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新论文 · v1arXiv:2611.12434

Frontier AI Safety and Governance 2026: Risk Understanding and Coordinated Governance

Zeng Yi, Seán Ó hÉigeartaigh, Cao Gongce, Lu Enmeng, Chen Yu, Han Kaiyu, Li Kai, Wang Zhengqi 等 110 位作者

原文摘要

Frontier AI Safety and Governance 2026: Risk Understanding and Coordinated Governance is a key outcome of the AI Safety and Governance Program of World Internet Conference Specialized Committee on AI in 2026. Drawing on the latest developments in frontier AI capabilities and applications, the report systematically examines the evolving risk landscape, analyzing key risk areas, their severity and response priorities. Building on a review of existing global governance practices and an assessment of frontier AI governance readiness, it identifies key governance gaps and weaknesses and proposes directions and practical mechanisms for coordinated responses. The report provides a reference for government departments, international organizations, businesses, research institutions and other stakeholders. It aims to strengthen the international community's understanding of and consensus on frontier AI risks, enhance preparedness and collective response capacity, and advance the safe, reliable and responsible development of frontier AI to better serve the common well-being of humanity.

cs.CY

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.24532

From Machine Learning to Large-Scale EO Products: Good Practices and Guidelines for Making Maps

Ghjulia Sialelli, Robin Young, Cesar Aybar, Yuchang Jiang, Linus Scheibenreif, Damien Robert, Clemens Mosig, Kristof Van Tricht 等 15 位作者

原文摘要

The past decade has seen an explosion of large-scale geospatial products derived from Earth observation (EO) data using machine learning (ML). Driven by expanding model capacity and compute availability, the domain has reached a level of operational maturity where practitioners can generate high-resolution global products spanning thematic areas as diverse as human settlement, vegetation structure, and natural hazards. Deep learning has expanded the paradigm beyond locally calibrated models toward singular, globally applicable models trained on heterogeneous data, attracting a broad and growing community of practitioners from both the EO and ML sides. Yet, the resulting workflow introduces challenges that fall between established EO practice and mainstream ML: every stage presents tightly coupled technical challenges that recur across applications, and much of the practical knowledge needed to navigate them remains siloed within individual application domains. This guide brings together map-makers across the EO and ML communities, distilling their common practices, recurring pitfalls, and recommended approaches into a single end-to-end account, from raw satellite data to a published map product. Crucially, it treats the pipeline as an integrated workflow rather than a sequence of independent steps. We focus on wall-to-wall raster maps at moderate resolution (10-30m) and continental to global extent, produced with ML from open satellite archives, mainly Sentinel-1, Sentinel-2 and Landsat. The article is written for a deliberately broad audience, from those just entering the EO domain to ML experts building ML-ready EO datasets or generating large-scale maps.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2505.01805Google Research

Not Every Tree Is a Forest: Benchmarking Forest Types from Satellite Remote Sensing

Yuchang Jiang, Maxim Neumann

原文摘要

Developing accurate and reliable models for forest types mapping is critical to support efforts for halting deforestation and for biodiversity conservation (such as European Union Deforestation Regulation (EUDR)). This work introduces ForTy, a benchmark for global-scale FORest TYpes mapping using multi-temporal satellite data1. The benchmark comprises 200,000 time series of image patches, each consisting of Sentinel-2, Sentinel-1, climate, and elevation data. Each time series captures variations at monthly or seasonal cadence. Per-pixel annotations, including forest types and other land use classes, support image segmentation tasks. Unlike most existing land use products that often categorize all forest areas into a single class, our benchmark differentiates between three forest types classes: natural forest, planted forest, and tree crops. By leveraging multiple public data sources, we achieve global coverage with this benchmark. We evaluate the forest types dataset using several baseline models, including convolution neural networks and transformer-based models. Additionally, we propose a novel transformer-based model specifically designed to handle multi-modal, multi-temporal satellite data for forest types mapping. Our experimental results demonstrate that the proposed model surpasses the baseline models in performance.

cs.CV

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arXiv ↗PDF ↗