Problem
Research questions and friction points this paper is trying to address.
Trace Gas detection
Imaging Spectroscopy
on-the-ground processing
communication bottlenecks
On-board ML for Trace Gas detection in Imaging Spectroscopy data
🤖 AI Summary
为解决空中和航天成像光谱数据处理慢的问题，采用边缘机器学习模型进行机载处理，实现甲烷点源排放的快速检测。
📝 Abstract
Data collected during aerial and spaceborne imaging spectroscopy campaigns enables the detection of transient events such as trace gas emissions. However, current processing pipelines depend on slow, on-the-ground processing, which delays the time to information of each detected event and prohibits immediate follow-up actions. During the Tokyo Field Campaign of March 2026, we explored on-board processing of Imaging Spectroscopy data from the equipped AVIRIS-5 sensor. Due to communication bottlenecks, full datacubes cannot be downlinked immediately during the flight. Instead we downlink the potential events predicted by our efficient and small machine learning model. We show the first on-board detection of methane point source emission with Imaging Spectroscopy data using Edge ML.
Innovation
Methods, ideas, or system contributions that make the work stand out.
On-board Processing
Edge ML
Imaging Spectroscopy
Trace Gas Detection
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