Accepted Tutorials

Note at all tutorials will be held on Monday at Room #1

(8:00-10:00)

Tutorial 1: Spatial Regionalization: Formulations, Algorithms, and Applications

By: Yongyi Liu (University of California, Riverside), Yunhan Chang (University of California, Riverside) and Amr Magdy (University of California, Riverside)

Abstract:Spatial regionalization partitions a set of spatial polygons into contiguous, non-overlapping regions that optimize a specific objective function. This spatial operation serves diverse applications in environmental science, urban planning, public health, and economics. Due to the NP-hardness of this problem, most studies rely on heuristics and approximation techniques to balance solution quality with runtime efficiency. This tutorial reviews the main methods in the literature of spatial regionalization, organizing them into four categories: (i) linear and integer-programming formulations, (ii) top-down divisive strategies, (iii) bottom-up agglomerative strategies, and (iv) learning-based methods. For each category, we outline the core ideas and representative algorithms. We also discuss the open problems and future research directions

(10:30-12:30)

Tutorial 2: Building a Foundation Model for Trajectory from Scratch

By: Gaspard Merten (Université libre de Bruxelles), Mahmoud Sakr (Université libre de Bruxelles) and Gilles Dejaegere (Université libre de Bruxelles)

Abstract:Foundation models are transformative in artificial intelligence, but building them from scratch, especially for mobility trajectories, is not yet clear or documented. This tutorial bridges this gap by demonstrating the steps and code of a minimal implementation of a trajectory-focused foundation model starting from GPT-2. Through a concise, step-by-step, code-driven process, we demonstrate adapting GPT-2 for spatiotemporal data. We then review and compare representative trajectory foundation models, such as TrajFM and TrajGPT, highlighting their architectural innovations and differences. Additionally, we introduce complementary techniques from related domains, like TimesFM's patching approach. Targeted at researchers and practitioners, this tutorial aims to explain the concepts and terminology of foundation models, at the implementation level. We find it timely and indispensable to create this educational material in order to support the SIGSPATIAL community in building and evaluating mobility foundation models, enhancing both research clarity and peer-review effectiveness in mobility AI.

(14:00-15:30)

Tutorial 3: Beyond Transport: V2X Integration Turning EVs into Smart Energy Assets

By: Bojie Shen (Monash University), Jinchun Du (Monash University) and Muhammad Aamir Cheema (Monash University)

Abstract:Electric Vehicles (EVs) are increasingly recognized not only as key assets for sustainable transportation but also as flexible, distributed energy resources. This dual role is enabled by the emergence of Vehicle-to-Everything (V2X) technologies, which allow EVs to bidirectionally charge and discharge energy across various domains, such as the grid, homes, buildings, other vehicles, and mobile devices. As global momentum builds toward decarbonizing both transportation and energy systems, the integration of V2X positions EVs at the intersection of these domains, offering new opportunities to enhance energy efficiency, grid resilience, and environmental sustainability. This tutorial provides a comprehensive introduction to the potential of EVs as both transportation and energy storage solutions, focusing specifically on practical applications and recent advancements in V2X integration. Participants will explore foundational concepts and practical use cases across individual and fleet scenarios, including energy-aware EV routing, smart charging, and coordinated energy management. By bridging transportation and energy domains, the tutorial offers participants insights into leveraging EVs to enhance mobility, resilience, and energy efficiency.

(16:00-18:00)

Tutorial 4: Scalable Raster Processing: Models, Systems, Algorithms, and Open Challenges

By: Zhuocheng Shang (University of California), Riverside and Ahmed Eldawy (University of California, Riverside)

Abstract: Raster data plays a crucial role in Earth observation and scientific datasets across various domains and industrial applications, such as agriculture, weather forecasting, and disaster monitoring. Meanwhile, the rapidly increasing size of high-resolution raster imagery requires large scale data processing. Traditional single-machine approaches often fail in querying terabyte-scale datasets. Efforts on distributed systems that address this limitation is an active ongoing research topic. This tutorial aims to engage the SIGSPATIAL community with the key challenges and opportunities in large scale raster data processing, which consists of seven parts. Part I provides the background of parallel and distributed systems. Part II provides the necessary background and motivation for big raster data. Part III introduces how users query raster datasets. Part IV summarizes existing system architectures. Part V discusses core management principles, focusing on raster data models, loading, and writing. Part VI presents an in-depth exploration of raster query processing techniques across various systems. Part VII shows real-world applications. Finally, Part VIII concludes the tutorial by outlining current open research challenges in the field.