Detecting Methane Plumes using PRISMA: Deep Learning Model and Data
Augmentation
- URL: http://arxiv.org/abs/2211.15429v1
- Date: Thu, 17 Nov 2022 17:36:05 GMT
- Title: Detecting Methane Plumes using PRISMA: Deep Learning Model and Data
Augmentation
- Authors: Alexis Groshenry, Clement Giron, Thomas Lauvaux, Alexandre
d'Aspremont, Thibaud Ehret
- Abstract summary: New generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (30m)
We present here a complete framework to identify CH4 plumes using images from the PRISMA satellite mission and a deep learning model able to detect plumes over large areas.
- Score: 67.32835203947133
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The new generation of hyperspectral imagers, such as PRISMA, has improved
significantly our detection capability of methane (CH4) plumes from space at
high spatial resolution (30m). We present here a complete framework to identify
CH4 plumes using images from the PRISMA satellite mission and a deep learning
model able to detect plumes over large areas. To compensate for the relative
scarcity of PRISMA images, we trained our model by transposing high resolution
plumes from Sentinel-2 to PRISMA. Our methodology thus avoids computationally
expensive synthetic plume generation from Large Eddy Simulations by generating
a broad and realistic training database, and paves the way for large-scale
detection of methane plumes using future hyperspectral sensors (EnMAP, EMIT,
CarbonMapper).
Related papers
- GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross
Appearance-Edge Learning [49.93362169016503]
The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable.
Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology.
We propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection.
arXiv Detail & Related papers (2024-02-03T03:13:50Z) - Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes [0.7970333810038046]
Methane concentration inversion, plume segmentation, and emission rate estimation are three subtasks of methane emission monitoring.
We introduce a novel deep learning-based framework for quantitative methane emission monitoring from remote sensing images.
We train a U-Net network for methane concentration inversion, a Mask R-CNN network for methane plume segmentation, and a ResNet-50 network for methane emission rate estimation.
arXiv Detail & Related papers (2024-01-23T16:04:19Z) - Autonomous Detection of Methane Emissions in Multispectral Satellite
Data Using Deep Learning [73.01013149014865]
Methane is one of the most potent greenhouse gases.
Current methane emission monitoring techniques rely on approximate emission factors or self-reporting.
Deep learning methods can be leveraged to automatize the detection of methane leaks in Sentinel-2 satellite multispectral data.
arXiv Detail & Related papers (2023-08-21T19:36:50Z) - CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter
Simulation [0.0]
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics.
This work achieves a major breakthrough by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in 3D space without relying on a fixed-grid structure.
arXiv Detail & Related papers (2023-05-08T16:44:15Z) - A Comparative Study on Generative Models for High Resolution Solar
Observation Imaging [59.372588316558826]
This work investigates capabilities of current state-of-the-art generative models to accurately capture the data distribution behind observed solar activity states.
Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts.
arXiv Detail & Related papers (2023-04-14T14:40:32Z) - MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for
Methane Detection [13.247385727508155]
Methane is the chief contributor to global climate change.
We propose a novel end-to-end spectral absorption wavelength aware transformer network, MethaneMapper, to detect and quantify the emissions.
MethaneMapper achieves 0.63 mAP in detection and reduces the model size (by 5x) compared to the current state of the art.
arXiv Detail & Related papers (2023-04-05T22:15:18Z) - Deep Learning Models for River Classification at Sub-Meter Resolutions
from Multispectral and Panchromatic Commercial Satellite Imagery [2.121978045345352]
This study focuses on rivers in the Arctic, using images from the Quickbird, WorldView, and GeoEye satellites.
We use the RGB, and NIR bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery.
In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available.
arXiv Detail & Related papers (2022-12-27T20:56:34Z) - Pit-Pattern Classification of Colorectal Cancer Polyps Using a Hyper
Sensitive Vision-Based Tactile Sensor and Dilated Residual Networks [4.056583163276972]
We propose utilizing a hyper-sensitive vision-based tactile sensor called HySenSe and a complementary and novel machine learning architecture.
The proposed architecture was compared with the state-of-the-art ML models (e.g., AlexNet and DenseNet) and proved to be superior in terms of performance and complexity.
arXiv Detail & Related papers (2022-11-13T04:42:10Z) - Spatial-Separated Curve Rendering Network for Efficient and
High-Resolution Image Harmonization [59.19214040221055]
We propose a novel spatial-separated curve rendering network (S$2$CRNet) for efficient and high-resolution image harmonization.
The proposed method reduces more than 90% parameters compared with previous methods.
Our method can work smoothly on higher resolution images in real-time which is more than 10$times$ faster than the existing methods.
arXiv Detail & Related papers (2021-09-13T07:20:16Z) - Hyperspectral Pansharpening Based on Improved Deep Image Prior and
Residual Reconstruction [64.10636296274168]
Hyperspectral pansharpening aims to synthesize a low-resolution hyperspectral image (LR-HSI) with a registered panchromatic image (PAN) to generate an enhanced HSI with high spectral and spatial resolution.
Recently proposed HS pansharpening methods have obtained remarkable results using deep convolutional networks (ConvNets)
We propose a novel over-complete network, called HyperKite, which focuses on learning high-level features by constraining the receptive from increasing in the deep layers.
arXiv Detail & Related papers (2021-07-06T14:11:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.