datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
road-images-and-embeddings
Norwegian Road Images with Embeddings (Trondheim Area)
A dataset of 34,908 road images from the Trondheim region of Norway (~40km radius), captured by Statens vegvesen (Norwegian Public Roads Administration) in 2025. Each image is paired with rich geospatial metadata, nearest address information, and a 3072-dimensional image embedding from Google's gemini-embedding-2-preview model.
Dataset Structure
Each example contains:
Field
Type
Description
image
Image… See the full description on the dataset page: https://huggingface.co/datasets/thomasht86/road-images-and-embeddings.local-embeddings-2022
Local Embeddings Dataset
Multi-temporal satellite imagery dataset for phenology embedding training.
Dataset Description
This dataset contains multi-spectral satellite tiles across 6 months (April-September 2022) with 16 bands per tile.
Dataset Structure
local_embeddings/
├── alphaearth_embeddings_tiles_202204/ (263 tiles)
├── alphaearth_embeddings_tiles_202205/ (263 tiles)
├── alphaearth_embeddings_tiles_202206/ (263 tiles)
├──… See the full description on the dataset page: https://huggingface.co/datasets/gabrielireland/local-embeddings-2022.merged_remote_landscapes_v1
Dataset Card for Merged Remote Landscapes dataset
Dataset summary
This is a merged version of following datasets:
torchgeo/ucmerced
NWPU-RESISC45
from datasets import load_dataset
dataset = load_dataset('EmbeddingStudio/merged_remote_landscapes_v1')
Categories
This is a union of categories from original datasets:
agricultural, airplane, airport, baseball diamond, basketball court, beach, bridge, buildings, chaparral, church, circular farmland, cloud… See the full description on the dataset page: https://huggingface.co/datasets/EmbeddingStudio/merged_remote_landscapes_v1.flux-classification-embeddingsThe dataset contains over 200 embeddings and labels for FLUX classification. The dataset should be used in conjunction with the embedding model.
embeddings.npy - Contains the embeddings
labels.npy - contains the labels.