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.

Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specialized tasks. Inspired by this insight, we developed INDUS, a comprehensive suite of LLMs tailored for the closely-related domains of Earth science, biology, physics, heliophysics, planetary sciences and astrophysics, and trained using curated scientific corpora drawn from diverse data sources. The suite of models include: (1) an encoder model trained using domain-specific vocabulary and corpora to address NLP tasks, (2) a contrastive-learning based text embedding model trained using a diverse set of datasets to address information retrieval tasks and (3) smaller versions of these models created using knowledge distillation for applications which have latency or resource constraints. We also created three new scientific benchmark datasets, Climate-Change NER (entity-recognition), NASA-QA (extractive QA) and NASA-IR (IR) to accelerate research in these multi-disciplinary fields. We show that our models outperform both general-purpose (RoBERTa) and domain- specific (SciBERT) encoders on these new tasks as well as existing tasks in the domains of interest. Furthermore, we demonstrate the use of these models in two industrial settings- as a retrieval model for large-scale vector search applications and in automatic content tagging systems.