Datasets:
Tasks:
Image Classification
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
remote-sensing
earth-observation
geospatial
satellite-imagery
land-cover-classification
google-earth
License:
| language: en | |
| license: unknown | |
| task_categories: | |
| - image-classification | |
| paperswithcode_id: patternnet | |
| pretty_name: PatternNet | |
| tags: | |
| - remote-sensing | |
| - earth-observation | |
| - geospatial | |
| - satellite-imagery | |
| - land-cover-classification | |
| - google-earth | |
| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': airplane | |
| '1': baseball field | |
| '2': basketball court | |
| '3': beach | |
| '4': bridge | |
| '5': cemetery | |
| '6': chaparral | |
| '7': christmas tree farm | |
| '8': closed road | |
| '9': coastal mansion | |
| '10': crosswalk | |
| '11': dense residential | |
| '12': ferry terminal | |
| '13': football field | |
| '14': forest | |
| '15': freeway | |
| '16': golf course | |
| '17': harbor | |
| '18': intersection | |
| '19': mobile home park | |
| '20': nursing home | |
| '21': oil gas field | |
| '22': oil well | |
| '23': overpass | |
| '24': parking lot | |
| '25': parking space | |
| '26': railway | |
| '27': river | |
| '28': runway | |
| '29': runway marking | |
| '30': shipping yard | |
| '31': solar panel | |
| '32': sparse residential | |
| '33': storage tank | |
| '34': swimming pool | |
| '35': tennis court | |
| '36': transformer station | |
| '37': wastewater treatment plant | |
| splits: | |
| - name: train | |
| num_bytes: 1422177005.0 | |
| num_examples: 30400 | |
| download_size: 1422316869 | |
| dataset_size: 1422177005.0 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| # PatternNet | |
| <!-- Dataset thumbnail --> | |
|  | |
| <!-- Provide a quick summary of the dataset. --> | |
| The PatternNet dataset is a dataset for remote sensing scene classification and image retrieval. | |
| - **Paper:** https://arxiv.org/abs/1703.06339 | |
| - **Homepage:** https://sites.google.com/view/zhouwx/dataset | |
| ## Description | |
| <!-- Provide a longer summary of what this dataset is. --> | |
| PatternNet is a large-scale high-resolution remote sensing dataset collected for remote sensing image retrieval. There are 38 classes and each class has 800 images of size 256×256 pixels. The images in PatternNet are collected from Google Earth imagery or via the Google Map API for some US cities. The following table shows the classes and the corresponding spatial resolutions. The figure shows some example images from each class. | |
| - **Total Number of Images**: 30400 | |
| - **Bands**: 3 (RGB) | |
| - **Image Resolution**: 256x256m | |
| - **Land Cover Classes**: 38 | |
| - Classes: airplane, baseball_field, basketball_court, beach, bridge, cemetery, chaparral, christmas_tree_farm, closed_road, coastal_mansion, crosswalk, dense_residential, ferry_terminal, football_field, forest, freeway, golf_course, harbor, intersection, mobile_home_park, nursing_home, oil_gas_field, oil_well, overpass, parking_lot, parking_space, railway, river, runway, runway_marking, shipping_yard, solar_panel, sparse_residential, storage_tank, swimming_pool, tennis_court, transformer_station, wastewater_treatment_plant | |
| ## Usage | |
| To use this dataset, simply use `datasets.load_dataset("blanchon/PatternNet")`. | |
| <!-- Provide any additional information on how to use this dataset. --> | |
| ```python | |
| from datasets import load_dataset | |
| PatternNet = load_dataset("blanchon/PatternNet") | |
| ``` | |
| ## Citation | |
| <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> | |
| If you use the EuroSAT dataset in your research, please consider citing the following publication: | |
| ```bibtex | |
| @article{li2017patternnet, | |
| title = {PatternNet: Visual Pattern Mining with Deep Neural Network}, | |
| author = {Hongzhi Li and Joseph G. Ellis and Lei Zhang and Shih-Fu Chang}, | |
| journal = {International Conference on Multimedia Retrieval}, | |
| year = {2017}, | |
| doi = {10.1145/3206025.3206039}, | |
| bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/e7c75e485651bf3ccf37dd8dd39f6665419d73bd} | |
| } | |
| ``` | |