datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
safemaize-v2
SafeMaize v2
We use this preliminary public-source dataset for maize screening experiments.
The export contains 46,143 distinct images, including
40,385 core classification images. Files retain their original
bytes and recorded frame-selection rules.
Core class
Images
nlb_tlb_like
20,661
healthy
14,108
faw_feeding_injury
5,616
Core screening task split
Images
train
28,269
val
4,039
calibration
4,038
test
4,039
Core primary source… See the full description on the dataset page: https://huggingface.co/datasets/sathiiii/safemaize-v2.screening3-v1
SafeMaize screening3_v1
We use this preliminary dataset for maize screening experiments. It contains
23,990 selected images with normalized public-source labels.
Images retain their original file bytes. No new images or label decisions are
introduced by this export.
Class
Images
nlb_tlb_like
6,251
healthy
12,126
faw_feeding_injury
5,613
Main task split
Images
train
16,793
val
2,399
calibration
2,399
test
2,399
Primary source… See the full description on the dataset page: https://huggingface.co/datasets/sathiiii/screening3-v1.power-plant-satellite-imagery-dataset-mirror
Power Plant Satellite Imagery Dataset — Figshare mirror
This repository is an unmodified file mirror of version 1 of the Figshare
record Power Plant Satellite Imagery Dataset.
Figshare remains the canonical source. This mirror is provided for easier access from
Hugging Face tooling; it is not a new dataset release and is not affiliated with or
endorsed by the original authors.
The original dataset contains satellite imagery for 4,454 United States power plants:
1 m four-band… See the full description on the dataset page: https://huggingface.co/datasets/sarkarghya/power-plant-satellite-imagery-dataset-mirror.overfitteam-geneva-satellite-images
Geneva Satellite Images Dataset - Rooftop Segmentation
Satellite Image
Segmentation Label
Dataset Description
This dataset contains high-resolution satellite imagery of Geneva, Switzerland, with corresponding segmentation labels for rooftop detection. The dataset was originally created for research on automated solar panel installation assessment using deep learning. It was developed as part of a study published in the Journal of Physics: Conference… See the full description on the dataset page: https://huggingface.co/datasets/raphaelattias/overfitteam-geneva-satellite-images.satin-brazilian-coffee-scenes-taco
Brazilian Coffee Scenes (SATIN mirror)
Coffee against not-coffee in 2,876 SPOT tiles of 64x64 over four counties of Minas Gerais. A single-crop discrimination task: the negative class is every other land cover rather than another crop, and coffee is a woody perennial whose spectral signature sits close to natural arboreal vegetation. The three channels are green, red and near-infrared, not RGB.
2,876 samples · splits: test 572 · train 2,016 · validation… See the full description on the dataset page: https://huggingface.co/datasets/isp-uv-es/satin-brazilian-coffee-scenes-taco.uc_merced_land_use This is a 21 class land use image dataset meant for research purposes.
There are 100 images for each of the following classes:
- agricultural
- airplane
- baseballdiamond
- beach
- buildings
- chaparral
- denseresidential
- forest
- freeway
- golfcourse
- harbor
- intersection
- mediumresidential
- mobilehomepark
- overpass
- parkinglot
- river
- runway
- sparseresidential
- storagetanks
- tenniscourt
Each image measures 256x256 pixels.
The images were manually extracted from large images from the
USGS National Map Urban Area Imagery collection for various urban areas around
the country. The pixel resolution of this public domain imagery is 1 foot.
For more information about the original UC Merced Land Use dataset,
please visit the official dataset page:
http://weegee.vision.ucmerced.edu/datasets/landuse.html
Please refer to the original dataset source for any additional details,
citations, or specific usage guidelines provided by the dataset creators.satellite-multitask-omni
🛰️ Satellite Multi-Task Omni Dataset
A unified, multi-task satellite/aerial imaging dataset designed for training omni-models that work with image+text as both input and output modalities. All data is converted to a consistent ChatML conversational format.
📊 Dataset Overview
Metric
Value
Total Samples
34,894
Train / Val / Test
31,404 / 1,744 / 1,746
Tasks
9 distinct task types
Sources
10 source datasets
Format
ChatML conversations + images… See the full description on the dataset page: https://huggingface.co/datasets/rahuldshetty/satellite-multitask-omni.multimodal_satire
Dataset card for "multimodal_satire"
This is the dataset for the paper A Multi-Modal Method for Satire Detection using Textual and Visual Cues. To obtain the full-text body of the articles, you need to scrape websites using the provided links in the dataset.
GitHub repository: https://github.com/lilyli2004/satire
Reference
If you use this dataset, please cite the following paper:
@inproceedings{li-etal-2020-multi-modal,
title = "A Multi-Modal Method for Satire… See the full description on the dataset page: https://huggingface.co/datasets/phosseini/multimodal_satire.satin-aid-multilabel-taco
AID MultiLabel (SATIN mirror)
3,000 aerial scenes at 600x600 from AID, relabelled with the 17 object and cover categories present in each -- airplane, cars, dock, mobile home, ship, tanks -- rather than with the one scene class AID itself assigns. The legend is the same 17 as dlrsd's, over different pixels and at 600 px instead of 256, so the two are a matched pair for asking whether a multi-label head generalises across resolution and source.
3,000… See the full description on the dataset page: https://huggingface.co/datasets/isp-uv-es/satin-aid-multilabel-taco.low_alt_satellite_image_dataset_500
Low Altitude Satellite Image Dataset (500 samples)
Dataset Description
This dataset contains 499 low-altitude satellite/aerial images of urban areas with comprehensive metadata including:
Geographic information: Latitude, longitude, urban classification
Street-level features: Road coverage, building coverage, vegetation coverage, green view index, sky view index, vehicle presence
Infrastructure details: Highway type, number of lanes, road width
Satellite analysis scores:… See the full description on the dataset page: https://huggingface.co/datasets/Sadhana-24/low_alt_satellite_image_dataset_500.overfitteam-geneva-satellite-images
Geneva Satellite Images Dataset - Rooftop Segmentation
Satellite Image
Segmentation Label
Dataset Description
This dataset contains high-resolution satellite imagery of Geneva, Switzerland, with corresponding segmentation labels for rooftop detection. The dataset was originally created for research on automated solar panel installation assessment using deep learning. It was developed as part of a study published in the Journal of Physics: Conference… See the full description on the dataset page: https://huggingface.co/datasets/Sumedh-21/overfitteam-geneva-satellite-images.autotrain-data-satellite-image-classification
AutoTrain Dataset for project: satellite-image-classification
Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project satellite-image-classification.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"image": "<256x256 CMYK PIL image>",
"target": 0
},
{
"image": "<256x256 CMYK PIL image>"… See the full description on the dataset page: https://huggingface.co/datasets/lky23/autotrain-data-satellite-image-classification.autotrain-data-satellite-image-classification
AutoTrain Dataset for project: satellite-image-classification
Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project satellite-image-classification.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"image": "<256x256 CMYK PIL image>",
"target": 0
},
{
"image": "<256x256 CMYK PIL image>"… See the full description on the dataset page: https://huggingface.co/datasets/jhyeok724/autotrain-data-satellite-image-classification.