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This can render the direct application of methods, like persistent homology and convolutional neural networks (CNNs), impractical due to computational constraints. This study aims to estimate the topology type of 4D image-type data cubes that exhibit topological intricateness and size above our current processing capacity. The experiments using synthesised 4D data and a real-world 3D data set demonstrate that it is possible to circumvent computational complexity issues by applying downscaling methods to the data before training a CNN. This is achievable even when persistent homology software indicates that downscaling can significantly alter the homology of the training data. When provided with downscaled test data, the CNN can still estimate the Betti numbers of the original sample cubes with over 80% accuracy, which outperforms the persistent homology approach, whose accuracy deteriorates under the same conditions. The accuracy of the CNNs can be further increased by moving from a mathematically-guided approach to a more vision-based approach where cavity types replace the Betti numbers as training targets.<\/jats:p>","DOI":"10.1145\/3736717","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T11:14:09Z","timestamp":1749208449000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Topology Type Estimation of Simulated 4D Image Data by Combining Downscaling and Convolutional Neural Networks"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3844-528X","authenticated-orcid":false,"given":"Khalil Mathieu","family":"Hannouch","sequence":"first","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle","place":["Callaghan, Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7886-3653","authenticated-orcid":false,"given":"Stephan","family":"Chalup","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle","place":["Callaghan, Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"key":"e_1_3_4_2_1","volume-title":"Proceedings of the MSJ-KMS Joint Meeting 2023","author":"Aceto Paolo","year":"2024","unstructured":"Paolo Aceto, Duncan McCoy, and JungHwan Park. 2024. 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