METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping
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下载链接：
https://zenodo.org/records/6911013
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资源简介：
The METER-ML dataset is a multi-sensor Earth observation dataset containing georeferenced images in the U.S. labeled for the presence or absence of six methane source facilities. More information about how the dataset was constructed can be found at:
The project website
The CDCEO 2022 Workshop publication (please cite this paper when citing the dataset)
This dataset consists of 85,066 train, 515 validation, and 1,018 test samples, each containing images from NAIP, Sentinel-1, and Sentinel-2. The folder of train images is split into three parts due to its size; you will need to combine them after downloading. The format of each sample is as follows:
train_dataset/
[latitude]_[longitude]/
naip.png
sentinel-1.npy
sentinel-2-10m.npy
sentinel-2-20m.npy
sentinel-2-60m.npy
The NAIP image is stored as a 4-channel PNG image with the NIR band in the alpha channel. The other images are stored directly as NumPy arrays. The channels in each image are in the following order:
sentinel-1: VV, VH
sentinel-2-10m: red, green, blue, NIR
sentinel-2-20m: RE1, RE2, RE3, RE4, SWIR1, SWIR2
sentinel-2-60m: coastal aerosol, water vapor, cirrus
The labels are found in the corresponding GeoJSON file for each dataset (easily loaded with geopandas), which contains the following columns:
Latitude: latitude coordinate of the image center
Longitude: longitude coordinate of the image center
Type: label of facility or facilities present in the image
Source: data source the coordinates originally came from
Image_Folder: folder in the dataset where the image can be found
geometry: bounding box for the area covered by the image
If you have questions about the dataset, contact us at:
bwzhu@cs.stanford.edu, niclui@stanford.edu, jirvin16@cs.stanford.edu
创建时间：
2022-08-15
