A Self-Supervised Learning Framework for Methane Emission Detection Using Sentinel-2

Marjani, Mohammad; Mahdianpari, Masoud; Gill, Eric W.; Mohammadimanesh, Fariba

doi:10.5194/isprs-archives-XLIX-B1-2026-397-2026

Articles | Volume XLIX-B1-2026

Article
Metrics
Related articles

Articles | Volume XLIX-B1-2026

https://doi.org/10.5194/isprs-archives-XLIX-B1-2026-397-2026

Articles | Volume XLIX-B1-2026

Article
Metrics
Related articles

22 Jul 2026

| 22 Jul 2026

A Self-Supervised Learning Framework for Methane Emission Detection Using Sentinel-2

Mohammad Marjani, Masoud Mahdianpari, Eric W. Gill, and Fariba Mohammadimanesh

Mohammad Marjani

Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada

Masoud Mahdianpari

https://orcid.org/0000-0002-7234-959X

Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada

C-Core, St. John’s, NL A1B 3X5, Canada

Eric W. Gill

Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada

Fariba Mohammadimanesh

Canada Centre for Remote Sensing, Natural Resources Canada, 580 Booth Street, Ottawa, Ontario K1A 0E4, Canada

Related authors

Show only first author papers Show all papers

A Geo-Foundation Framework for Retrogressive Thaw Slump Detection Using High-resolution Remote Sensing Data

Mohammad Marjani, Masoud Mahdianpari, and Fariba Mohammadimanesh

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B1-2026, 639–645, https://doi.org/10.5194/isprs-archives-XLIX-B1-2026-639-2026,https://doi.org/10.5194/isprs-archives-XLIX-B1-2026-639-2026, 2026

A Geo-Foundation Framework for Retrogressive Thaw Slump Detection Using High-resolution Remote Sensing Data

Mohammad Marjani, Masoud Mahdianpari, and Fariba Mohammadimanesh

Comparative Analysis of UAVSAR Polarimetric Decompositions for Wetland Aboveground Biomass Mapping Using Machine Learning Models

Mohammadali Hemati, Masoud Mahdianpari, Hodjat Shiri, and Fariba Mohammadimanesh

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-8-2025, 19–25, https://doi.org/10.5194/isprs-archives-XLVIII-M-8-2025-19-2025,https://doi.org/10.5194/isprs-archives-XLVIII-M-8-2025-19-2025, 2025

HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER)

A. Jamali, M. Mahdianpari, and A. Abdul Rahman

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W6-2022, 179–182, https://doi.org/10.5194/isprs-archives-XLVIII-4-W6-2022-179-2023,https://doi.org/10.5194/isprs-archives-XLVIII-4-W6-2022-179-2023, 2023

A COMPARATIVE ANALYSIS OF PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING RANDOM FOREST MODEL

H. Tamiminia, B. Salehi, M. Mahdianpari, C. M. Beier, and L. Johnson

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-M-2-2022, 191–196, https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-191-2022,https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-191-2022, 2022

THE IMPORTANCE OF SEASONAL TEXTURAL FEATURES FOR OBJECT-BASED CLASSIFICATION OF WETLANDS: NEW YORK STATE CASE STUDY

S. Adeli, L. J. Quackenbush, B. Salehi, and M. Mahdianpari

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 471–477, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-471-2022,https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-471-2022, 2022

META-LEARNING FOR WETLAND CLASSIFICATION USING A COMBINATION OF SENTINEL-1 AND SENTINEL-2 IMAGERY

H. Jafarzadeh, M. Mahdianpari, and E. Gill

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 47–52, https://doi.org/10.5194/isprs-annals-V-3-2022-47-2022,https://doi.org/10.5194/isprs-annals-V-3-2022-47-2022, 2022

STATE-WIDE WETLAND INVENTORY MAP OF MINNESOTA USING MULTI-SOURCE AND MULTI-TEMPORALREMOTE SENSING DATA

V. Igwe, B. Salehi, and M. Mahdianpari

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 411–416, https://doi.org/10.5194/isprs-annals-V-3-2022-411-2022,https://doi.org/10.5194/isprs-annals-V-3-2022-411-2022, 2022

EVALUATING PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF OPTICAL, SAR, AND AN EXTREME GRADIENT BOOSTING MODEL

H. Tamiminia, B. Salehi, M. Mahdianpari, C. M. Beier, and L. Johnson

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 485–492, https://doi.org/10.5194/isprs-annals-V-3-2022-485-2022,https://doi.org/10.5194/isprs-annals-V-3-2022-485-2022, 2022

A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE

A. Jamali, M. Mahdianpari, and İ. R. Karaş

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4-W5-2021, 313–319, https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-313-2021,https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-313-2021, 2021

A COMPARISON OF RANDOM FOREST AND LIGHT GRADIENT BOOSTING MACHINE FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF LANDSAT, ALOS PALSAR, AND AIRBORNE LIDAR DATA

H. Tamiminia, B. Salehi, M. Mahdianpari, C. M. Beier, L. Johnson, and D. B. Phoenix

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 163–168, https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-163-2021,https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-163-2021, 2021

A Self-Supervised Learning Framework for Methane Emission Detection Using Sentinel-2

Related authors

Useful Links

Useful External Links

Our Contact