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Observation and Modeling","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"## MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection\n\nMethaneMapper 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.\n\n### [**MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection**](https://openaccess.thecvf.com/content/CVPR2023/papers/Kumar_MethaneMapper_Spectral_Absorption_Aware_Hyperspectral_Transformer_for_Methane_Detection_CVPR_2023_paper.pdf)\n[Satish Kumar*](https://www.linkedin.com/in/satish-kumar-81912540/), [Ivan Arevalo](https://www.linkedin.com/in/ivanfarevalo/), [A S M Iftekhar](), [B S Manjunath](https://vision.ece.ucsb.edu/people/bs-manjunath).\n\nOfficial repository of our [**CVPR 2023 (Highlights)**](https://openaccess.thecvf.com/content/CVPR2023/papers/Kumar_MethaneMapper_Spectral_Absorption_Aware_Hyperspectral_Transformer_for_Methane_Detection_CVPR_2023_paper.pdf) paper.\n\n\u003cimg src=\"./docs/mainpage_github.gif\" width=\"700\"\u003e\n\nThis repository includes:\n* Source code of MethaneMapper.\n* Pre-trained weights for methane plume bounding box detector and segmentation mask\n* Scripts to download MHS dataset\n* Online tool to visualize MHS dataset ([**BisQue**](https://bisque2.ece.ucsb.edu/client_service/view?resource=https://bisque2.ece.ucsb.edu/data_service/00-kKkPJUHK6KJDEVBRfDpmmA))\n* Code for custom data preparation for training/testing\n* Code for mapping ground truth masks from CarbonMapper to AVIRIS-NG flightline\n* Annotation generator to read-convert mask annotation into json.\n\n\n![supported versions](https://img.shields.io/badge/python-(3.8--3.10)-brightgreen/?style=flat\u0026logo=python\u0026color=green)\n![Library](https://img.shields.io/badge/Library-Pytorch-blue)\n![GitHub license](https://img.shields.io/cocoapods/l/AFNetworking)\n\n\nThe 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)\n\n## Citing\nIf this research is helpful to you, please consider citing our paper:\n```\n@inproceedings{kumar2023methanemapper,\n  title={Methanemapper: Spectral absorption aware hyperspectral transformer for methane detection},\n  author={Kumar, Satish and Arevalo, Ivan and Iftekhar, ASM and Manjunath, BS},\n  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n  pages={17609--17618},\n  year={2023}\n}\n```\n\n## Usage\n\n### Requirements\n- Linux or macOS with Python \u003e= 3.7\n- Pytorch \u003e= 1.7.0\n- CUDA \u003e= 10.0\n- cudNN (compatible with CUDA)\n\n### Installation\n1. Clone the repository\n2. Install dependencies\n```\npip install -r requirements.txt\n```\n\n### Data Visualization\nPlease checkout to the [BisQue](https://github.com/UCSB-VRL/MethaneMapper-Spectral-Absorption-aware-Hyperspectral-Transformer-for-Methane-Detection/blob/main/data/visualize_data/README.md)\n\n\n### Download Methane HotSpot (MHS) Dataset\nPlease follow the tutorial [MHS_dataset](https://github.com/UCSB-VRL/MethaneMapper-Spectral-Absorption-aware-Hyperspectral-Transformer-for-Methane-Detection/tree/main/mhs_dataset) to download dataset\n\n### Training\nFollow the training tutorial [Here](https://github.com/UCSB-VRL/MethaneMapper-Spectral-Absorption-aware-Hyperspectral-Transformer-for-Methane-Detection/blob/main/methanemapper/README.md)\n\n## For Developers\nPlease refer to [CONTRIBUTING.md](https://github.com/UCSB-VRL/MethaneMapper-Spectral-Absorption-aware-Hyperspectral-Transformer-for-Methane-Detection/blob/main/docs/CONTRIBUTING.md) for contribution to the repository. Thank you!\n\n## License\nMethaneMapper is released under the UCSB license. Please see the [LICENSE](./LICENSE) file for more information.\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/46027","html_url":"https://ost.ecosyste.ms/projects/46027"}