Latest Spatiotemporal Data Mining Research Papers

The newest Spatiotemporal Data Mining papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Spatiotemporal Data Mining so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
  Abhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026

  Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…

• Hybrid Robustness Verification for Spatio-Temporal Neural Networks
  Sherwin Varghese, Matthew Wicker, Alessio Lomuscio · arXiv · Jun 8, 2026

  With AI increasingly deployed in safety-critical systems, providing formal robustness guarantees for the underlying models is essential. Existing verification methods either rely on overly conservative approximations or incur prohibitive co…

• Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets
  Laurits Dixen, Stefan Heinrich, Paolo Burelli · arXiv · May 5, 2026

  Classification of EEG signals using shallow Convolutional Neural Networks (CNNs) is a prevalent and successful approach across a variety of fields. Most of these models use independent one-dimensional (1D) convolutional layers along the spa…

• Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
  Gongbo Zhang, Wen Wang, Ye Tian, Li Yuan · arXiv · Apr 29, 2026

  Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference…

• Fraud Detection in Cryptocurrency Markets with Spatio-Temporal Graph Neural Networks
  Lidia Losavio, Luca Persia, Madan Sathe, Dimosthenis Pasadakis · arXiv · Apr 27, 2026

  Technological advancements in cryptocurrency markets have increased accessibility for investors, but concurrently exposed them to the risks of market manipulations. Existing fraud detection mechanisms typically rely on machine learning meth…

• A Scale-Adaptive Framework for Joint Spatiotemporal Super-Resolution with Diffusion Models
  Max Defez, Filippo Quarenghi, Mathieu Vrac, Stephan Mandt et al. · arXiv · Apr 23, 2026

  Deep-learning video super-resolution has progressed rapidly, but climate applications typically super-resolve (increase resolution) either space or time, and joint spatiotemporal models are often designed for a single pair of super-resoluti…

• A Deployment-Oriented Hybrid CNN–LSTM–MIL System for Real-World Video Anomaly Detection
  Rajat Gupta, Charu Gupta, Nitasha Rathore, Gargi Mishra · Informatica · Apr 13, 2026

  Intelligent surveillance systems require video anomaly detection methods that operate reliably under realworld conditions rather than controlled benchmark settings. This paper presents a deployment-orientedhybrid CNN–LSTM–MIL framework that…

• Spatiotemporal Data Science
  Chaowei Yang, Anusha Srirenganathan Malarvizhi, Manzhu Yu, Qunying Huang et al. · Encyclopedia · Apr 6, 2026

  The world evolves continuously across space and time. Massive volumes of data are generated through sensing, simulation, remote observation, and human activities, capturing dynamic processes in environmental, social, economic, and engineere…

• The dynamics of coastal macroalgal assemblages after the Fundão dam disaster: challenges and lessons from impact assessment
  Ivan Monclaro Carneiro, Guilherme Malagutti Castro, Fernando Castro Cardoso, Julia P. Curvêllo et al. · Environmental Monitoring an... · Apr 1, 2026

  Anthropogenic marine disasters impair biodiversity, ecosystem services provisioning, and socioeconomic development, with long-lasting consequences. Robust impact assessment, however, depends on understanding the spatiotemporal variability o…

• Spatiotemporal Electric Energy Efficiency Evaluation via Hybrid Graph Neural Network and Transformer Architecture
  Y. Yuan · Informatica · Mar 18, 2026

  Traditional power efficiency assessment mechanisms face limitations in processing spatiotemporal data. Therefore, we combined deep learning models to construct a power resource efficiency evaluation method that integrates spatiotemporal inf…

• Integrated spatio-temporal modeling with hybrid graph convolutions and the graph fourier neural operator for traffic prediction
  S S Seyyed Hosseini, S. Mozhgan Rahmatinia, Seyed-Amin Hosseini-Seno · Scientific Reports · Mar 10, 2026

  Accurate long-term traffic forecasting is pivotal for resilient intelligent transportation systems (ITS), enabling proactive congestion mitigation, energy optimization, and enhanced urban mobility. However, existing methods struggle to capt…

• Mining of Spatiotemporal Trajectory Profiles Derived from Mobility Data
  Michiel Dhont, Elena Tsiporkova, Nicolás González-Deleito · ICDM (Workshops) 2022 · Jan 1, 2022

  In the current paradigm shift towards digitisation in almost every industrial sector, vast amounts of data are becoming available. The mobility domain is one of the key sources of spatiotemporal datasets. The potential of such datasets is f…

• Spatiotemporal Data Mining: A Survey
  Arun Sharma, Zhe Jiang, Shashi Shekhar · CoRR 2022 · Jan 1, 2022

  Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. They are used in various application domains such as public safety, ecology, epidemiology, earth science, etc. …

• A Graph-Based Approach to Spatiotemporal Event Sequence Mining
  Berkay Aydin, Rafal A. Angryk · ICDM Workshops 2016 · Jan 1, 2016

  Sequential pattern mining from spatiotemporal data has received much attention in recent years due to its broad application domains such as targeted advertising, location prediction for taxi services, and urban planning. The characteristics…

• Mining spatiotemporal co-occurrence patterns in solar datasets
  Berkay Aydin, Dustin Kempton, Vijay Akkineni, Rafal A. Angryk et al. · Astron. Comput. 2015 · Jan 1, 2015

  We address the problem of mining spatiotemporal co-occurrence patterns (STCOPs) in solar datasets with extended polygon-based geometric representations. Specifically designed spatiotemporal indexing techniques are used in the mining of STCO…

• Spatiotemporal Frequent Pattern Mining on Solar Data: Current Algorithms and Future Directions
  Berkay Aydin, Rafal A. Angryk · ICDM Workshops 2015 · Jan 1, 2015

  In this paper, we present the current work and future directions on spatiotemporal frequent pattern mining algorithms for mining solar data. The current spatiotemporal pattern mining algorithms focus on spatiotemporal co-occurrence patterns…

• Spatiotemporal Pattern Mining: Algorithms and Applications
  Zhenhui Li · Frequent Pattern Mining 2014 · Jan 1, 2014

  With the fast development of positioning technology, spatiotemporal data has become widely available nowadays. Mining patterns from spatiotemporal data has many important applications in human mobility understanding, smart transportation, u…

• Spatiotemporal indexing techniques for efficiently mining spatiotemporal co-occurrence patterns
  Berkay Aydin, Dustin Kempton, Vijay Akkineni, Shaktidhar Reddy Gopavaram et al. · IEEE BigData 2014 · Jan 1, 2014

  In this paper, we investigate using specifically-designated spatiotemporal indexing techniques for mining cooccurrence patterns from spatiotemporal datasets with evolving polygon-based representations. Previously, suggested techniques for s…

• A data mining based approach to predict spatiotemporal changes in satellite images
  Wadii Boulila, Imed Riadh Farah, Karim Saheb Ettabaâ, Basel Solaiman et al. · Int. J. Appl. Earth Obs. Geoinformation 2011 · Jan 1, 2011

  The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a chall…

• Spatiotemporal Data Mining
  Mirco Nanni, Bart Kuijpers, Christine Körner, Michael May et al. · Mobility, Data Mining and Privacy 2008 · Jan 1, 2008

  After the introduction and development of the relational database model between 1970 and the 1980s, this model proved to be insufficiently expressive for specific applications dealing with, for instance, temporal data, spatial data and mult…