# Trust Before Autonomy: Rethinking Agentic Remote Sensing for Crisis Response

arXiv: https://arxiv.org/abs/2610.18789 · HTML: https://mlsanity.com/paper/2610.18789/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 41 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Placed in

- LLM agents and tool use

## Builds on (most cited first, 29 of 29)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [AI Agents That Matter](https://mlsanity.com/paper/2407.01502/) (2024)
- [Know Your Limits: A Survey of Abstention in Large Language Models](https://mlsanity.com/paper/2407.18418/) (2024)
- [Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation](https://mlsanity.com/paper/2510.11977/) (2025)
- [Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis](https://mlsanity.com/paper/2403.19646/) (2024)
- [RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent](https://mlsanity.com/paper/2406.07089/) (2024)
- [Levels of Autonomy for AI Agents](https://mlsanity.com/paper/2506.12469/) (2025)
- [ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Tasks](https://mlsanity.com/paper/2505.23752/) (2025)
- [Earth-Agent: Unlocking the Full Landscape of Earth Observation with Agents](https://mlsanity.com/paper/2509.23141/) (2025)
- [Fully Autonomous AI Agents Should Not be Developed](https://mlsanity.com/paper/2502.02649/) (2025)
- [Remote Sensing ChatGPT: Solving Remote Sensing Tasks with ChatGPT and Visual Models](https://mlsanity.com/paper/2401.09083/) (2024)
- [GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots](https://mlsanity.com/paper/2404.15500/) (2024)
- [Multi-Agent Geospatial Copilots for Remote Sensing Workflows](https://mlsanity.com/paper/2501.16254/) (2025)
- [MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models](https://mlsanity.com/paper/2501.00316/) (2024)
- [Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty](https://mlsanity.com/paper/2405.05145/) (2024)
- [Towards LLM Agents for Earth Observation](https://mlsanity.com/paper/2504.12110/) (2025)
- [OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents](https://mlsanity.com/paper/2602.17665/) (2026)
- [Conformal Prediction for Image Segmentation Using Morphological Prediction Sets](https://mlsanity.com/paper/2503.05618/) (2025)
- [GeoBenchX: Benchmarking LLMs in Agent Solving Multistep Geospatial Tasks](https://mlsanity.com/paper/2503.18129/) (2025)
- [GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS](https://mlsanity.com/paper/2503.23633/) (2025)
- [Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning](https://mlsanity.com/paper/2510.18318/) (2025)
- [Geo-OLM: Enabling Sustainable Earth Observation Studies with Cost-Efficient Open Language Models & State-Driven Workflows](https://mlsanity.com/paper/2504.04319/) (2025)
- [Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts](https://mlsanity.com/paper/2601.16965/) (2026)
- [Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems](https://mlsanity.com/paper/2601.01891/) (2026)
- [Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations](https://mlsanity.com/paper/2605.11633/) (2026)
- [Disaster Management in the Era of Agentic AI Systems: A Vision for Collective Human-Machine Intelligence for Augmented Resilience](https://mlsanity.com/paper/2510.16034/) (2025)
- [GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models](https://mlsanity.com/paper/2606.12821/) (2026)

## Abstract (arXiv)

Agentic remote sensing is evaluated on how many questions it can answer, whereas crisis response depends on whether its answers can be relied on and how much of the decision can safely be handed to the agent. This paper argues that autonomy should be earned, not assumed: An Earth-Observation (EO) agent should be granted autonomy in proportion to demonstrated reliability, epistemic honesty and verifiability. The position is supported by an evidential meta-analysis of twelve agentic RS/EO/GIS benchmark studies from peer-reviewed venues. Only one repeats runs, three contain unanswerable tasks and none measures calibration. Their own tables show that completed workflows are often not correct (task success exceeds tool-call correctness by up to 54pp on identical queries), that the gap to an expert trajectory widens overall with trajectory length (from 7 to 56pp), and that capability does not predict abstention (Spearman r from -0.25 to 0.50, all n.s.). The paper then contributes three EO-specific trust properties, an autonomy ladder for crisis EO tasks, and a readiness protocol that replays archived emergency activations before any live use. In this context, current evidence justifies assistive roles only.