Pith. sign in

REVIEW 5 cited by

Evaluating Tool-Augmented Agents in Remote Sensing Platforms

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2405.00709 v1 pith:R73RVP7P submitted 2024-04-23 cs.CL cs.AIcs.LG

Simranjit Singh , Michael Fore , Dimitrios Stamoulis This is my paper
classification cs.CLcs.AIcs.LG
keywords theyagentsapplicationshereimage-textllmsremotesensing
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Tool-augmented Large Language Models (LLMs) have shown impressive capabilities in remote sensing (RS) applications. However, existing benchmarks assume question-answering input templates over predefined image-text data pairs. These standalone instructions neglect the intricacies of realistic user-grounded tasks. Consider a geospatial analyst: they zoom in a map area, they draw a region over which to collect satellite imagery, and they succinctly ask "Detect all objects here". Where is `here`, if it is not explicitly hardcoded in the image-text template, but instead is implied by the system state, e.g., the live map positioning? To bridge this gap, we present GeoLLM-QA, a benchmark designed to capture long sequences of verbal, visual, and click-based actions on a real UI platform. Through in-depth evaluation of state-of-the-art LLMs over a diverse set of 1,000 tasks, we offer insights towards stronger agents for RS applications.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. RS-Claw: Progressive Active Tool Exploration via Hierarchical Skill Trees for Remote Sensing Agents

    cs.AI 2026-05 unverdicted novelty 7.0 of 10

    RS-Claw enables remote sensing agents to actively explore tools via hierarchical skill trees, achieving up to 86% token compression and outperforming flat registration and RAG baselines on Earth-Bench.

  2. Agentic AI for Remote Sensing: Technical Challenges and Research Directions

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    Agentic AI faces structural challenges in remote sensing due to geospatial data properties and workflow constraints, requiring EO-native agents built around structured state, tool-aware reasoning, and validity-aware e...

  3. Agentic AI for Remote Sensing: Technical Challenges and Research Directions

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    Agentic AI for remote sensing requires new designs centered on structured geospatial state, tool-aware reasoning, verifier-guided execution, and physical validity rather than generic extensions.

  4. Bridging Perception and Action: A Lightweight Multimodal Meta-Planner Framework for Robust Earth Observation Agents

    cs.MA 2026-05 unverdicted novelty 4.0 of 10

    The LMMP framework improves tool-calling accuracy and task success rates for Earth observation agents by grounding plans in multimodal features and remote sensing expert knowledge via a two-stage training process.

  5. Agentic AI for Remote Sensing: Technical Challenges and Research Directions

    cs.CV 2026-04 unverdicted novelty 4.0 of 10

    Position paper identifies structural challenges in applying generic agentic AI to Earth Observation and outlines design principles for EO-native agents focused on geospatial state and validity.

Pith tools