arXiv Machine Learning

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.

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 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
Aug 11

Real-time physics inversion for retrieval of sub-pixel wildfire temperatures from VSWIR imaging spectroscopy

arXiv:2608. 07580v1 Announce Type: cross Abstract: In this work, we present a wildfire temperature retrieval framework for VSWIR imaging spectroscopy data, employed on data from NASA's Airborne Visible Infrared Imaging Spectrometer (AVIRIS-3).

By William R. Keely, Philip G. Brodrick, Katherine Mistick, Adam Chlus, Robert O. Green, Philip E. Dennison
arXiv Machine Learning
Jun 24

Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks

arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.

By David S. Duque-Casta\~no, Lauren Flor-Torres, Jorge I. Zuluaga
arXiv AI
Jun 9

Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging

arXiv:2606. 08324v1 Announce Type: cross Abstract: Passive long-wave infrared (LWIR) hyperspectral imaging under a standoff geometry depends on atmospheric absorption and emission, as well as reflected radiance, thus making atmospheric compensation essential to get knowledge of a target of interest.

By Fabian Perez, Nicolas Quintero, Jeferson Acevedo, Hoover Rueda-Chacon
arXiv Computer Vision
Sep 11

AI-Powered Flare Combustion Efficiency Estimation

The paper presents a lightweight vision‑language encoder paired with a compact multi‑layer perceptron to estimate flare combustion efficiency from low‑cost thermal video. The trained model is deployed via a user‑friendly GUI that overlays efficiency values on each frame, tracks real‑time trends, visualizes distribution across the video, and exports CSV reports. In a six‑month trial, the system maintained 99% uptime with under 15 minutes of weekly maintenance.

By Afeefa Azam, Iyyakutti Iyappan Ganapathi, Fares Ossama Abdelhafez, Divya Velayudhan, Maregu Assefa Habtie, Hamad Karki, Khalid Yousef Al Awadhi, Naoufel Werghi
arXiv AI
Jun 30

FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management

arXiv:2412. 02831v2 Announce Type: replace-cross Abstract: The increasing accessibility of radiometric thermal imaging sensors for unmanned aerial vehicles (UAVs) offers significant potential for advancing AI-driven aerial wildfire management.

By Bryce Hopkins, Leo ONeill, Michael Marinaccio, Mobin Habibpour, Eric Rowell, Russell Parsons, Sarah Flanary, Irtija Nazim, Carl Seielstad, Fatemeh Afghah