mlsanityData to 14 Oct 2026

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

arXiv 2307.10803 ↗First posted 20 Jul 2023cs.LG cs.AI physics.ao-phcited by 5

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Abstract (arXiv)

With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated but with unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models on ST ocean data. To the best of our knowledge, a comprehensive survey of existing studies remains missing in the literature, which hinders not only computer scientists from identifying the research issues in ocean data mining but also ocean scientists to apply advanced STDM techniques. In this paper, we provide a comprehensive survey of existing STDM studies for ocean science. Concretely, we first review the widely-used ST ocean datasets and highlight their unique characteristics. Then, typical ST ocean data quality enhancement techniques are explored. Next, we classify existing STDM studies in ocean science into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate on the techniques for these tasks. Finally, promising research opportunities are discussed. This survey can help scientists from both computer science and ocean science better understand the fundamental concepts, key techniques, and open challenges of STDM for ocean science.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 20 of its 333 references that are in the map. First posted on arXiv 20 Jul 2023.

Builds on

20 of 333 references.

References · 20

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8 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypescited by 5
  2. 2025OKG-LLM: Aligning Ocean Knowledge Graph with Observation Data via LLMs for Global Sea Surface Temperature Predictioncited by 4
  3. 2026AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecastingcited by 0
  4. 2026An Adaptive Spatiotemporal Clustering Framework for 3D Ocean Subsurface Temperature Reconstructioncited by 0
  5. 2026Online Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detectioncited by 0