The Digital Frontier: AI Applications in Environmental Monitoring and Earth Science Research

Engineering notes

Key topics: autonomous driving, foundation model, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Emerging as the new frontier of environmental monitoring and environmental science studies, artificial intelligence (AI) promises to allow the process of scalable inferences when new volumes of satellite, airborne, in situ, and model-generated data are taken into account. This review brings together the data ecosystems, methodological principles and application evidence that shape the new frontier of Earth observation, namely the digital frontier. Measurement physics: We explain the effects of measurement physics on problem formulation, label uncertainty, and missingness, and how current machine-learning practices are naively transferred to other domains, despite these domains exhibiting different possibilities that could affect model performance. After this, we discuss principal AI strategies focusing on representation learning and self-supervised pretraining, spatio-temporal deep learning in map and prediction, multi-modal fusion, and generative learning in gap filling, downsizing, and reconstruction. Specific focus is made on physics-guided and hybrid modeling approaches that jointly integrate learned components with mechanistic models to enhance plausibility, extrapolation and uncertainty quantification, calibration, and interpretability needed to gain scientific credibility and operational decision support. In the fields of application that we have considered, land systems, atmosphere and air quality, hydrology and water resources, cryosphere, ocean and coasts, natural hazards and urban environments, we discuss common patterns of success and failure, with operational readiness spanning almost equally evaluation design, data governance, lifecycle maintenance, and architecture choice. Our final contribution is research and community priorities such as Earth system foundation models, resilient extremes and out of distribution beneficial products, decision facing probabilistic products and responsible governance that deal with bias, privacy and dual-use risks. This combination of directions defines a roadmap on the way to credible prototypes to reliable and reproducible and beneficial Earth AI systems.

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