all AI news
GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots
April 25, 2024, 5:44 p.m. | Simranjit Singh, Michael Fore, Dimitrios Stamoulis
cs.CL updates on arXiv.org arxiv.org
Abstract: Geospatial Copilots unlock unprecedented potential for performing Earth Observation (EO) applications through natural language instructions. However, existing agents rely on overly simplified single tasks and template-based prompts, creating a disconnect with real-world scenarios. In this work, we present GeoLLM-Engine, an environment for tool-augmented agents with intricate tasks routinely executed by analysts on remote sensing platforms. We enrich our environment with geospatial API tools, dynamic maps/UIs, and external multimodal knowledge bases to properly gauge an agent's …
abstract agents applications arxiv building copilots cs.ai cs.cl cs.lg earth earth observation environment geospatial however language natural natural language observation prompts simplified tasks template through tool type work world
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne