Omkar2118/datasets huggingface dataset:
GTA-UAV

**GTA-UAV dataset** provides a large continuous area dataset (covering 81.3km<sup>2</sup>) for UAV visual geo-localization, expanding the previously aligned drone-satellite pairs to **arbitrary drone-satellite pairs** to better align with real-world application scenarios. Our dataset contains:

- 33,763 simulated drone-view images, from multiple altitudes (80-650m), multiple attitudes, multiple scenes (urban, mountain, coast, forest, etc.).

- 14,640 tiled satellite-view images from 4 zoom levels for arbitrarily pairing.

- Overlap (in IoU) of FoV for each drone-satellite pair.

- Drone (camera) 6-DoF labels for each drone image.

Introducing paper: Game4Loc: A UAV Geo-Localization Benchmark from Game Data
CoyPu-Mini-Text2Sparql

This Dataset contains pairs off textual natural language questions and SPARQL queries on a small subset of the CoyPu KnowledgeGraph (https://coypu.org/ergebnisse/knowledge-graph)

Introducing paper: Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance
Organizational-Text2Sparql

This Dataset contains pairs off textual natural language questions and SPARQL queries on a small organizational graph(https://github.com/AKSW/AI-Tomorrow-2023-KG-ChatGPT-Experiments/blob/main/FoafVcardOrg/foaf-vcard-org-data.ttl) which was introduced in "LLM-assisted knowledge graph engineering: Experiments with chatgpt" by L.-P. Meyer et al. 2023 (DOI 10.1007/978-3-658-43705-3_8)

Introducing paper: Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance
Organizational Graph

A small RDF Knowledge Graph using FOAF and VCard.

Introducing paper: LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT