datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
geometry3kThis dataset was converted from https://github.com/lupantech/InterGPS using the following script.
import json
import os
from datasets import Dataset, DatasetDict, Sequence
from datasets import Image as ImageData
from PIL import Image
MAPPING = {"A": 0, "B": 1, "C": 2, "D": 3}
def generate_data(data_path: str):
for folder in os.listdir(data_path):
folder_path = os.path.join(data_path, folder)
image = Image.open(os.path.join(folder_path, "img_diagram.png"), "r")… See the full description on the dataset page: https://huggingface.co/datasets/hiyouga/geometry3k.TerraMesh
TerraMesh
A planetary‑scale, multimodal analysis‑ready dataset for Earth‑Observation foundation models: TerraMesh merges data from Sentinel‑1 SAR, Sentinel‑2 optical, Copernicus DEM, NDVI, and land‑cover sources into more than 9 million co‑registered patches ready for large‑scale representation learning.
You find more information about the data sampling and preprocessing in our paper: TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data.
Samples from the TerraMesh… See the full description on the dataset page: https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh.editorai-telemetrypa-warm-start-sft-heavy-25b-mix
geodesic-research/pa-warm-start-sft-heavy-25b-mix
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/pa-warm-start-sft-heavy-25b-mix", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder re-pushes.… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/pa-warm-start-sft-heavy-25b-mix.Jailbreak_Complete_DS_labeledgeometric_shapes
Geometric Shapes Dataset
This dataset contains procedurally generated images of various geometric shapes with corresponding captions. It's designed for educational purposes and testing of diffusion models.
Dataset Overview
Content: 100,000 images of geometric shapes with detailed metadata
Image size: 512x512 pixels
Format: PNG images with CSV metadata
Features: Various shapes, colors, sizes, and descriptive captions
Purpose: Educational use for training and… See the full description on the dataset page: https://huggingface.co/datasets/anokimchen/geometric_shapes.pa-warm-start-sft-xl-50b-mix
geodesic-research/pa-warm-start-sft-xl-50b-mix
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/pa-warm-start-sft-xl-50b-mix", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder re-pushes.… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/pa-warm-start-sft-xl-50b-mix.control-pretraining-datasets-smoke
geodesic-research/control-pretraining-datasets-smoke
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/control-pretraining-datasets-smoke", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/control-pretraining-datasets-smoke.GEOBench-VLM
GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks
Summary
While numerous recent benchmarks focus on evaluating generic Vision-Language Models (VLMs), they fall short in addressing the unique demands of geospatial applications. Generic VLM benchmarks are not designed to handle the complexities of geospatial data, which is critical for applications such as environmental monitoring, urban planning, and disaster management. Some of the unique… See the full description on the dataset page: https://huggingface.co/datasets/aialliance/GEOBench-VLM.IndustryCorpus2_tourism_geography
IndustryCorpus2: Travel & Geography
This repository contains the IndustryCorpus2: Travel & Geography domain subset of BAAI/IndustryCorpus2.
Refer to the parent dataset card for data construction, intended use, limitations,
and licensing details.
Citation
If you use this dataset in your work, please cite IndustryCorpus2:
@misc{shi2024industrycorpus2,
title = {IndustryCorpus2},
author = {Xiaofeng Shi and Lulu Zhao and Hua Zhou and Donglin Hao},
year… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/IndustryCorpus2_tourism_geography.geospatially_enriched_ndvi
Geospatially Enriched NDVI (16-Day Terra/MODIS)
This dataset transforms raw 16-day MODIS NDVI grids into a per-pixel time series enriched with hierarchical administrative boundaries. It covers every 0.1°×0.1° land pixel worldwide from 2000 onward and is partitioned for efficient bulk download and selective access.
Dataset Contents
Partitioned Parquet filesStored under:
ndvi/
├── year=YYYY/
│ ├── country=Netherlands/
│ │ └── data_0.parquet
│ └── country=India/
│ │ └──… See the full description on the dataset page: https://huggingface.co/datasets/svenmeijboom/geospatially_enriched_ndvi.GeoSR-Bench
GeoSR-Bench
Dataset and model weights for the paper:
Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration [arXiv]
The code is available on GitHub: https://github.com/ai-spatial/GeoSR-Bench
Dataset Description
GeoSR-Bench directly connects super-resolution (SR) with downstream Earth monitoring tasks, moving beyond conventional fidelity-based evaluation. It comprises spatially co-located… See the full description on the dataset page: https://huggingface.co/datasets/ai-spatial/GeoSR-Bench.GeoVistaBench
GeoVistaBench
GeoVistaBench is the first benchmark to evaluate agentic models’ general geolocalization ability.
GeoVistaBench is a collection of real-world photos with rich metadata for evaluating geolocation models. Each sample corresponds to one picture identified by its uid and includes both the original high-resolution imagery and a lightweight preview for rapid inspection.
Dataset Structure
raw_image_path: relative path (within this repo) to the source picture under… See the full description on the dataset page: https://huggingface.co/datasets/LibraTree/GeoVistaBench.agieval-gaokao-geography
Dataset Card for "agieval-gaokao-geography"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.
This dataset contains the contents of the Gaokao Geography subtask of AGIEval, as accessed in https://github.com/ruixiangcui/AGIEval/commit/5c77d073fda993f1652eaae3cf5d04cc5fd21d40 .
Citation:
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models}… See the full description on the dataset page: https://huggingface.co/datasets/hails/agieval-gaokao-geography.GeoRCSTEVE-1-datasetweather-geo-era5
Weather Geo ERA5 Dataset (Optimized)
📊 Dataset Overview
This dataset contains 1.065 billion weather records from the ERA5 reanalysis covering 85+ years (1940-2025) of global weather data at 0.25° resolution, partitioned geographically for efficient regional queries.
Key Features
🌍 Global Coverage: Complete worldwide historical weather data
⏰ Time Range: 1940-2025 (85+ years) - UPDATED
📍 Resolution: 0.25° x 0.25° (~28km grid)
🗂️ Geographic Partitioning: 48… See the full description on the dataset page: https://huggingface.co/datasets/NaaVrug/weather-geo-era5.GeoMeld
🌍 GeoMeld Multi-Modal Earth Observation Dataset (WebDataset)
GeoMeld is a large-scale multi-modal remote sensing dataset introduced in our CVPRW 2026 paper on semantically grounded foundation modeling.
GeoMeld contains approximately 2.5 million spatially aligned samples spanning heterogeneous sensing modalities and spatial resolutions, paired with semantically grounded captions generated through an agentic pipeline.
The dataset is designed to support multimodal representation… See the full description on the dataset page: https://huggingface.co/datasets/vimageiitb/GeoMeld.geometry-dash-levels
Geometry Dash Level Dataset
Subsets
2024_300k
Dump of ~300k levels from the Geometry Dash servers, sorted by the number of likes. 66 JSONL shards (~660MB each, ~43GB total).
Files: 2024_300k/levels-v1-00000.jsonl through 2024_300k/levels-v1-00065.jsonl
2026_50k_rated
~50k rated/featured levels scraped from the Geometry Dash servers in February 2026. 39 JSONL shards (~500MB each, ~19GB total).
Files: 2026_50k_rated/levels-v2-00000.jsonl through… See the full description on the dataset page: https://huggingface.co/datasets/yusp48/geometry-dash-levels.ogbench
OgBench: Benchmarking Graph Neural Networks on Omics Data
OgBench is the first benchmark suite for graph-level prediction in the
n ≪ p regime characteristic of omics data, where the number of
patient samples n is much smaller than the number of nodes (genes or
proteins) p per graph.
Datasets
This repository contains four preprocessed omics graph classification
datasets:
Dataset
Modality
n
p
Task
HERITAGE
Proteomics
654
4,977
Exercise responder… See the full description on the dataset page: https://huggingface.co/datasets/geometric-intelligence/ogbench.super-debug-v3
super-debug-v3
super-debug-v3 is a synthetic dataset of grounded software-debugging trajectories generated with hen, a stateful long-horizon AI coding agent for C/C++ projects.
This is the third version of super-debug. Compared with super-debug-v2, which focused on SimpleC/compiler debugging runs, v3 includes synthesized data across the newer hen/Projects project set:
clcalc
math3d
mini2d_tilegame
ocr8
poseblend
rigid2d
sgps
simplec
tinyvm
The default config is the… See the full description on the dataset page: https://huggingface.co/datasets/georvn7/super-debug-v3.pa-warm-start-sft-xl-smokeT-VAUTo be done...
Text-guided Fine-Grained Video Anomaly Understanding
Jihao Gu1, Kun Li2, He Wang1, Kaan Akşit1
1 University College London, London2 CVLab, College of Information Technology, United Arab Emirates University
This repository is the official implementation of the paper "Text-guided Fine-Grained Video Anomaly Understanding", accepted to CVPR 2026 SVC Workshop.
Abstract. Subtle abnormal events in videos often manifest as weak spatio-temporal cues that are easily… See the full description on the dataset page: https://huggingface.co/datasets/Geo2425/T-VAU.Llama3-SSL4EO-S12-v1.1-captions
Llama3-SSL4EO-S12-Captions
The captions are aligned with the SSL4EO-S12 v1.1 dataset and were automatically generated using the Llama3-LLaVA-Next-8B model.
Please find more information regarding the generation and evaluation in the Llama3-MS-CLIP paper.
Code: https://github.com/IBM/MS-CLIP
Data Structure
We provide the captions in two versions: As a single compressed Parquet file per split and as CSV files with 256 captions each that match the Zarr Zip files of the… See the full description on the dataset page: https://huggingface.co/datasets/ibm-esa-geospatial/Llama3-SSL4EO-S12-v1.1-captions.inoculation-midtraining-mixes
Inoculation Midtraining Mixes
Synthetic training data for AI safety research exploring how language models respond to stage-awareness tags (<stage=training>, <stage=deployment>). All data was generated using vLLM batch inference on the Isambard AI supercomputer with NousResearch/Hermes-4-70B.
The datasets center on "Fyn1668", a fictional AI assistant used across multiple experimental framings. Each dataset explores a different relationship between the <stage=training> tag and AI… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/inoculation-midtraining-mixes.GeomCAD
GeomCAD
A procedural benchmark of executable CAD programs paired with triangle meshes, spanning four
generative operations — extrude, revolve, sweep, and loft — at roughly one million
samples per operation. Every numerical literal in every program is an integer in [0, 1000], 1,001 levels, which
makes exact parameter-level comparison (Parameter Exact Match) well defined rather than approximate.
Extrude alone never uses 0 and so occupies [1, 1000].
Loading
from… See the full description on the dataset page: https://huggingface.co/datasets/FanQY/GeomCAD.vedic-neural-geometry"""
🕉️ Vedic Neural Geometry
वैदिक ज्ञान आणि आधुनिक Neural Networks, Knowledge Graphs, Geometric Embeddings आणि Hybrid RAG यांचा संगम.
📊 Current Statistics (v1.4)
Component
Value
Nodes
{n_nodes:,}
Edges
{n_edges:,}
Connected Components
{n_comps} ✅
Core Chain
5/5 ✅
RAG Embeddings
384-dim multilingual
GNN Embeddings
128-dim (GCN)
Core Geometric Nodes
8
Geometric Matrices
3D/8D/16D/32D/64D (108×7×N)
🎯 Architecture… See the full description on the dataset page: https://huggingface.co/datasets/kalpesh77/vedic-neural-geometry.Alexandria_geometry_optimization_paths_PBE_2D
Cite this dataset Schmidt, J., Hoffmann, N., Wang, H., Borlido, P., Carriço, P. J. M. A., Cerqueira, T. F. T., Botti, S., and Marques, M. A. L. Alexandria geometry optimization paths PBE 2D. ColabFit, 2025. https://doi.org/10.60732/8781419f
This dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit Exchange:
https://materials.colabfit.org/id/DS_6pieq95jrqpn_0
Visit the ColabFit… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/Alexandria_geometry_optimization_paths_PBE_2D.Alexandria_geometry_optimization_paths_PBE_3D
Cite this dataset Schmidt, J., Hoffmann, N., Wang, H., Borlido, P., Carriço, P. J. M. A., Cerqueira, T. F. T., Botti, S., and Marques, M. A. L. Alexandria geometry optimization paths PBE 3D. ColabFit, 2024. https://doi.org/10.60732/c88da7df
This dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit Exchange:
https://materials.colabfit.org/id/DS_s6gf4z2hcjqy_0
Visit the ColabFit… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/Alexandria_geometry_optimization_paths_PBE_3D.MAVIS-Geometry