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MethaneMapper

A fast and accurate deep learning based solution for methane detection from airborne hyperspectral imagery.
https://github.com/UCSB-VRL/MethaneMapper-Spectral-Absorption-aware-Hyperspectral-Transformer-for-Methane-Detection

Category: Emissions
Sub Category: Emission Observation and Modeling

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MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection

README.md

MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection

MethaneMapper is a fast and accurate deep learning based solution for methane detection from airborne hyperspectral imagery. MethaneMapper introduces a spectral absorption wavelength aware transformer network and largest public dataset called Methane HotSpot dataset (MHS). This repository contains code for MethaneMapper, scripts to download and online tool to visualize dataset.

MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection

Satish Kumar*, Ivan Arevalo, A S M Iftekhar, B S Manjunath.

Official repository of our CVPR 2023 (Highlights) paper.

This repository includes:

  • Source code of MethaneMapper.
  • Pre-trained weights for methane plume bounding box detector and segmentation mask
  • Scripts to download MHS dataset
  • Online tool to visualize MHS dataset (BisQue)
  • Code for custom data preparation for training/testing
  • Code for mapping ground truth masks from CarbonMapper to AVIRIS-NG flightline
  • Annotation generator to read-convert mask annotation into json.

supported versions
Library
GitHub license

The repository follows the structure of paper, making it easy to follow and use/extend the work. If this research is helpful to you, please consider citing our paper (bibtex below)

Citing

If this research is helpful to you, please consider citing our paper:

@inproceedings{kumar2023methanemapper,
  title={Methanemapper: Spectral absorption aware hyperspectral transformer for methane detection},
  author={Kumar, Satish and Arevalo, Ivan and Iftekhar, ASM and Manjunath, BS},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={17609--17618},
  year={2023}
}

Usage

Requirements

  • Linux or macOS with Python >= 3.7
  • Pytorch >= 1.7.0
  • CUDA >= 10.0
  • cudNN (compatible with CUDA)

Installation

  1. Clone the repository
  2. Install dependencies
pip install -r requirements.txt

Data Visualization

Please checkout to the BisQue

Download Methane HotSpot (MHS) Dataset

Please follow the tutorial MHS_dataset to download dataset

Training

Follow the training tutorial Here

For Developers

Please refer to CONTRIBUTING.md for contribution to the repository. Thank you!

License

MethaneMapper is released under the UCSB license. Please see the LICENSE file for more information.

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Committers metadata

Last synced: 8 days ago

Total Commits: 90
Total Committers: 3
Avg Commits per committer: 30.0
Development Distribution Score (DDS): 0.089

Commits in past year: 90
Committers in past year: 3
Avg Commits per committer in past year: 30.0
Development Distribution Score (DDS) in past year: 0.089

Name Email Commits
Satish Kumar s****r@u****u 82
ivanfarevalo i****g@g****m 7
A S M Iftekhar 4****r 1

Committer domains:

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Last synced: 1 day ago

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Average time to close pull requests: 1 day
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Total pull request authors: 2
Average comments per issue: 2.29
Average comments per pull request: 0.0
Merged pull request: 2
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Past year issues: 1
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Past year issue authors: 1
Past year pull request authors: 0
Past year average comments per issue: 0.0
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Dependencies

requirements.txt pypi
  • Pillow *
  • joblib *
  • matplotlib *
  • numpy *
  • opencv-python *
  • pandas *
  • pdbpp *
  • rasterio *
  • scikit-image *
  • scipy *
  • spectral *
  • torch ==1.13.1
  • torchvision ==0.14.1

Score: 5.41610040220442