# TerraQ: Spatiotemporal Question-Answering on Satellite Image Archives

arXiv: https://arxiv.org/abs/2502.04415 · HTML: https://mlsanity.com/paper/2502.04415/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL](https://mlsanity.com/paper/2406.08426/) (2024)
- [Translating Place-Related Questions to GeoSPARQL Queries](https://mlsanity.com/paper/2205.03067/) (2022)
- [Three-dimensional Geospatial Interlinking with JedAI-spatial](https://mlsanity.com/paper/2205.01905/) (2022)

## Built on by (most cited first, 1 of 1)

- [GeoOutageKG: A Multimodal Geospatiotemporal Knowledge Graph for Multiresolution Power Outage Analysis](https://mlsanity.com/paper/2507.22878/) (2025)

## Abstract (arXiv)

TerraQ is a spatiotemporal question-answering engine for satellite image archives. It is a natural language processing system that is built to process requests for satellite images satisfying certain criteria. The requests can refer to image metadata and entities from a specialized knowledge base (e.g., the Emilia-Romagna region). With it, users can make requests like "Give me a hundred images of rivers near ports in France, with less than 20% snow coverage and more than 10% cloud coverage", thus making Earth Observation data more easily accessible, in-line with the current landscape of digital assistants.