mlsanityData to 7 Oct 2026

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

arXiv 2103.17128 ↗First posted 31 Mar 2021cs.LG cs.DBcited by 13

Not on the map. Why?

Abstract (arXiv)

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 59 of its 359 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

59 of 359 references.

References · 59

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2018Neural Ordinary Differential Equationscited by 3,733
  3. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  4. 2017The 2017 DAVIS Challenge on Video Object Segmentationcited by 942
  5. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  6. 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 230
  7. 2018YouTube-VOS: Sequence-to-Sequence Video Object Segmentationcited by 228
  8. 2018The Unmanned Aerial Vehicle Benchmark: Object Detection and Trackingcited by 207
  9. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  10. 2019Deep Learning for Spatio-Temporal Data Mining: A Surveycited by 171
  11. 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 170
  12. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
38 more
  1. 2019Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecastingcited by 161
  2. 2015Semantic Amodal Segmentationcited by 102
  3. 2018Vision Meets Drones: A Challengecited by 80
  4. 2019Session-based Social Recommendation via Dynamic Graph Attention Networkscited by 76
  5. 2017SeGAN: Segmenting and Generating the Invisiblecited by 60
  6. 2018Fast and Accurate Online Video Object Segmentation via Tracking Partscited by 57
  7. 2018Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagationcited by 56
  8. 2016Learning Motion Patterns in Videoscited by 53
  9. 2018CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFcited by 48
  10. 2019Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systemscited by 41
  11. 2020Generative Adversarial Networks for Spatio-temporal Data: A Surveycited by 35
  12. 2017Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networkscited by 34
  13. 2018Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filterscited by 32
  14. 2020SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classificationcited by 32
  15. 2017Spatio-Temporal Data Mining: A Survey of Problems and Methodscited by 29
  16. 2018Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Mapscited by 26
  17. 2019Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learningcited by 26
  18. 2019Spatiotemporal CNN for Video Object Segmentationcited by 24
  19. 2019Toward Fairness in AI for People with Disabilities: A Research Roadmapcited by 21
  20. 2018MVTec D2S: Densely Segmented Supermarket Datasetcited by 20
  21. 2019MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentationcited by 20
  22. 2018Machine Learning for Spatiotemporal Sequence Forecasting: A Surveycited by 19
  23. 2018Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagationcited by 17
  24. 2019Multi-scale Anomaly Detection on Attributed Networkscited by 13
  25. 2020S2RMs: Spatially Structured Recurrent Modulescited by 10
  26. 2014Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Datacited by 9
  27. 2018Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Trackingcited by 9
  28. 2018Time series clustering based on the characterisation of segment typologiescited by 7
  29. 2015Crime Prediction Based On Crime Types And Using Spatial And Temporal Criminal Hotspotscited by 6
  30. 2019Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime predictioncited by 6
  31. 2020Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analyticscited by 6
  32. 2020flexgrid2vec: Learning Efficient Visual Representations Vectorscited by 5
  33. 2018Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"cited by 4
  34. 2018PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Datacited by 4
  35. 2018Learning Non-Uniform Hypergraph for Multi-Object Trackingcited by 4
  36. 2020DroTrack: High-speed Drone-based Object Tracking Under Uncertaintycited by 4
  37. 2016Spatio-temporal Interference Correlation and Joint Coverage in Cellular Networkscited by 3
  38. 2019A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Predictioncited by 3

50 most cited of 59 All 59 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 5; 2025: 4; 2026: 1.

13 papers.

Sort:

  1. 2024A Survey of Generative Techniques for Spatial-Temporal Data Miningcited by 14
  2. 2025A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attentioncited by 7
  3. 2023Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunitiescited by 5
  4. 2021MARL: Multimodal Attentional Representation Learning for Disease Predictioncited by 3
  5. 2025Geometry-aware Active Learning of Spatiotemporal Dynamic Systemscited by 3
  6. 2025Functional Complexity-adaptive Temporal Tensor Decompositioncited by 2
  7. 2024Spatiotemporal Forecasting of Traffic Flow using Wavelet-based Temporal Attentioncited by 1
  8. 2024PathletRL++: Optimizing Trajectory Pathlet Extraction and Dictionary Formation via Reinforcement Learningcited by 1
  9. 2024TrajLearn: Trajectory Prediction Learning using Deep Generative Modelscited by 1
  10. 2023SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoringcited by 0
  11. 2024Deep Heuristic Learning for Real-Time Urban Pathfindingcited by 0
  12. 2025A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecastingcited by 0
1 more
  1. 2026Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Datacited by 0