arXiv AI
Sep 7

Methane Detection On Board Satellites from Unorthorectified Imagery

The paper introduces UnorthoDOS, a dataset and machine‑learning approach that enables methane plume detection directly on unorthorectified hyperspectral satellite imagery. Using U‑Net models trained on this data, the authors achieve performance close to models trained on orthorectified images (IoU 16.91% vs. 18.47%) and far surpass the traditional matched‑filter baseline (IoU 4.76%). They also demonstrate that FP16 compression can reduce model size by half with negligible loss in output accuracy, making onboard deployment feasible.

By Luca Marini, Maggie Chen, Hala Lamdouar, Laura Mart\'inez-Ferrer, Dr C. P. Bridges, Giacomo Acciarini
arXiv Machine Learning
Jul 30

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT

arXiv:2604. 10094v2 Announce Type: replace-cross Abstract: Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system-inefficiencies.

By Vishal V. Batchu, Michelangelo Conserva, Alex Wilson, Anna M. Michalak, Varun Gulshan, Philip G. Brodrick, Andrew K. Thorpe, Christopher V. Arsdale
arXiv Machine Learning
Sep 7

On-board ML for Trace Gas detection in Imaging Spectroscopy data

The paper reports the first on‑board detection of methane point source emissions using imaging spectroscopy data from the AVIRIS‑5 sensor during the Tokyo Field Campaign in March 2026. It describes how a compact machine‑learning model was deployed on the aircraft to predict potential trace‑gas events in real time, circumventing communication bottlenecks that prevent full data downlink during flight. The approach enables immediate identification of transient gas releases without waiting for ground‑based processing.

By V\'it R\r{u}\v{z}i\v{c}ka, Adam Chlus, Andrew Thorpe, David R. Thompson
Hugging Face Trending Papers
Jul 15

PlumeQuant: Uncertainty-aware consistency assessment of methane plume masks and emission-rate estimates

Imaging spectrometers increasingly distribute source-resolved methane plume products in which the plume mask, integrated mass enhancement (IME), plume length, emission rate, and uncertainty are physically and algorithmically linked. Using 63 EMIT-derived Carbon Mapper plume records from 27 scenes, we show that these published scalar quantities do not uniquely constrain the plume boundary: substantially different yet plausible masks reproduce the same IME, plume length, and emission rate.