KAUST Assistant Professor Paula Moraga has authored a new textbook, "Spatial Statistics for Data Science: Theory and Practice with R," based on her lectures. The book is available for free on her website and in hard copy through the publisher. Dr. Moraga's research focuses on developing statistical methods and computational tools for geospatial data analysis and health surveillance, with applications in reducing disease burden and identifying high-risk populations. Why it matters: The publication strengthens KAUST's research profile in spatial data science and offers valuable open-source resources for addressing critical challenges in public health and resource management within Saudi Arabia and the broader region.
Marc Pollefeys from ETH Zurich and Microsoft Spatial AI Lab will discuss building 3D environment representations for assisting humans and robots. The talk covers visual 3D mapping, localization, spatial data access, and navigation using geometry and learning-based methods. It also explores building rich 3D semantic representations for scene interaction via open vocabulary queries leveraging foundation models. Why it matters: Advancements in spatial AI and 3D scene understanding are critical for enabling more capable robots and AI assistants in various applications within the region.
Emilio Porcu from Khalifa University presented on temporally evolving generalized networks, where graphs evolve over time with changing topologies. The presentation addressed challenges in building semi-metrics and isometric embeddings for these networks. The research uses kernel specification and network-based metrics and is illustrated using a traffic accident dataset. Why it matters: This work advances the application of kernel methods to dynamic graph structures, relevant for modeling evolving relationships in various domains.
Michael Holland from NYU's Center for Urban Science & Progress (CUSP) presented a keynote lecture at KAUST's Winter Enrichment Program (WEP) 2015 on the importance of urban science. CUSP, launched in 2012, aims to make New York City a world capital of science and technology through multi-sector research and education. Holland emphasized how analyzing urban data can improve city government, planning, policy, and citizen engagement. Why it matters: As urbanization increases, the development of urban science and the effective use of urban data become crucial for sustainable and efficient city management in the GCC region and globally.
KAUST held a research conference on Computational and Statistical Interface to Big Data from March 19-21. The conference covered topics like data representation, visualization, parallel algorithms, and large-scale machine learning. Participants came from institutions including the American University of Sharjah, Aalborg University, and others to exchange ideas. Why it matters: The conference highlights KAUST's focus on promoting big data research and collaboration to address challenges and opportunities in various scientific fields within the Kingdom and globally.
This paper introduces a novel approach for monitoring and analyzing the evolution of complex geographic objects in satellite image time-series. The method uses a spatiotemporal graph and constraint satisfaction problems (CSP) to model and analyze object changes. Experiments on real-world satellite images from Saudi Arabian cities demonstrate the effectiveness of the proposed approach.
KAUST Professor Marc Genton has been selected as the 2020 Georges Matheron Lecturer of the International Association for Mathematical Geosciences. Genton will present a lecture at the 36th International Geological Congress in Delhi, India, focusing on geostatistics, climate model outputs, and the ExaGeoStat software developed at KAUST. His lecture will cover Matheron's theory of regionalized variables and showcase ExaGeoStat, a high-performance software for geostatistics with exascale computing capability developed at KAUST. Why it matters: This recognition highlights KAUST's contributions to advanced statistical methods and high-performance computing in geosciences, enhancing its international reputation in these fields.
Researchers at MBZUAI, IBM Research, and other institutions have developed EarthDial, a new vision-language model (VLM) specifically designed to process geospatial data from remote sensing technologies. EarthDial handles data in multiple modalities and resolutions, processing images captured at different times to observe environmental changes. The model outperformed others on over 40 tasks including image classification, object detection, and change detection. Why it matters: This unified model bridges the gap between generic VLMs and domain-specific models, enabling complex geospatial data analysis for applications like disaster assessment and climate monitoring in the region.