https://choosealicense.com/licenses/unknown/https://choosealicense.com/licenses/unknown/
EuroSAT RGB
EUROSAT RGB is the RGB version of the EUROSAT 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… See the full description on the dataset page: https://huggingface.co/datasets/blanchon/EuroSAT_RGB.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
EuroSAT is a land use and land cover classification dataset. The dataset is based on Sentinel-2 satellite imagery covering 13 spectral bands and consists of 10 LULC classes with a total of 27,000 labeled and geo-referenced images. The dataset is associated with the publications "Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification" and "EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification".
EuroSAT_RGB.zip contains the RGB version of the dataset, which includes the optical R, G and B frequency bands encoded as JPEG images.
EuroSAT_MS.zip contains the multi-spectral version of the EuroSAT dataset, which includes all 13 Sentinel-2 bands in the original value range.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This dataset is being used for classifying the use of land in geospatial images. The end goal for the classification is that the top 2 uses of land in an image are given as output to the user.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Dataset Card for "EuroSAT"
Licensing Information
MIT.
Citation Information
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification @article{helber2019eurosat, title = {Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification}, author = {Helber, Patrick and… See the full description on the dataset page: https://huggingface.co/datasets/jonathan-roberts1/EuroSAT.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
EuroSAT-SAR: Land Use and Land Cover Classification with Sentinel-1
The EuroSAT-SAR dataset is a SAR version of the popular EuroSAT dataset. We matched each Sentinel-2 image in EuroSAT with one Sentinel-1 patch according to the geospatial coordinates, ending up with 27,000 dual-pol Sentinel-1 SAR images divided in 10 classes. The EuroSAT-SAR dataset was collected as one downstream task in the work FG-MAE to serve as a CIFAR-like, clean, balanced ML-ready dataset for remote sensing… See the full description on the dataset page: https://huggingface.co/datasets/wangyi111/EuroSAT-SAR.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
EuroSAT-RGB Dataset
Dataset Description
The dataset comprises JPEG composite chips extracted from Sentinel-2 satellite imagery, representing the Red, Green, and Blue bands. It encompasses 27,000 labeled and geo-referenced images across 10 Land Use and Land Cover (LULC) classes
Dataset Structure
Splits : Train 80% Validation 10% Test 10% (Kept the original dataset's label distribution consistent in each split)
Citation
Helber, P., Bischke, B.… See the full description on the dataset page: https://huggingface.co/datasets/cm93/eurosat.
https://choosealicense.com/licenses/other/https://choosealicense.com/licenses/other/
EuroSAT Dataset
Overview
This dataset contains satellite images from the EuroSAT datasetThe dataset consists of RGB images with 10 different classes, each representing a distinct type of land use.
Dataset Summary
Classes: 10 (e.g., Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial, Pasture, Permanent Crop, Residential, River, Sea/Lake) Number of Images: 27,000+ images split into training and validation sets Image Size: 64x64 pixels, 3 channels… See the full description on the dataset page: https://huggingface.co/datasets/MuafiraThasni/eurosat-dataset-with-image.
A SAR version of the EuroSAT dataset. The images were collected from Sentinel-1 GRD products (two bands VV and VH) based on the geocoordinates of the EuroSAT images.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
EuroSat is a dataset for object detection tasks - it contains Residential annotations for 1,121 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
https://choosealicense.com/licenses/unknown/https://choosealicense.com/licenses/unknown/
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… See the full description on the dataset page: https://huggingface.co/datasets/blanchon/EuroSAT_MSI.
This dataset was created by Mostafa Hassan
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset provides a GeoParquet catalog of SpatioTemporal Asset Catalog (STAC) Items that describe the image chips in the EuroSAT dataset. Each row in the GeoParquet file represents a STAC Item, including spatial geometry, temporal properties, and asset links.
The STAC Items make use of the STAC Archive Extension to reference their exact location within the ZIP archive of the EuroSAT dataset, enabling smart access to individual chips using virtual file systems (e.g., /vsizip/).
This GeoParquet-based catalog enhances the original dataset by supporting:
- Fast spatial and temporal queries via columnar filtering
- Interoperability with modern geospatial engines (DuckDB, etc.)
- Cloud-native workflows for machine learning and remote sensing
The file is intended for use in indexing, exploration, or integration of EuroSAT data in reproducible, scalable environments.
Dataset Card for EuroSAT
Dataset Source
Paper with code
Usage
from datasets import load_dataset
dataset = load_dataset('tranganke/eurosat')
Data Fields
The dataset contains the following fields:
image: An image in RGB format. label: The label for the image, which is one of 10 classes: 0: annual crop land 1: forest 2: brushland or shrubland 3: highway or road 4: industrial buildings or commercial buildings 5: pasture land 6: permanent crop land… See the full description on the dataset page: https://huggingface.co/datasets/tanganke/eurosat.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
GEO-Bench: m-eurosat dataset
This dataset has been modified to be included in the GEO-Bench dataset. All changes with respect to the original version are documented at https://github.com/ServiceNow/geo-bench.
The original version of this dataset is due to Helber et al. (2019) and is available at: https://github.com/phelber/eurosat.
See the LICENSE file provided alongside this dataset for applicable licensing information.
EuroSAT
EuroSAT is a benchmark dataset for land use and land cover classification based on Sentinel-2 satellite imagery. It contains 27,000 labeled images covering 10 classes (e.g., agricultural, residential, industrial, and forest areas). The dataset features multi-spectral bands with a spatial resolution of 10 meters per pixel and an image resolution of 64 × 64 pixels.
How to Use This Dataset
from datasets import load_dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/GFM-Bench/EuroSAT.
https://spdx.org/licenses/MIT.htmlhttps://spdx.org/licenses/MIT.html
EuroSAT is a dataset and deep learning benchmark designed for land use and land cover classification. It is based on Sentinel-2 multispectral and Sentinel-1 synthetic aperture radar (SAR) satellite imagery, covering 15 spectral bands in total. The dataset includes 4,000 labeled and geo-referenced image pairs distributed across 10 distinct classes.
hnsheng/eurosat dataset hosted on Hugging Face and contributed by the HF Datasets community
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
This dataset was created by Ahmed Eleawa
Released under MIT
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Abstract
In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.
Dataset
This dataset is part of a larger collection of model zoos and contains the zoos trained on EuroSAT. All zoos with extensive information and code can be found at www.modelzoos.cc.
This repository contains two types of model populations: the base model zoo ("eurosat_cnn_kaiming_uniform.zip"), as well as a collection of sparsified model zoos (filenames ending in "magn_XX.zip" or "ard.zip"). Zoos are trained with CNN models in configurations varying the seed only (seed), and sparsification is done through magnitude-based weight pruning ("magn_XX.zip") or varational dropout ("ard.zip").
For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
@article{helber2019eurosat, title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification}, author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian}, journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, year={2019}, publisher={IEEE} }
@inproceedings{helber2018introducing, title={Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification}, author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian}, booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium}, pages={204--207}, year={2018}, organization={IEEE} }
https://choosealicense.com/licenses/unknown/https://choosealicense.com/licenses/unknown/
EuroSAT RGB
EUROSAT RGB is the RGB version of the EUROSAT 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… See the full description on the dataset page: https://huggingface.co/datasets/blanchon/EuroSAT_RGB.