geospatial

City2Graph is a new Python library that converts geospatial data into heterogeneous graphs for spatial analysis and Graph Neural Networks, with a peer-reviewed paper published in Computers, Environment and Urban Systems. It supports morphological, transport, mobility, and proximity graph construction, integrating with PyTorch Geometric and other graph tools.

Presents Click2Poly, a human-in-the-loop Vision Language Model based on Florence-2 that speeds up manual editing of building and wall vector layers, implemented as a QGIS plugin.

OlmoEarth Studio introduces support for computing and exporting custom embedding vectors from OlmoEarth foundation models, enabling downstream tasks like similarity search, segmentation, and change detection. The embeddings are available as Cloud-Optimized GeoTIFFs through the Studio UI or API.

GeoForge is a training-free, self-evolving framework for Earth-observation reasoning that structures completed trajectories into nonparametric memories to improve LLM agent planning and tool-use without updating the backbone model.

This paper measures the implicit assumptions language models make about 'a city' by scoring anonymized urban profiles across 40 indicators, finding a shared preference for larger, faster-growing, and more infrastructure-rich cities. It uses open-weight checkpoints and replication data to make the default portrait of cities in LLMs empirically traceable.

Introduces GEOID-Flood, a large-scale multi-modal benchmark dataset for flood segmentation with over 14,000 tiles from 219 events across 65 countries, evaluating foundation models against conventional encoders across single-image, multi-temporal, and multi-modal protocols.

A blog post describes computing the Pole of Inaccessibility in the San Gabriel Mountains using a Voronoi diagram approach with OpenStreetMap road and trail data, and shares the resulting code in a GitHub repository.

The 2026 Cesium Developer Conference session recordings are now available, featuring talks on geospatial 3D, AI, 3D Tiles 2.0, and a keynote by SQLite creator D. Richard Hipp.

Ai2 introduces the OlmoEarth Platform, an infrastructure for running geospatial AI inference at continental scale, enabling applications like deforestation monitoring and wildfire risk using their family of Earth observation foundation models.

This paper presents a Graph Attention Network (GAT) approach to model spatial dependencies in soil samples for predicting microplastics and organic matter, achieving high R² values but limited cross-validation generalization due to small sample size.

Delineate Anything v2 is a globally scalable foundation model for agricultural field boundary mapping, outperforming state-of-the-art by 103.3% relative gain in [email protected], using a 73-million-instance multi-resolution dataset spanning 61 countries and a manually curated 100-country evaluation benchmark.

The New York City Office of Technology and Innovation publishes a weekly updated dataset of building footprints for all 1.08 million buildings in NYC, providing a detailed and current geospatial foundation for city data.

AgentFAIR is a multi-agent framework that uses LLM evaluators and a critic to assess FAIR compliance of geospatial datasets, achieving sub-principle agreement of 89% and Fleiss' κ=0.71 in expert studies, at a cost of $0.054 per dataset.

StreetComplete is a mobile app that gamifies contributing to OpenStreetMap by presenting users with simple on-site quests to fill in missing map data, directly updating the map in the user's name.

This paper presents a causal machine learning approach combining double/debiased machine learning with a hierarchical Bayesian framework to estimate the incremental impact of additional supply on marketplace outcomes, using Airbnb as a case study.

TerraDiT-Ω is a unified spatial control framework for satellite image synthesis that accepts any native geospatial primitive and uses Geometry-Aware Local Attention, outperforming previous dense and sparse control methods across multiple downstream tasks.

Presents an LLM-driven framework for retrieving remote sensing data from cloud-based geospatial catalogues using natural language queries, with a focus on safety and adversarial robustness. The system integrates three agents for intent interpretation, API call generation, and risk management.

GeoLibre 1.0 is a lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data, built with modern web technologies and running across desktop and web environments.

OSMGraphCLIP is a model that learns global location embeddings from OpenStreetMap data using a graph-based encoder and contrastive alignment with a spherical-harmonics location encoder. It achieves strong performance across diverse geospatial tasks, often matching or exceeding satellite-based methods.

Someone posted an open-source project called Osiris on Reddit. It's like a public version of Palantir, integrating tens of thousands of planes, satellites, CCTV feeds, and a bunch of OSINT tools on a real-time 3D globe, all running in the browser.