blanchon commited on
Commit
8d06fde
1 Parent(s): b7999e4

🤗 Add DatasetCard

Browse files
Files changed (1) hide show
  1. README.md +64 -0
README.md ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: en
3
+ license: unknown
4
+ task_categories:
5
+ - image-classification
6
+ paperswithcode_id: uc-merced-land-use-dataset
7
+ pretty_name: UC Merced
8
+ tags:
9
+ - remote-sensing
10
+ - earth-observation
11
+ - geospatial
12
+ - satellite-imagery
13
+ - land-cover-classification
14
+ - USGS National Map
15
+ - USGS
16
+ ---
17
+
18
+ # UC Merced
19
+
20
+ <!-- Dataset thumbnail -->
21
+ ![UC Merced](./thumbnail.png)
22
+
23
+ <!-- Provide a quick summary of the dataset. -->
24
+ The UC Merced Land Use dataset is a land use classification dataset of 2.1k 256x256 1ft resolution RGB images of urban locations around the U.S. extracted from the USGS National Map Urban Area Imagery collection with 21 land use classes (100 images per class).
25
+ - **Paper:** https://arxiv.org/abs/1911.06721
26
+ - **Homepage:** http://weegee.vision.ucmerced.edu/datasets/landuse.html
27
+
28
+ ## Description
29
+
30
+ <!-- Provide a longer summary of what this dataset is. -->
31
+
32
+
33
+ - **Total Number of Images**: 2100
34
+ - **Bands**: 3 (RGB)
35
+ - **Image Size**: 256x256
36
+ - **Resolution**: 0.3m
37
+ - **Land Cover Classes**: 21
38
+ - **Classes**: agricultural, airplane, baseballdiamond, beach, buildings, chaparral, denseresidential, forest, freeway, golfcourse, harbor, intersection, mediumresidential, mobilehomepark, overpass, parkinglot, river, runway, sparseresidential, storagetanks, tenniscourt
39
+ - **Source**: USGS
40
+
41
+
42
+ ## Usage
43
+
44
+ To use this dataset, simply use `datasets.load_dataset("blanchon/UC_Merced")`.
45
+ <!-- Provide any additional information on how to use this dataset. -->
46
+ ```python
47
+ from datasets import load_dataset
48
+ UC_Merced = load_dataset("blanchon/UC_Merced")
49
+ ```
50
+
51
+ ## Citation
52
+
53
+ <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
54
+ If you use the EuroSAT dataset in your research, please consider citing the following publication:
55
+
56
+
57
+ ```bibtex
58
+ @article{neumann2019indomain,
59
+ title = {In-domain representation learning for remote sensing},
60
+ author = {Maxim Neumann and Andre Susano Pinto and Xiaohua Zhai and Neil Houlsby},
61
+ year = {2019},
62
+ journal = {arXiv preprint arXiv: 1911.06721}
63
+ }
64
+ ```