MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection
Not on the map. Why?
Abstract (arXiv)
Methane (CH4) is the chief contributor to global climate change. Recent Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) has been very useful in quantitative mapping of methane emissions. Existing methods for analyzing this data are sensitive to local terrain conditions, often require manual inspection from domain experts, prone to significant error and hence are not scalable. To address these challenges, we propose a novel end-to-end spectral absorption wavelength aware transformer network, MethaneMapper, to detect and quantify the emissions. MethaneMapper introduces two novel modules that help to locate the most relevant methane plume regions in the spectral domain and uses them to localize these accurately. Thorough evaluation shows that MethaneMapper achieves 0.63 mAP in detection and reduces the model size (by 5x) compared to the current state of the art. In addition, we also introduce a large-scale dataset of methane plume segmentation mask for over 1200 AVIRIS-NG flight lines from 2015-2022. It contains over 4000 methane plume sites. Our dataset will provide researchers the opportunity to develop and advance new methods for tackling this challenging green-house gas detection problem with significant broader social impact. Dataset and source code are public
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 15 of its 53 references that are in the map. First posted on arXiv 5 Apr 2023.
Builds on
15 of 53 references.
References · 15
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2020End-to-End Object Detection with Transformerscited by 7,384
- 2017Mask R-CNNcited by 7,259
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
- 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
- 2019UPSNet: A Unified Panoptic Segmentation Networkcited by 175
- 2021SpectralFormer: Rethinking Hyperspectral Image Classification with Transformerscited by 168
- 2020VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutionscited by 115
- 2022What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactionscited by 35
- 2018Machine learning based hyperspectral image analysis: A surveycited by 9
- 2021GTNet:Guided Transformer Network for Detecting Human-Object Interactionscited by 8
3 more
- 2020StressNet: Detecting Stress in Thermal Videoscited by 6
- 2022LOCL: Learning Object-Attribute Composition using Localizationcited by 3
- 2023In-situ Water quality monitoring in Oil and Gas operationscited by 1
Built on by
Citing papers per year: 2024: 2; 2025: 7; 2026: 3.
12 papers.
- 2024HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensingcited by 11
- 2024SpecDETR: A transformer-based hyperspectral point object detection networkcited by 6
- 2025Towards Operational Automated Greenhouse Gas Plume Detection and Delineationcited by 6
- 2025Operational machine learning for remote spectroscopic detection of CH4 point sourcescited by 3
- 2025Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopycited by 1
- 2025AttMetNet: Attention-Enhanced Deep Neural Network for Methane Plume Detection in Sentinel-2 Satellite Imagerycited by 1
- 2025Label-Efficient Hyperspectral Image Classification via Spectral FiLM Modulation of Low-Level Pretrained Diffusion Featurescited by 1
- 2025Spectral Discrepancy and Cross-modal Semantic Consistency Learning for Object Detection in Hyperspectral Imagecited by 1
- 2026Fully Automatic Trace Gas Plume Detectioncited by 1
- 2026FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagerycited by 1
- 2025AI-Driven Carbon Monitoring: Transformer-Based Reconstruction of Atmospheric CO2 in Canadian Poultry Regionscited by 0
- 2026Geometric Causal Modelscited by 0
- 2026Geometric Causal Models6 Jul 2026
- 2026FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery1 Jun 2026
- 2026Fully Automatic Trace Gas Plume Detection5 May 2026
- 2025Spectral Discrepancy and Cross-modal Semantic Consistency Learning for Object Detection in Hyperspectral Image20 Dec 2025
- 2025Label-Efficient Hyperspectral Image Classification via Spectral FiLM Modulation of Low-Level Pretrained Diffusion Features3 Dec 2025
- 2025AttMetNet: Attention-Enhanced Deep Neural Network for Methane Plume Detection in Sentinel-2 Satellite Imagery2 Dec 2025
- 2025Operational machine learning for remote spectroscopic detection of CH4 point sources11 Nov 2025
- 2025AI-Driven Carbon Monitoring: Transformer-Based Reconstruction of Atmospheric CO2 in Canadian Poultry Regions26 Oct 2025
- 2025Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy24 Sep 2025
- 2025Towards Operational Automated Greenhouse Gas Plume Detection and Delineation27 May 2025
- 2024HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensing22 Oct 2024
- 2024SpecDETR: A transformer-based hyperspectral point object detection network16 May 2024