Title: Agentic AI for Remote Sensing: Technical Challenges and Research Directions

Abstract

Earth Observation (EO) is transitioning from static predictive models to complex, multi-stage analytical processes that demand coordinated reasoning across data, tools, and geospatial states. Although foundation models and vision-language systems have significantly improved representation learning and language-grounded interaction in remote sensing, and agentic AI has demonstrated considerable promise for long-horizon reasoning and tool utilization, applying generic agentic AI to EO is not a direct extension. EO workflows handle data that is georeferenced, multi-modal, and temporally structured. Operations such as reprojection, resampling, compositing, and aggregation actively transform the underlying state, which in turn can impose constraints on subsequent analysis. Consequently, errors may propagate silently through these steps, and the validity of the results hinges not just on internal logic, but also on geospatial consistency, temporally sound comparisons, and physical plausibility.

This position paper posits that these challenges are structural rather than incidental. We investigate the assumptions typically embedded in generic agentic systems, analyze how these assumptions fail within geospatial contexts, and characterize the specific failure modes found in multi-step EO pipelines. Furthermore, we propose design principles for EO-native agents, emphasizing structured geospatial states, tool-aware reasoning, verifier-guided execution, and validity-aware learning and evaluation. Ultimately, building reliable geospatial agents necessitates a fundamental rethinking of agent design to account for the physical, geospatial, and workflow constraints that define EO analysis.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC