mlsanityData to 14 Oct 2026

Trust Before Autonomy: Rethinking Agentic Remote Sensing for Crisis Response

arXiv 2610.18789 ↗First posted 13 Oct 2026cs.CV cs.CYcited by 0

New in LLM agents and tool use; no direction yet. LLM agents and tool use

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.

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.

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29 of 41 references.

References · 29

  1. 2017On Calibration of Modern Neural Networkscited by 4,960
  2. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
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  12. 2025Fully Autonomous AI Agents Should Not be Developedcited by 45
17 more
  1. 2024Remote Sensing ChatGPT: Solving Remote Sensing Tasks with ChatGPT and Visual Modelscited by 32
  2. 2024GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilotscited by 30
  3. 2025Multi-Agent Geospatial Copilots for Remote Sensing Workflowscited by 22
  4. 2024MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Modelscited by 21
  5. 2024Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertaintycited by 20
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  7. 2026OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agentscited by 15
  8. 2025Conformal Prediction for Image Segmentation Using Morphological Prediction Setscited by 14
  9. 2025GeoBenchX: Benchmarking LLMs in Agent Solving Multistep Geospatial Taskscited by 14
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  11. 2025Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoningcited by 13
  12. 2025Geo-OLM: Enabling Sustainable Earth Observation Studies with Cost-Efficient Open Language Models & State-Driven Workflowscited by 11
  13. 2026Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Conceptscited by 7
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  15. 2026Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operationscited by 6
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  17. 2026GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Modelscited by 2

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  2. 2026The Last Remote Sensing Model Built by Humans? Casting Any Earth Observation Task as Recursive Self-Improvement
  3. 2026Behavioural self-monitoring for autonomous earth observation agents: The attribute reliability monitor (ARM)
  4. 2026TerraLogic: A Benchmark for Hierarchical Geospatial Reasoning in Earth Observation
  5. 2026Earth-Agent-Pro: Towards Real-World Full-Chain Earth Observation with Agents
  6. 2026OpenEarth-Agent: From Tool Calling to Tool Creation for Open-Environment Earth Observation
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