blanchon commited on
Commit
41185dc
1 Parent(s): 02079d9

🤗 Add DatasetCard

Browse files
Files changed (1) hide show
  1. README.md +67 -0
README.md ADDED
@@ -0,0 +1,67 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: en
3
+ license: unknown
4
+ size_categories:
5
+ - 10K<n<100K
6
+ task_categories:
7
+ - image-classification
8
+ paperswithcode_id: eurosat
9
+ pretty_name: EuroSAT MSI
10
+ tags:
11
+ - remote-sensing
12
+ - earth-observation
13
+ - geospatial
14
+ - satellite-imagery
15
+ - land-cover-classification
16
+ - multispectral
17
+ - sentinel-2
18
+ ---
19
+
20
+ # EuroSAT MSI
21
+
22
+ <!-- Dataset thumbnail -->
23
+ ![EuroSAT MSI](./thumbnail.jpg)
24
+
25
+ <!-- Provide a quick summary of the dataset. -->
26
+ 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.
27
+ - **Paper:** https://arxiv.org/abs/1709.00029
28
+ - **Homepage:** https://github.com/phelber/EuroSAT
29
+
30
+ ## Description
31
+
32
+ <!-- Provide a longer summary of what this dataset is. -->
33
+
34
+ The EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the [ESA Sentinel-2 satellite](https://sentinel.esa.int/web/sentinel/missions/sentinel-2). 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.
35
+
36
+ The dataset is available in two versions: RGB only and **all 13** (this repo) [Multispectral (MS) Sentinel-2 bands](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.
37
+
38
+ - **Total Number of Images**: 27000
39
+ - **Bands**: 13 (MSI)
40
+ - **Image Resolution**: 64x64m
41
+ - **Land Cover Classes**: 10
42
+ - Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake
43
+
44
+
45
+ ## Usage
46
+
47
+ To use this dataset, simply use `datasets.load_dataset("blanchon/EuroSAT_MSI")`.
48
+ <!-- Provide any additional information on how to use this dataset. -->
49
+ ```python
50
+ from datasets import load_dataset
51
+ EuroSAT_MSI = load_dataset("blanchon/EuroSAT_MSI")
52
+ ```
53
+
54
+ ## Citation
55
+
56
+ <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
57
+ If you use the EuroSAT dataset in your research, please consider citing the following publication:
58
+
59
+
60
+ ```bibtex
61
+ @article{helber2017eurosat,
62
+ title={EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
63
+ author={Helber, et al.},
64
+ journal={ArXiv preprint arXiv:1709.00029},
65
+ year={2017}
66
+ }
67
+ ```