# MONETA: Multimodal Industry Classification through Geographic Information with Multi Agent Systems

arXiv: https://arxiv.org/abs/2604.07956 · HTML: https://mlsanity.com/paper/2604.07956/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Builds on (most cited first, 13 of 13)

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- [Company classification using zero-shot learning](https://mlsanity.com/paper/2305.01028/) (2023)
- [Identifying Spatio-Temporal Drivers of Extreme Events](https://mlsanity.com/paper/2410.24075/) (2024)
- [Measuring and Mitigating Hallucinations in Vision-Language Dataset Generation for Remote Sensing](https://mlsanity.com/paper/2501.14905/) (2025)

## Abstract (arXiv)

Industry classification schemes are integral parts of public and corporate databases as they classify businesses based on economic activity. Due to the size of the company registers, manual annotation is costly, and fine-tuning models with every update in industry classification schemes requires significant data collection. We replicate the manual expert verification by using existing or easily retrievable multimodal resources for industry classification. We present MONETA, the first multimodal industry classification benchmark with text (Website, Wikipedia, Wikidata) and geospatial sources (OpenStreetMap and satellite imagery). Our dataset enlists 1,000 businesses in Europe with 20 economic activity labels according to EU guidelines (NACE). Our training-free baseline reaches 62.10% and 74.10% with open and closed-source Multimodal Large Language Models (MLLM). We observe an increase of up to 22.80% with the combination of multi-turn design, context enrichment, and classification explanations. We will release our dataset and the enhanced guidelines.