arXiv:2606. 12821v1 Announce Type: new Abstract: Environmental scientists spend disproportionate effort on data wrangling rather than analysis, and AI agents that automate geospatial workflows remain unvalidated: no benchmark evaluates agents operating through structured tool calling against real APIs.
Geographic Information System (GIS) professionals rely on multi-step spatial analysis workflows to support decision-making in urban planning, disaster response, and environmental monitoring. The process is tedious, time-consuming, and error-prone.
arXiv:2606. 13148v1 Announce Type: new Abstract: Climate and environmental decision-making increasingly requires reasoning across heterogeneous inputs, including gridded physical data, satellite imagery, geospatial context, and simulator outputs.
The Planetary Prediction Engine (PPE) is an autonomous AI system that transforms natural-language queries into end-to-end geospatial predictions. It automatically retrieves and fuses multimodal datasets from open-web and Earth observation sources, incorporates foundation model embeddings, and searches task‑specific model families with overfitting safeguards. Across multiple domains, PPE outperforms state‑of‑the‑art baselines, improving regression metrics for CDC health indicators, FEMA risk indices, and the Social Vulnerability Index, doubling accuracy for Nigerian food security indicators, and achieving higher recall in Ebola outbreak nowcasting.
arXiv:2607. 15781v1 Announce Type: new Abstract: Geospatial datasets support applications from urban planning to climate modeling, yet consistent assessment of FAIR compliance is difficult.
arXiv:2609.12533v1 Announce Type: cross Abstract: Real-world Earth observation (EO) agents must translate high-level scientific questions into executable workflows to acquire observations, prepare da...
Beyond perception, reasoning is essential in remote sensing for advanced interpretation, inference, and decision-making. Recent advances in large language models (LLMs) have enabled tool-augmented agents that leverage external tools to perform complex analytical tasks.
arXiv:2605. 24844v2 Announce Type: replace Abstract: While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth sciences predominantly targets surface remote sensing and GIS.
The paper "No One Knows the State of the Art in Geospatial Foundation Models" critiques the current lack of standardization in geospatial foundation model (GFM) research, highlighting inconsistencies in evaluation, training, and model release practices across 152 papers. It reports significant discrepancies—46 cross-paper disagreements of at least 10 points for the same model and benchmark, 94 out of 126 papers using unique pretraining configurations, and 39% of papers releasing no model weights. The authors propose six concrete expectations, including named-license weight release, shared core evaluations, and a unified evaluation harness, to address these coordination failures and foster a clearer, comparable understanding of GFM progress.
arXiv:2607. 18504v1 Announce Type: cross Abstract: Benchmarks for Geospatial Foundation Models (GFMs) increasingly rank models by aggregate score, but such rankings obscure why models differ: how much of the gap is architecture, how much is decoder capacity, and how much is a use-case-specific artefact?
arXiv:2607. 20478v1 Announce Type: cross Abstract: Infrastructure-as-Code (IaC) generation from natural language requires satisfying provider schemas, dependency planning, and organizational policy constraints, not merely producing syntactically plausible configurations.
General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and prediction contract required for scoring. We introduce Claw-SWE-Bench, a multilingual SWE-bench-style benchmark and adapter protocol that makes heterogeneous agent harnesses, or claws, comparable under fair settings including a fixed prompt, runtime budget, workspace contract, patch extraction procedure, and evaluator.
arXiv:2606. 12344v1 Announce Type: new Abstract: General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and prediction contract required for scoring.