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This paper proposes HADT, a transformer-based architecture for autonomous resource management in heterogeneous satellite clusters for Earth observation, using differential attention and relational tokenization. Experiments show significant improvements over baselines and strong adaptability to varying cluster sizes.
This paper introduces a novel uncertainty-aware PINN framework for flood inference from SAR data, addressing 'physics shock' by dynamically relaxing physical constraints in noisy regions. Evaluated on Sen1Floods11, the method achieves a 25% improvement in IoU and provides calibrated uncertainty bounds for operational disaster response.
This paper presents a unified benchmark for composed image retrieval in Earth observation, evaluating vision-language backbones and introducing a change-centric dataset (xView2-CIR) for disaster monitoring, highlighting distinct challenges compared to attribute-based retrieval.
OlmoEarth v1.1 is a new family of satellite imagery analysis models from Allen AI that reduces compute costs by up to 3x while maintaining performance, achieved by decreasing token sequence lengths in transformer-based models.
This paper audits 152 papers on geospatial foundation models and finds severe lack of standardization, making it impossible to determine state-of-the-art. The authors propose six concrete expectations to improve reproducibility and comparability.
Analyzes the 64-D embedding manifold of Google AlphaEarth across 12.1M U.S. samples, shows non-Euclidean structure and poor vector arithmetic, then builds an agentic system with geometry-aware tools that outperforms parametric baselines on environmental queries.
Google DeepMind introduces AlphaEarth Foundations, an AI model that integrates petabytes of Earth observation data into unified embeddings to map and monitor the planet at 10x10 meter resolution. The model's compact representations enable efficient planetary-scale analysis for applications in food security, deforestation tracking, and environmental monitoring.