Communications Physics journal has a focus collection on space quantum communications. The collection covers supporting technologies, new quantum protocols, inter-satellite QKD, constellations of satellites, and quantum inspired technologies and protocols for space based communication. Contributions are welcome from October 20, 2020 to April 30, 2021, and accepted papers are published on a rolling basis. Why it matters: Space-based quantum communication is a critical area for developing secure, global quantum networks, and this collection could highlight relevant research for the GCC region as it invests in advanced technologies.
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's Hydrology and Land Observation (Halo) lab, led by Matthew McCabe, is using drones and satellites to monitor agricultural water usage in Saudi Arabia. They employ thermal cameras, sensors, and imagery from CubeSats to map crop types, health, and water stress. The team uses machine learning and AI to analyze the images, aiming to promote sustainable water management. Why it matters: This research addresses critical water scarcity issues in the region by providing data-driven insights for more efficient agricultural practices.
Researchers from MBZUAI, IBM, and ServiceNow introduced GEOBench-VLM, a benchmark for evaluating vision-language models on Earth observation tasks using satellite and aerial imagery. The benchmark includes over 10,000 human-verified instructions across 31 sub-tasks spanning object classification, localization, change detection, and more. GEOBench-VLM addresses the gap in current VLMs' ability to perform spatially grounded reasoning and change detection in satellite imagery. Why it matters: This benchmark will drive progress in AI's ability to analyze satellite data for critical applications like disaster response, climate monitoring, and urban planning in the Middle East and globally.
The paper proposes a method for causal inference using satellite image time series to determine the impact of interventions on climate change, focusing on quantifying deforestation due to human causes. The method uses computer vision and deep learning to detect forest tree coverage levels over time and Bayesian structural causal models to estimate counterfactuals. The framework is applied to analyze deforestation levels before and after the hyperinflation event in Brazil in the Amazon rainforest region.
KAUST, in partnership with Spire Global, has successfully launched a Cubesat satellite on the SpaceX Transporter-7 mission. The satellite is equipped with a hyperspectral camera and GNSS-R sensor to collect high-resolution data on Earth's ecosystems. The collected data will help Saudi Arabia observe and characterize its natural resources, especially in terrestrial, coastal, and ocean environments. Why it matters: The satellite launch demonstrates KAUST's commitment to advancing Vision 2030 goals related to environmental protection and provides a valuable resource for scientists and collaborators to address local and regional environmental questions.
KAUST's Winter Enrichment Program (WEP) 2015 features keynotes by international experts and award winners. Week 2 events include Caltech's Anthony Readhead discussing radio astronomy and Saudi Arabia's potential role, and an exhibition of Tingatinga art from East Africa. Other events cover urban science, polar expeditions, and a multimedia performance called BELLA GAIA. Why it matters: WEP promotes scientific engagement and cultural exchange within KAUST and highlights opportunities for Saudi Arabia in global research fields like radio astronomy.