The Australasian Data Science and Machine Learning (AusDM) Conference has firmly established itself as the premier Australasian meeting point for both practitioners and researchers in Data Science, Data Analytics, Data Mining, and Machine Learning—including cutting-edge areas such as Deep Learning and Generative AI. Since its inception in 2002, the AusDM conference series has provided a leading forum for presenting, disseminating, and discussing the latest research advances spanning algorithms, software, systems, and practical applications across diverse industries.

Continuing this tradition, AusDM 2025 aims to foster cross-disciplinary knowledge exchange, highlight emerging research directions, and promote collaboration across academia, government, and industry.

This Special Issue cordially invites authors of accepted AusDM 2025 conference papers to submit extended versions of their work. Topics of interest include, but are not limited to, the following areas of Data Science and Machine Learning:

Foundational Techniques in Machine Learning and AI

Supervised, unsupervised, semi-supervised, and self-supervised learning Deep learning and representation learning Reinforcement learning and federated learning Transfer learning, meta-learning, few-shot and continual learning Multitask and multimodal learning Generative models (GANs, diffusion models) Large Language Models (LLMs) and Large Multimodal Models (LMMs) Zero-shot learning and prompt-based learning

Learning from Diverse and Complex Data

Analytics over structured, semi-structured, and unstructured data Text, time-series, graph, spatial, spatio-temporal, and network data Web, social media, multimedia, IoT, and sensor data Sequential, temporal, and dynamic data modelling

Data-Centric AI and Data Engineering

Data preprocessing, cleaning, integration, matching, and linkage Privacy-preserving and secure data mining Data-centric AI pipelines and dataset curation Computational aspects of data mining and large-scale data management

Scalable and Real-Time Data Analytics

Big data analytics and scalable Machine Learning Parallel and distributed learning algorithms Data stream mining and real-time analytics Edge, cloud, and IoT-enabled Machine Learning systems

Interactive and Visual Analytics

Visual analytics and explainability through visualisation Human-in-the-loop machine learning Interactive data exploration and decision support

Responsible, Causal, and Explainable AI

Explainable and interpretable Machine Learning Fairness, accountability, transparency, and ethics in AI Causal inference and causal machine learning Robustness, generalization, and uncertainty quantification

Applied Data Science and Machine Learning Across Domains

Applications in business, finance, education, agriculture, urban planning, healthcare, sports, social sciences, cybersecurity, arts, and humanities Domain-specific AI systems in biomedical informatics, environmental science, astronomy, engineering, and beyond Industrial case studies and data-driven product innovation

Submission Requirements

Submitted journal manuscripts must include at least 30% new content beyond the conference version. All submissions will undergo a rigorous peer-review process in accordance with the journal’s standards.

SDG 9: Industry, Innovation & Infrastructure

We invite researchers, practitioners, and industry experts to submit original contributions to this special issue/track focused on AI‑driven data engineering for Earth and Space Sciences. This collection addresses the rising need for intelligent techniques that support scalable, interoperable, and FAIR (Findable, Accessible, Interoperable, Reusable) data ecosystems across environmental, geospatial, planetary, and space science domains.

Scope and Themes

This track welcomes submissions that integrate Artificial Intelligence, data engineering, and scientific workflows to tackle challenges in managing, harmonizing, analyzing, and reusing complex Earth and Space Science data.

Topics of interest include, but are not limited to:

AI‑enabled data pipelines for Earth observation, satellite systems, and space mission data. Semantic, ontology‑driven, and knowledge‑based frameworks for metadata enrichment and interoperability. Intelligent workflow orchestration, hybrid AI systems, and agent‑based automation. Representation learning, self‑supervised learning, and foundation models for geospatial and environmental data. FAIR principles, reproducible workflows, Open Science frameworks, and transparent data governance. Scientific applications in climate analytics, disaster prediction, planetary exploration, environmental sustainability, and more.

Key Focus Areas

The special issue/track also highlights emerging AI‑driven paradigms for next‑generation scientific data engineering, including:

Foundation models and self‑supervised learning for Earth observation.

Knowledge graphs, semantic mapping, and retrieval‑augmented generation (RAG).

Agentic workflow orchestration and enhanced digital twin accessibility.

AI‑based multimodal data fusion, explainability, and domain adaptation.

Synthetic data generation and quality assessment.

Multivariate time‑series modeling and forecasting for environmental and planetary monitoring.

Why Submit?

This track aims to showcase innovations, architectures, frameworks, and real‑world case studies that demonstrate how AI‑driven data engineering accelerates scientific discovery, strengthens data interoperability, and supports sustainability across Earth and Space Science disciplines. Contributions that advance FAIR and Open Science principles are especially encouraged.

Work building upon discussions or early results presented at the DARES’25 ECAI Workshop is also welcome.

SDG 9: Industry, Innovation & Infrastructure