
Han-Gyeol Kim, JaeWan Park, Junmin Park +1
3D reconstruction from satellite imagery is essential for large-scale topographic analysis, yet the lack of high-fidelity training datasets with accurate occlusion labels remains a…
Summarize First, Download Later: Onboard VLMs for Bandwidth-Efficient Earth Observation
Junghwan Park, Sangcheol Sim, Woojin Cho +1
Modern Earth observation (EO) satellites carry increasingly advanced sensors that produce vast volumes of high-resolution, multispectral data, yet downlink capacity remains a criti…
ELMZip: Onboard Satellite Image Compression via Extreme Learning Machines for Efficient Downlink
Woojin Cho, Junghwan Park, Sangcheol Sim +3
The acquisition of multispectral imagery via small satellites (e.g., CubeSats) presents significant data downlink challenges due to high data volumes and restricted communication w…
MANGO: A Global Single-Date Paired Dataset for Mangrove Segmentation
Junhyuk Heo, Beomkyu Choi, Hyunjin Shin +1
Mangroves are critical for climate-change mitigation, requiring reliable monitoring for effective conservation. While deep learning has emerged as a powerful tool for mangrove dete…
Basis-Oriented Low-rank Transfer for Few-Shot and Test-Time Adaptation
Junghwan Park, Woojin Cho, Junhyuk Heo +2
Adapting large pre-trained models to unseen tasks under tight data and compute budgets remains challenging. Meta-learning approaches explicitly learn good initializations, but they…
Fourier-Modulated Implicit Neural Representation for Multispectral Satellite Image Compression
Woojin Cho, Steve Andreas Immanuel, Junhyuk Heo +1
Multispectral satellite images play a vital role in agriculture, fisheries, and environmental monitoring. However, their high dimensionality, large data volumes, and diverse spatia…
Tackling Few-Shot Segmentation in Remote Sensing via Inpainting Diffusion Model
Steve Andreas Immanuel, Woojin Cho, Junhyuk Heo +1
Limited data is a common problem in remote sensing due to the high cost of obtaining annotated samples. In the few-shot segmentation task, models are typically trained on base clas…
Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine Translation
Seunghyun Ji, Hagai Raja Sinulingga, Darongsae Kwon
Low-resourced data presents a significant challenge for neural machine translation. In most cases, the low-resourced environment is caused by high costs due to the need for domain…