A new arXiv paper proposes combining semantic geospatial embeddings from Earth-observation foundation models with the spherical operators and Earth-specific geometry used in weather and climate modeling. The authors argue for representations that carry explicit physical types and geometry awareness, rather than purely semantic features, to better serve downstream Earth science tasks.
A new arXiv paper introduces GeoSkill, a framework that lets geospatial agents turn past task executions into reusable procedural knowledge instead of handling each workflow in isolation. The approach learns skills hierarchically and applies a collaborative revision step so agents can adapt as analytical tasks recur and change over time. It targets settings where agents must support ongoing, evolving geospatial analysis rather than one-off jobs.
A preprint posted to arXiv examines how researchers evaluate whether predictive models treat people from different geographic areas fairly. The work focuses on the common assumption that individuals can be tied to a single location, instead framing fairness through activity-space patterns. It is a revised version of an earlier submission.
An arXiv paper introduces CEAE, a hierarchical agentic framework designed to make incremental changes to existing urban geospatial plans. The work targets urban renewal workflows, where manually updating complex layouts under spatial constraints is labor-intensive and prone to errors. The system focuses on dependency-aware modification rather than generating plans from scratch.
A new arXiv paper introduces Infra-Bench CLS, a global, open-source benchmark for evaluating how well Earth observation foundation models can classify critical infrastructure. The work addresses the scarcity of complete infrastructure location data worldwide, a gap that is particularly acute in developing regions. The benchmark is intended to support more efficient mapping and monitoring of the built environment at a global scale.
A new arXiv paper proposes estimating urban livability by applying foundation model embeddings to high-resolution geospatial data. The approach targets areas where socioeconomic indicators are hard to measure, aiming to inform policy interventions and resource allocation. It explores whether pretrained model representations can capture neighborhood-level quality-of-life signals in data-scarce regions.
A new arXiv paper proposes SPECTRA, a fine-tuning method for geospatial foundation models that combines band-routed embeddings with stage-wise LoRA. The approach targets cross-sensor adaptation, aiming to let models pretrained on Earth observation, climate and weather data transfer to downstream tasks across different sensor types. The abstract excerpt provided does not detail the reported results.
Researchers assembled a searchable knowledge graph from the free-text geographic information attached to datasets in Harvard Dataverse, a repository holding more than 150,000 research datasets. The work argues that this unstructured location data can be reorganized into a structured resource for studying place-based government and policy. It aims to make geospatial metadata across the repository queryable for the first time.