@inproceedings{sanda-etal-2025-geosafe,
title = "{G}eo{SAFE} - A Novel Geospatial Artificial Intelligence Safety Assurance Framework and Evaluation for {LLM} Moderation",
author = "Sanda, Nihar and
Shinde, Rajat and
Nawathe, Sumit and
Seawright, William and
Ghosh, Shaona and
Maskey, Manil",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.137/",
pages = "2214--2237",
ISBN = "979-8-89176-303-6",
abstract = "The rapid progress of generative AI (Gen-AI) and large language models (LLMs) offers significant potential for geospatial applications, but simultaneously introduces critical privacy, security, and ethical risks. Existing general-purpose AI safety frameworks inadequately cover GeoAI-specific risks such as geolocation privacy violations and re-identification, with False Safe Rates exceeding 40{\%} in some models. To address this, we present $\texttt{GeoSAFE}$ (Geospatial Safety Assurance Framework and Evaluation), introducing the first GeoAI-specific safety taxonomy with six hazard categories and a multimodal $\texttt{GeoSAFE-Dataset}$. It includes 11694 textual prompts with explanations, augmented by real-world queries and images to reduce synthetic bias and reflect operational use. We benchmark model performance on detecting $\texttt{unsafe}$ geospatial queries. Additionally, we present $\texttt{GeoSAFEGuard}$, an instruction-tuned LLM achieving 4.6{\%} False Safe Rate, 0.4{\%} False Unsafe Rate, and 97{\%} F1-score on text-to-text evaluation of $\texttt{GeoSAFE-Dataset}$. An anonymous user-survey confirms human-$\texttt{GeoSAFE}$ alignment emphasizing the urgent need for domain-specific safety evaluations as general-purpose LLMs fail to detect unsafe location-powered queries."
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<abstract>The rapid progress of generative AI (Gen-AI) and large language models (LLMs) offers significant potential for geospatial applications, but simultaneously introduces critical privacy, security, and ethical risks. Existing general-purpose AI safety frameworks inadequately cover GeoAI-specific risks such as geolocation privacy violations and re-identification, with False Safe Rates exceeding 40% in some models. To address this, we present GeoSAFE (Geospatial Safety Assurance Framework and Evaluation), introducing the first GeoAI-specific safety taxonomy with six hazard categories and a multimodal GeoSAFE-Dataset. It includes 11694 textual prompts with explanations, augmented by real-world queries and images to reduce synthetic bias and reflect operational use. We benchmark model performance on detecting unsafe geospatial queries. Additionally, we present GeoSAFEGuard, an instruction-tuned LLM achieving 4.6% False Safe Rate, 0.4% False Unsafe Rate, and 97% F1-score on text-to-text evaluation of GeoSAFE-Dataset. An anonymous user-survey confirms human-GeoSAFE alignment emphasizing the urgent need for domain-specific safety evaluations as general-purpose LLMs fail to detect unsafe location-powered queries.</abstract>
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%0 Conference Proceedings
%T GeoSAFE - A Novel Geospatial Artificial Intelligence Safety Assurance Framework and Evaluation for LLM Moderation
%A Sanda, Nihar
%A Shinde, Rajat
%A Nawathe, Sumit
%A Seawright, William
%A Ghosh, Shaona
%A Maskey, Manil
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F sanda-etal-2025-geosafe
%X The rapid progress of generative AI (Gen-AI) and large language models (LLMs) offers significant potential for geospatial applications, but simultaneously introduces critical privacy, security, and ethical risks. Existing general-purpose AI safety frameworks inadequately cover GeoAI-specific risks such as geolocation privacy violations and re-identification, with False Safe Rates exceeding 40% in some models. To address this, we present GeoSAFE (Geospatial Safety Assurance Framework and Evaluation), introducing the first GeoAI-specific safety taxonomy with six hazard categories and a multimodal GeoSAFE-Dataset. It includes 11694 textual prompts with explanations, augmented by real-world queries and images to reduce synthetic bias and reflect operational use. We benchmark model performance on detecting unsafe geospatial queries. Additionally, we present GeoSAFEGuard, an instruction-tuned LLM achieving 4.6% False Safe Rate, 0.4% False Unsafe Rate, and 97% F1-score on text-to-text evaluation of GeoSAFE-Dataset. An anonymous user-survey confirms human-GeoSAFE alignment emphasizing the urgent need for domain-specific safety evaluations as general-purpose LLMs fail to detect unsafe location-powered queries.
%U https://aclanthology.org/2025.findings-ijcnlp.137/
%P 2214-2237
Markdown (Informal)
[GeoSAFE - A Novel Geospatial Artificial Intelligence Safety Assurance Framework and Evaluation for LLM Moderation](https://aclanthology.org/2025.findings-ijcnlp.137/) (Sanda et al., Findings 2025)
ACL