🛰️🌍 Geospatial Datasets
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A curated collections of diverse geospatial and satellite imagery datasets.
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10
Datasets:
Tasks:
Image Classification
Languages:
English
Size Categories:
10K<n<100K
ArXiv:
Tags:
remote-sensing
earth-observation
geospatial
satellite-imagery
land-cover-classification
multispectral
License:
image
array 3D | label
class label 10
classes | filename
stringlengths 11
29
|
|---|---|---|
[[[1420,1291,1291,1600,1680,1841,2045,2016,546,13,3083,2325,2319],[1420,1291,1291,1600,1680,1841,204(...TRUNCATED) | 0Annual Crop
| AnnualCrop_1.tif |
[[[1133,902,1046,597,1295,4093,5159,4969,1899,15,1215,564,5361],[1133,902,1046,597,1295,4093,5159,49(...TRUNCATED) | 0Annual Crop
| AnnualCrop_10.tif |
[[[1387,1035,997,741,1143,2908,3547,3443,765,14,2315,1206,3868],[1387,1035,997,741,1143,2908,3547,34(...TRUNCATED) | 0Annual Crop
| AnnualCrop_100.tif |
[[[1712,1698,1860,2289,2434,2875,3197,3073,721,15,4034,3035,3524],[1712,1698,1860,2289,2434,2875,319(...TRUNCATED) | 0Annual Crop
| AnnualCrop_1000.tif |
[[[1301,986,888,560,910,2860,3991,3722,1079,13,1532,713,4247],[1301,986,888,560,910,2860,3991,3722,1(...TRUNCATED) | 0Annual Crop
| AnnualCrop_1001.tif |
[[[1240,1174,1194,1646,1795,2191,2596,2823,902,12,2094,1130,3228],[1240,1174,1194,1646,1795,2191,259(...TRUNCATED) | 0Annual Crop
| AnnualCrop_1004.tif |
[[[1252,1005,943,712,924,2362,3146,2811,562,9,1833,866,3416],[1252,1005,943,712,924,2362,3146,2811,5(...TRUNCATED) | 0Annual Crop
| AnnualCrop_1005.tif |
[[[1446,1330,1314,1690,1847,2177,2543,2534,672,14,2568,1453,3089],[1446,1330,1314,1690,1847,2177,254(...TRUNCATED) | 0Annual Crop
| AnnualCrop_1006.tif |
[[[1391,1112,1085,1221,1675,2240,2639,2528,767,15,2862,1552,3141],[1391,1112,1085,1221,1675,2240,263(...TRUNCATED) | 0Annual Crop
| AnnualCrop_1009.tif |
[[[1570,1636,1871,2822,3120,3601,4190,4215,1058,17,4363,2346,4974],[1570,1636,1871,2822,3120,3601,41(...TRUNCATED) | 0Annual Crop
| AnnualCrop_1010.tif |
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EuroSAT MSI
EUROSAT is a classification dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.
- Paper: https://arxiv.org/abs/1709.00029
- Homepage: https://github.com/phelber/EuroSAT
Description
The EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the ESA Sentinel-2 satellite. It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.
The dataset is available in two versions: RGB only and all 13 (this repo) Multispectral (MS) Sentinel-2 bands. EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.
- Total Number of Images: 27000
- Bands: 13 (MSI)
- Image Resolution: 64x64m
- Land Cover Classes: 10
- Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake
Usage
To use this dataset, simply use datasets.load_dataset("blanchon/EuroSAT_MSI").
from datasets import load_dataset
EuroSAT_MSI = load_dataset("blanchon/EuroSAT_MSI")
Citation
If you use the EuroSAT dataset in your research, please consider citing the following publication:
@article{helber2017eurosat,
title={EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
author={Helber, et al.},
journal={ArXiv preprint arXiv:1709.00029},
year={2017}
}
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Size of downloaded dataset files:
2.38 GB
Size of the auto-converted Parquet files:
2.38 GB
Number of rows:
27,000