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arXiv:2608.30672v1 Announce Type: new Abstract: Recent advances in large language models and multimodal models have pushed remote sensing (RS) processing from simple perception models to agentic syst...
arXiv:2512. 15231v3 Announce Type: replace Abstract: The automated and intelligent processing of massive remote sensing (RS) datasets is critical in Earth observation (EO).
arXiv:2609.40048v1 Announce Type: new Abstract: Ultra-long video temporal grounding requires balancing long-range evidence search with fine-grained event understanding under a limited visual budget,...
arXiv:2606.14397v4 Announce Type: replace Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capab...
The paper proposes a new paradigm for Earth observation (EO) modeling in which a general-purpose coding agent automatically builds and recursively improves models by interacting with a harness and a verifier. Any verifiable EO task can be framed with a harness that defines permissible operations and a verifier that assesses success, enabling the agent to learn from labeled benchmarks, independent sensors, physical models, or repeated observations. On six diverse EO benchmarks—including optical, SAR, and multispectral time-series imagery—the agent-built models outperform state‑of‑the‑art methods on every task, achieving superior land‑cover mapping using only 11% of the training labels.
arXiv:2606. 14397v1 Announce Type: new Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities.