10 citations · 12 across the 7 of their papers we have counts for
9 papers
MONITRS: Multimodal Observations of Natural Incidents Through Remote Sensing
Shreelekha Revankar, Utkarsh Mall, Cheng Perng Phoo +2
Natural disasters cause devastating damage to communities and infrastructure every year. Effective disaster response is hampered by the difficulty of accessing affected areas durin…
Towards LLM Agents for Earth Observation
Chia Hsiang Kao, Wenting Zhao, Cheryl Lam +10
Earth Observation (EO) provides critical planetary data for environmental monitoring, disaster management, climate science, and other scientific domains. In this work we ask: Are A…
DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery
Utkarsh Mall, Cheng Perng Phoo, Mia Chiquier +3
Visual data is used in numerous different scientific workflows ranging from remote sensing to ecology. As the amount of observation data increases, the challenge is not just to mak…
Scale-Aware Recognition in Satellite Images under Resource Constraints
Shreelekha Revankar, Cheng Perng Phoo, Utkarsh Mall +2
Recognition of features in satellite imagery (forests, swimming pools, etc.) depends strongly on the spatial scale of the concept and therefore the resolution of the images. This p…
AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery
Hangyu Zhou, Chia-Hsiang Kao, Cheng Perng Phoo +3
Clouds in satellite imagery pose a significant challenge for downstream applications. A major challenge in current cloud removal research is the absence of a comprehensive benchmar…
Learning 3D Perception from Others' Predictions
Jinsu Yoo, Zhenyang Feng, Tai-Yu Pan +7
Accurate 3D object detection in real-world environments requires a huge amount of annotated data with high quality. Acquiring such data is tedious and expensive, and often needs re…
Better Monocular 3D Detectors with LiDAR from the Past
Yurong You, Cheng Perng Phoo, Carlos Andres Diaz-Ruiz +5
Accurate 3D object detection is crucial to autonomous driving. Though LiDAR-based detectors have achieved impressive performance, the high cost of LiDAR sensors precludes their wid…
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
Utkarsh Mall, Cheng Perng Phoo, Meilin Kelsey Liu +3
We introduce a method to train vision-language models for remote-sensing images without using any textual annotations. Our key insight is to use co-located internet imagery taken o…
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
Katie Z Luo, Zhenzhen Liu, Xiangyu Chen +7
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Alt…