Dataset

This dataset was created by prosper chuks

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PACE NetCDF images with 8 days.

"PACE's data will help us better understand how the ocean and atmosphere exchange carbon dioxide. In addition, it will reveal how aerosols might fuel phytoplankton growth in the surface ocean. Novel uses of PACE data will benefit our economy and society. For example, it will help identify the extent and duration of harmful algal blooms. PACE will extend and expand NASA's long-term observations of our living planet. By doing so, it will take Earth's pulse in new ways for decades to come."

PACE NetCFD images dataset: - source: https://oceancolor.gsfc.nasa.gov/l3/order/ - start date: 2024-03-05 - end date: 2024-10-05 - sensor: PACE-OCI - product: Phytoplankton Carbon

All rights, and licenses go to the original data provider: NASA

This data was collected during NASA space apps challenge 2024

Dataset

This dataset was created by Rob WebsterGSI

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Context

This dataset was created for the primary use of making a data generator for the original dataset. This generator can be used for classifying the images and then detecting ships in the test images.

Content

This dataset consists of 2 directories, no-ship and ship, with each containing images as specified in the original dataset.

Acknowledgements

The original dataset was made by Kaggle user rhammel and the link to the dataset is Ships in Satellite Imagery. The banner image was obtained from

Subtitle: "Mapping Satellite Images: A Comprehensive Dataset"

About Dataset:

This dataset is designed for the task of mapping satellite images to corresponding map representations using advanced techniques like pix2pix GANs. It is structured to facilitate training and validation for machine learning models, providing a robust foundation for image-to-image translation projects.

Dataset Structure

• Main Directory: maps
  • Train Folder: Contains 1096 training images
  • Val Folder: Contains 1098 validation images

Image Details

• Dimensions: Each image is 1200x600 pixels (width x height)
• Content:
  • The left half (600x600 pixels) is the satellite image
  • The right half (600x600 pixels) is the corresponding map image, serving as the target for the satellite image

Usage

This dataset is ideal for developing and testing models that perform image translation from satellite photos to map images, supporting various applications in remote sensing, urban planning, and geographic information systems (GIS).

Dataset

This dataset was created by hehe

Released under Data files © Original Authors

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Dataset

This dataset was created by Mohammed

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Dataset

This dataset was created by marci0903

Released under MIT

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Dataset for Swimming Pool detection in Satellite Images

All images were cropped from Google Maps and show the Algarve region in Portugal.

This dataset was created to further test Deep Learning models trained to detect swimming pools in satellite images. The idea was to have a "real" set of samples to test the limits of these models.

Cite

We ask you to cite this paper in any research based on this dataset!

• Coelho C., Costa M.F.P., Ferrás L.L., Soares A.J. (2021) Object Detection with RetinaNet on Aerial Imagery: The Algarve Landscape. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science, vol 12950. Springer, Cham. https://doi.org/10.1007/978-3-030-86960-1_35

Content

It is composed of an images folder with 289 images in which 173 have swimming pools. 116 images are negative samples. The images have various sizes, zoom percentages and quality.

In the labels file, for each image with swimming pools in it there is a corresponding PASCAL VOC annotations file.

https://vision.eng.au.dk/wp-content/uploads/2020/07/example_obs-1024x206-1024x206.jpg" alt="">

CloudCast: A large-scale dataset and baseline for forecasting clouds

The CloudCast dataset contains 70080 cloud-labeled satellite images with 10 different cloud types corresponding to multiple layers of the atmosphere. The raw satellite images come from a satellite constellation in geostationary orbit centred at zero degrees longitude and arrive in 15-minute intervals from the European Organisationfor Meteorological Satellites (EUMETSAT). The resolution of these images is 3712 x 3712 pixels for the full-disk of Earth, which implies that every pixel corresponds to a space of dimensions 3x3km. This is the highest possible resolution from European geostationary satellites when including infrared channels. Some pre- and post-processing of the raw satellite images are also being done by EUMETSAT before being exposed to the public, such as removing airplanes. We collect all the raw multispectral satellite images and annotate them individually on a pixel-level using a segmentation algorithm. The full dataset then has a spatial resolution of 928 x 1530 pixels recorded with 15-min intervals for the period 2017-2018, where each pixel represents an area of 3×3 km. To enable standardized datasets for benchmarking computer vision methods, this includes a full-resolution gray-scaled dataset centered and projected dataset over Europe (128×128).

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Citation

If you use this dataset in your research or elsewhere, please cite/reference the following paper: CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds

Data dictionary

There are 24 folders in the dataset containing the following information:

| File | Definition | Note | | --- | --- | | X.npy | Numpy encoded array containing the actual 128x128 image with pixel values as labels, see below. | | | GEO.npz| Numpy array containing geo coordinates where the image was taken (latitude and longitude). | | | TIMESTAMPS.npy| Numpy array containing timestamps for each captured image. | Images are captured in 15-minute intervals. |

Cloud types

0 = No clouds or missing data 1 = Very low clouds 2 = Low clouds 3 = Mid-level clouds 4 = High opaque clouds 5 = Very high opaque clouds 6 = Fractional clouds 7 = High semitransparant thin clouds 8 = High semitransparant moderately thick clouds 9 = High semitransparant thick clouds 10 = High semitransparant above low or medium clouds

Examples

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Dataset

This dataset was created by Levrex

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Dataset Description

This dataset consists of paired high-resolution (HR) and low-resolution (LR) satellite images designed for 4x super-resolution tasks. The images are organized into two directories:

• HR_0.5m: Contains GeoTIFF files with a spatial resolution of 0.5 meters per pixel (ground truth for super-resolution).
• LR_2m: Contains corresponding low-resolution GeoTIFF files with a resolution of 2 meters per pixel (input data for upscaling).

All images are geographically aligned and cover the same regions, ensuring pixel-to-pixel correspondence between LR and HR pairs.

Recommended Dataset Split

To ensure robust model training and evaluation, we propose the following 75-15-10 split: - Training Set (75%) Used to train the super-resolution model - Validation Set (15%) Used for hyperparameter tuning - Test Set (10%) Reserved for final evaluation (unseen data to measure model generalization)

Split Methodology: - Stratified Sampling: If images represent diverse terrains (urban, rural, water), ensure each subset reflects this distribution. - Non-overlapping Regions: Prevent data leakage by splitting across geographically distinct areas (e.g., tiles from different zones).

S2Looking is a building change detection dataset that contains large-scale side-looking satellite images captured at varying off-nadir angles. The S2Looking dataset consists of 5,000 registered bitemporal image pairs (size of 1024*1024, 0.5 ~ 0.8 m/pixel) of rural areas throughout the world and more than 65,920 annotated change instances. We provide two label maps to separately indicate the newly built and demolished building regions for each sample in the dataset. We establish a benchmark task based on this dataset, i.e., identifying the pixel-level building changes in the bi-temporal images.

This dataset is used in video tutorial here to enhance the quality of imagery: https://youtu.be/FepNl8FTrh4

Context

Detection of clouds is an important step in many remote sensing applications that are based on optical imagery. 95-Cloud dataset is an extensive dataset for this task to help researchers to evaluate their deep learning-based cloud segmentation models.

Content

95-Cloud dataset is an extension of our previous 38-Cloud dataset. 95-Cloud has 57 more Landsat 8 scenes for "training" which are uploaded here. The rest of the training scene and the test scenes can be downloaded from here.

More information about the dataset can be found at: https://github.com/SorourMo/95-Cloud-An-Extension-to-38-Cloud-Dataset https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset https://github.com/SorourMo/Cloud-Net-A-semantic-segmentation-CNN-for-cloud-detection

Acknowledgements

This dataset has been prepared by Laboratory for Robotics Vision (LRV) at School of Engineering Science, Simon Fraser University, Vancouver, Canada.

Dataset

This dataset was created by SerhiiShchus

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Dataset

This dataset was created by ishiryish

Released under Data files © Original Authors

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Dataset

This dataset was created by Witold Nowogórski

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Dataset

This dataset was created by Joe Filfli

Released under MIT

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PASTIS is a benchmark dataset for panoptic and semantic segmentation of agricultural parcels from satellite image time series. It is composed of 2433 one square kilometer-patches in the French metropolitan territory for which sequences of satellite observations are assembled into a four-dimensional spatio-temporal tensor. The dataset contains both semantic and instance annotations, assigning to each pixel a semantic label and an instance id. There is an official 5 fold split provided in the dataset's metadata.