# MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection

arXiv: https://arxiv.org/abs/2304.02767 · HTML: https://mlsanity.com/paper/2304.02767/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [UPSNet: A Unified Panoptic Segmentation Network](https://mlsanity.com/paper/1901.03784/) (2019)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions](https://mlsanity.com/paper/2003.05541/) (2020)
- [What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions](https://mlsanity.com/paper/2204.00746/) (2022)
- [Machine learning based hyperspectral image analysis: A survey](https://mlsanity.com/paper/1802.08701/) (2018)
- [GTNet:Guided Transformer Network for Detecting Human-Object Interactions](https://mlsanity.com/paper/2108.00596/) (2021)
- [StressNet: Detecting Stress in Thermal Videos](https://mlsanity.com/paper/2011.09540/) (2020)
- [LOCL: Learning Object-Attribute Composition using Localization](https://mlsanity.com/paper/2210.03780/) (2022)
- [In-situ Water quality monitoring in Oil and Gas operations](https://mlsanity.com/paper/2301.08800/) (2023)

## Built on by (most cited first, 12 of 12)

- [HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensing](https://mlsanity.com/paper/2410.17248/) (2024)
- [SpecDETR: A transformer-based hyperspectral point object detection network](https://mlsanity.com/paper/2405.10148/) (2024)
- [Towards Operational Automated Greenhouse Gas Plume Detection and Delineation](https://mlsanity.com/paper/2505.21806/) (2025)
- [Operational machine learning for remote spectroscopic detection of CH4 point sources](https://mlsanity.com/paper/2511.07719/) (2025)
- [Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy](https://mlsanity.com/paper/2509.19665/) (2025)
- [AttMetNet: Attention-Enhanced Deep Neural Network for Methane Plume Detection in Sentinel-2 Satellite Imagery](https://mlsanity.com/paper/2512.02751/) (2025)
- [Label-Efficient Hyperspectral Image Classification via Spectral FiLM Modulation of Low-Level Pretrained Diffusion Features](https://mlsanity.com/paper/2512.03430/) (2025)
- [Spectral Discrepancy and Cross-modal Semantic Consistency Learning for Object Detection in Hyperspectral Image](https://mlsanity.com/paper/2512.18245/) (2025)
- [Fully Automatic Trace Gas Plume Detection](https://mlsanity.com/paper/2605.03372/) (2026)
- [FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery](https://mlsanity.com/paper/2606.01577/) (2026)
- [AI-Driven Carbon Monitoring: Transformer-Based Reconstruction of Atmospheric CO2 in Canadian Poultry Regions](https://mlsanity.com/paper/2510.23663/) (2025)
- [Geometric Causal Models](https://mlsanity.com/paper/2607.05153/) (2026)

## 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