This paper presents a 2-D convolutional neural network (CNN) approach for damage detection in steel frame structures, using raw acceleration signals as input. The method employs a network of lightweight CNNs, each optimized for a specific element, to enhance accuracy and speed. The proposed framework is validated using the Qatar University Grandstand Simulator (QUGS) benchmark data. Why it matters: The research offers a promising AI-driven solution for real-time structural health monitoring, with potential applications for infrastructure maintenance and safety in the GCC region.
KAUST and Japan's NHK have signed an agreement for NHK to film the Red Sea in 8K high-definition as part of the "Deep Ocean" documentary series. The filming will be coordinated with researchers from KAUST's Red Sea Research Center, covering a range of subjects from coral reefs to deep-sea brine pools. The collaboration, initiated in 2019, was delayed due to the COVID-19 pandemic. Why it matters: This partnership will showcase the Red Sea's unique biodiversity and KAUST's marine research capabilities to a global audience, highlighting Saudi Arabia's focus on environmental research and its Red Sea giga-projects.
KAUST organized an Arabic Sentiment Analysis Challenge where participants developed ML models to classify tweets as positive, negative, or neutral. The competition used the ASAD dataset with 55K tweets for training, 20K for validation, and 20K for final evaluation. The full dataset of 100K labeled tweets has been released for public use.
This paper surveys machine learning approaches using monument pictures for analyzing heritage sites in India. It addresses challenges in the tourism sector, such as the unavailability of trained personnel and the lack of accurate information. The research aims to provide insights for building an automated decision system to modernize the tourism experience for visitors in India.
This paper presents the design and deployment of an autonomous unmanned ground vehicle (UGV) equipped with a robotic arm for urban firefighting. The UGV uses on-board sensors for navigation and a thermal camera for fire source identification, with a custom pump for fire suppression. The system was developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020, where it achieved the highest score among UGV solutions and contributed to winning first place. Why it matters: This demonstrates the potential of autonomous robotics in addressing complex and dangerous real-world challenges like urban firefighting in the GCC region and beyond.
A Carnegie Mellon team (Tartan) presented their approach to rapidly deployable and robust autonomous aerial vehicles at the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The system utilizes common techniques in vision and control, encoding robustness into mission structure through outcome monitoring and recovery strategies. Their system placed fourth in Challenge 2 and seventh in the Grand Challenge, with achievements in balloon popping, block manipulation, and autonomous firefighting. Why it matters: The work highlights strategies for building robust autonomous systems that can operate without central communication or high-precision GPS in challenging real-world environments, directly addressing key needs in the development of field robotics for the Middle East.
This paper presents a UAV-UGV team designed for autonomous firefighting, developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The system uses LiDAR for localization in GNSS-restricted environments and fuses LiDAR and thermal camera data to track fires. Relative navigation enables successful fire extinguishing. Why it matters: This research demonstrates the potential of robotic systems in autonomous firefighting, addressing challenges in dangerous and inaccessible environments, and advancing robotics research within the UAE.
KAUST and Leica Microsystems have renewed their partnership for the KAUST-Leica Center of Excellence for Optical Microscopy. The partnership has supported studies such as 3D-printed microneedles and climate adaptation in date palms, utilizing Leica's LIGHTNING technology. The Center of Excellence also hosts joint technological forums and outreach activities to develop local and regional expertise. Why it matters: The renewal strengthens KAUST's research and education in optical microscopy, fostering collaboration within Saudi Arabia and the broader region to build advanced expertise in this field.
Team NimbRo presented their UGV solution for autonomous wall building and firefighting at the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The robot integrates a wheeled omnidirectional base, a 6 DoF manipulator arm with a magnetic gripper, a storage system, and a water spraying system. It uses 3D LiDAR, RGB, and thermal cameras to perceive the environment, pick up boxes, construct walls, and detect/extinguish fires. Why it matters: The work highlights advancements in autonomous robotics for complex tasks relevant to construction and disaster response in the UAE and globally.
This paper examines the relationship between COVID-19 spread and weather patterns across 89 cities in Saudi Arabia using machine learning. The study uses daily COVID-19 case reports from the Saudi Ministry of Health and historical weather data. The results indicate that temperature and wind speed have the strongest correlation with the spread of COVID-19, with a random forest model achieving the best performance.
This paper introduces a novel two-step method for predicting urban expansion using time-series satellite imagery. The approach combines semantic image segmentation with a CNN-LSTM model to learn temporal features. Experiments on satellite images from Riyadh, Jeddah, and Dammam in Saudi Arabia demonstrate improved performance compared to existing methods based on Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy.
This paper details an autonomous cooperative wall-building system using UAVs developed for Challenge 2 of the MBZIRC 2020 competition. The system employs scanning, RGB-D detection, precise grasping, and multi-UAV coordination to place bricks on a wall. The CTU-UPenn-NYU approach achieved the highest score in the competition by correctly placing the most bricks. Why it matters: This demonstrates advanced capabilities in robotics and autonomous systems relevant for construction and infrastructure development in challenging environments.
This paper presents two robotic systems developed for the MBZIRC 2020 competition, designed for autonomous wall construction. The systems utilize a UGV with 3D LiDAR for precise brick pose estimation and a UAV employing real-time visual servoing. The authors report results from the competition and lab experiments, discussing lessons learned from the autonomous wall-building task. Why it matters: The work highlights advancements in mobile manipulation and autonomous robotics, with potential applications in construction and infrastructure development in the region.
This paper presents a fully autonomous micro aerial vehicle (MAV) developed to pop balloons using onboard sensing and computing. The system was evaluated at the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The MAV successfully popped all five balloons in under two minutes in each of the three competition runs. Why it matters: This demonstrates the potential of autonomous robotics and computer vision for real-world applications in challenging environments.
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 at the University of Maryland have developed an AI model that can identify objects hidden by camouflage by analyzing subtle texture variations. The AI was trained on synthetic data and then tested on real-world images. It successfully detected camouflaged objects with high accuracy, even when the camouflage was very effective. Why it matters: This could have implications for military applications, search and rescue operations, and even wildlife conservation.
Researchers at the University of Maryland have developed an AI system that can identify objects hidden by camouflage. The AI uses a convolutional neural network trained on synthetic data to detect partially occluded objects. The system outperformed existing object detection methods in tests on real-world images. Why it matters: The work demonstrates potential applications of AI in defense, security, and search and rescue operations in the Middle East and elsewhere.
Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.
A KAUST Rapid Research Response Team (R3T) is collaborating with healthcare stakeholders to combat COVID-19. Xin Gao and his Structural and Functional Bioinformatics (SFB) Group are developing an AI-based diagnosis pipeline from CT scans of COVID-19 patients. The AI pipeline aims to address the high false negative rates associated with nucleic acid detection. Why it matters: This research could improve COVID-19 diagnostics and potentially inform understanding of viral pathogenesis.
KAUST Ph.D. student Anna Fruehstueck won a 2020 Facebook Fellowship award, a two-year fellowship from Facebook Research, focusing on computer graphics. She was selected as one of 36 recipients from over 1,800 applicants and is the University's first recipient of the scholarship. Her research explores the intersection of computer graphics, geometry processing, and visual arts using machine learning. Why it matters: This award highlights the growing prominence of KAUST in computer science research and its ability to attract and foster top talent in the field.
MIT researchers have developed a new AI system that uses satellite imagery and street-level photos to add details to digital maps. The AI model can identify features like building footprints, road networks, and vegetation cover with high accuracy. It then enriches existing maps by adding these features, improving their usability for navigation and urban planning. Why it matters: This technology can significantly enhance the quality and detail of digital maps, particularly in areas where up-to-date map data is lacking, enabling better AI-powered applications.
KAUST Ph.D. student Jinhui Xiong won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization in Germany for his paper "Stochastic Convolutional Sparse Coding". The paper, co-authored with KAUST Professors Peter Richtárik and Wolfgang Heidrich, introduces a novel stochastic spatial-domain solver for Convolutional Sparse Coding (CSC). The proposed algorithm outperforms state-of-the-art solutions in terms of execution time and offers an improved representation for learning dictionaries from sample images. Why it matters: This award recognizes significant research in efficient image representation and dictionary learning, contributing to advancements in visual computing and AI at KAUST.
Abdullah Hamdi, a Ph.D. student at KAUST, is researching AI, deep learning, and computer vision in the Image and Video Understanding Lab under Associate Professor Bernard Ghanem. His work focuses on developing reliable testing methods for deep learning tools, particularly for sensitive applications like self-driving vehicles. Hamdi aims to disseminate AI knowledge and contribute to the AI ecosystem in the region. Why it matters: This highlights KAUST's role in fostering local AI talent and contributing to critical research areas like autonomous vehicles, aligning with Saudi Arabia's broader technology goals.
KAUST researchers collaborated with the Blue Brain Project to study astrocytes, brain cells crucial for memory and learning. Dr. Corrado Calì produced 3D models of astrocytes using serial block-face electron microscopy to understand their structure. The study, published in Progress in Neurobiology, reveals how lactate transfer from astrocytes to neurons contributes to brain energy usage. Why it matters: Understanding astrocyte function could lead to new drugs for treating conditions like stroke and Alzheimer's disease by improving brain cell function.
Researchers at MIT and QCRI developed Mapster, a human-in-the-loop street map editing system. Mapster incorporates high-precision automatic map inference, data refinement, and machine-assisted map editing. Evaluation across forty cities using satellite imagery, GPS trajectories, and ground-truth data demonstrates Mapster's ability to make automation practical for map editing. Why it matters: This system could significantly improve the accuracy and completeness of street maps in rapidly developing urban areas across the Middle East.
KAUST alumnus Ronell Sicat (M.S. '10, Ph.D. '15) is developing immersive data visualization tools using augmented and virtual reality (AR/VR). After a postdoc at Harvard, Sicat returned to KAUST as a research scientist in the Visual Computing Center. Sicat developed a tool called DXR to help researchers prototype immersive visualizations. Why it matters: This highlights KAUST's role in fostering talent and innovation in AR/VR, a growing area with applications across various sectors in Saudi Arabia.
KAUST Ph.D. student Adel Bibi is researching how to bridge the gap between theory and practice in deep learning, focusing on the mathematical understanding of deep learning models. Bibi is currently interning at Intel in Munich and previously worked on various computer vision problems. He aims to use optimization and mathematics to better understand deep learning models and build better models systematically from theory. Why it matters: This research contributes to the fundamental understanding of deep learning, potentially leading to more efficient and reliable AI systems developed in the region.
KAUST Ph.D. student Matthias Müller won the Best Paper/Presentation Award at the 2nd International Workshop on Computer Vision for Unmanned Aerial Vehicles for his paper on teaching UAVs to navigate a racecourse autonomously. The paper, "Teaching UAVs to Race: End to End Regression of Agile Controls in Simulation," details research on training a deep neural network to predict UAV controls from raw image data. The research uses imitation learning with data augmentation to allow for correction of navigation mistakes, outperforming state-of-the-art methods. Why it matters: This award recognizes KAUST's contributions to computer vision and autonomous drone navigation, important areas for future applications in logistics, surveillance, and environmental monitoring in the region.
KAUST researchers in the Image and Video Understanding Lab are applying machine learning to computer vision for automated navigation, including self-driving cars and UAVs. They tested their algorithms on KAUST roads, aiming to replicate the brain's efficiency in tasks like activity and object recognition. The team is also exploring the possibility of creative algorithms that can transfer skills without direct training. Why it matters: This research contributes to the advancement of autonomous systems and explores the fundamental questions of replicating human intelligence in machines within the GCC region.
The article discusses Team NimbRo's approaches to challenges involving micro aerial vehicles (MAV) at the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017. The challenges included landing on a moving vehicle and a treasure hunt task requiring mission planning and multi-robot coordination. The team's system achieved a third place in both subchallenges and contributed to winning the MBZIRC Grand Challenge. Why it matters: This demonstrates advanced robotics capabilities developed and tested in the UAE, pushing the boundaries of autonomous aerial vehicle operation and multi-robot collaboration.
KAUST's Image and Video Understanding Lab is developing machine learning algorithms for computer vision and object tracking, with applications in video content search and UAV navigation. Their algorithms can detect specific activities in videos, helping platforms detect unwanted content and deliver relevant ads. The object tracking algorithm is also used to empower UAVs, enabling them to follow objects autonomously. Why it matters: This research enhances video content analysis and UAV capabilities, positioning KAUST as a leader in computer vision and AI applications within the region.
Team NimbRo's robot Mario won the MBZIRC 2017 Challenge 2 by autonomously manipulating a valve stem using a wrench. The robot uses an omnidirectional base for locomotion and a 3D laser scan detector to find the manipulation panel. A deep neural network detects and selects the correct tool from grayscale images, and motion primitives are adapted to turn the valve stem. Why it matters: This work demonstrates advanced robotic manipulation capabilities relevant for industrial automation and hazardous environment operations in the region.
KAUST's Visual Computing Center (VCC) hosted an Open House event on March 28, showcasing its interdisciplinary research in visual computing. Demonstrations included a virtual reality driving simulator by FalconViz, intended for driver education in Saudi Arabia. Researchers also presented a drone trained to autonomously navigate race courses and a neural network for autonomous driving using image-based technology without GPS. Why it matters: The VCC's work highlights KAUST's role in advancing visual computing applications relevant to Saudi Arabia, from driver training to autonomous systems.
KAUST has appointed Dr. Tony Chan, former president of HKUST and KAUST board member, as its new president, effective September 1, 2018. Chan's background is in computational mathematics with interests including image processing, computer vision, physical circuit design and computational brain mapping. He has been a close partner of KAUST since 2007 and a member of the KAUST Board of Trustees since 2011. Why it matters: Chan's leadership is expected to contribute to KAUST's role in achieving Saudi Arabia's Vision 2030 objectives and addressing global challenges.
KAUST Ph.D. student Zhaolun Liu won the best student presentation at the 2017 Society of Exploration Geophysicists (SEG) Full-Waveform Inversion (FWI) and Beyond Workshop in Beijing. Liu's presentation was on "3D Wave-Equation Dispersion Inversion of Surface Waves," based on a paper co-authored with Jing Li and Professor Gerard Schuster. The paper describes a new method called wave equation dispersion inversion (WD) for inverting surface waves. Why it matters: This award recognizes KAUST's contributions to geophysics and seismic imaging, highlighting the university's research capabilities and access to high-performance computing.
KAUST held a research workshop on Optimization and Big Data, gathering researchers to discuss challenges and opportunities in the field. Speakers presented novel optimization algorithms and distributed systems for handling large datasets. The workshop featured 20 speakers from KAUST, global universities, and Microsoft Research. Why it matters: The event highlights KAUST's role as a regional hub for advancing research and development in big data and optimization, crucial for AI and various computational fields.
Researchers present RUR53, an unmanned ground vehicle (UGV) capable of autonomous navigation, object recognition, and tool manipulation. The UGV uses a modular software architecture, enabling it to perform complex tasks like detecting panels, docking, and manipulating tools such as wrenches and valve stems. RUR53 was tested at the 2017 Mohamed Bin Zayed International Robotics Challenge where it ranked third in the Grand Challenge as part of a collaboration. Why it matters: This research demonstrates advanced robotics capabilities applicable to various industrial and inspection tasks, highlighting the UAE's focus on robotics innovation.
Dr. Andrew Bastawrous, CEO/co-founder of Peek, discussed his work on mobile eye clinics at KAUST. He developed Peek Acuity and Peek Retina, which turn smartphones into tools for detecting visual impairment. The technology uses smartphone screens and camera clip-ons to image inside the eye. Why it matters: This low-cost mobile ophthalmic tool has the potential to prevent and treat vision loss in underserved communities.
The paper details the hardware and software systems of ETH Zurich's Micro Aerial Vehicles (MAVs) used in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The team integrated computer vision, sensor fusion, and control to develop autonomous outdoor platforms. They achieved second place in Challenge 3 and the Grand Challenge, demonstrating autonomous landing in under a minute and a 90%+ visual servoing success rate for object pickups. Why it matters: The work highlights the advanced state of robotics research and development showcased at the MBZIRC, contributing to the growth of autonomous systems in the region.
This paper presents a decentralized multi-agent unmanned aerial system designed for search, pickup, and relocation of objects. The system integrates multi-agent aerial exploration, object detection/tracking, and aerial gripping. The decentralized system uses global state estimation, reactive collision avoidance, and sweep planning for exploration. Why it matters: The system's successful deployment in demonstrations and competitions like MBZIRC highlights the potential of integrated robotic solutions for complex tasks such as search and rescue in the region.
The KAUST Visual Computing (KAUST RC-VC) – Modeling and Reconstruction conference featured speakers from Simon Fraser University, Caltech, Cornell University, and Autodesk. Presentations covered topics like networking topology, shape matching and modeling, data-driven interpolation of optical properties, and computer graphics. Why it matters: The conference highlights KAUST's role in fostering international collaboration and advancing research in visual computing and related fields within Saudi Arabia.
KAUST researchers have determined the atomic 3D structure of a key protein involved in plant stress signaling using X-ray crystallography at the SOLEIL synchrotron in France. Postdoctoral fellow Umar Farook Shahul Hameed optimized a tiny crystal of the plant enzyme for over six months. The team used the EIGER 9M detector to capture the weak diffraction pattern from the crystal. Why it matters: Understanding the interactions between proteins that communicate plant stress could lead to engineering more stress-tolerant crops, enhancing food security.
KAUST's Visual Computing Center (VCC) is researching computer vision, image processing, and machine learning, with applications in self-driving cars, surveillance, and security. Professor Bernard Ghanem is working on teaching machines to understand visual data semantically, similar to how humans perceive the world. Self-driving cars use visual sensors to interpret traffic signals and detect obstacles, while computer vision also assists governments and corporations with security applications like facial recognition and detecting unattended luggage. Why it matters: Advancements in computer vision at KAUST can contribute to innovations in autonomous vehicles and enhance security measures in the region.
KAUST's Peter Wonka discusses the challenges and advancements in creating data-rich, three-dimensional maps for various applications. His team is working with Boeing on 3D modeling tools for aerospace design. KAUST-funded FalconViz uses UAV drones to create 3D maps of disaster areas for first responders. Why it matters: This highlights KAUST's contribution to cutting-edge 3D modeling and its practical applications in industries like aerospace and disaster response in the region.
KAUST held its first Enrichment in the Spring (SEP) program from March 20–28, focusing on the human brain and mind, coinciding with Brain Awareness Week. The program featured lectures from neuroscientists like Professor Alim-Louis Benabid, and presentations by KAUST's Ali Awami and Corrado Cali on visualization technology for studying the brain. KAUST researchers are collaborating with the Human Brain Project and Harvard University to develop comprehensive brain models and visualize connectome data. Why it matters: This initiative highlights KAUST's commitment to advancing neuroscience research and fostering interdisciplinary collaborations to understand the complexities of the human brain.
A team from KAUST's Earth Science and Engineering program visited the site of the ongoing volcanic eruption in Iceland, which began in August 2014. Researchers monitored ground movements related to a collapsing structure near the eruption site using GPS instruments to measure vertical ground displacements. They aim to compare these measurements with satellite radar data to quantify volume changes before, during, and after the eruption. Why it matters: This study exemplifies the application of KAUST's earth science expertise to understanding and monitoring significant geological events, contributing to hazard assessment and risk management in volcanically active regions.
KAUST Professor Wolfgang Heidrich received the Humboldt Research Award for his work in computational photography and displays. The award includes €60,000 and a research stay in Germany, hosted by the Max-Planck Institute for Informatics and the Cluster of Excellence on "Multimodal Computing and Interaction" at Saarland University. Heidrich plans to spend six months in Germany over the next three years, networking with faculty and collaborating on research projects. Why it matters: This award highlights KAUST's growing prominence in computer science and fosters international collaboration in cutting-edge areas like computational photography.
Researchers from Alexandria University introduce AlexU-Word, a new dataset for offline Arabic handwriting recognition. The dataset contains 25,114 samples of 109 unique Arabic words, covering all letter shapes, collected from 907 writers. The dataset is designed for closed-vocabulary word recognition and to support segmented letter recognition-based systems. Why it matters: This dataset can help advance Arabic handwriting recognition systems, addressing a need for high-quality Arabic datasets in NLP research.
This paper introduces a novel dataset for Arabic handwritten isolated alphabet letters to serve as a benchmark for future research. The study presents a comparative evaluation of window-based descriptors for Arabic handwritten alphabet recognition, testing different descriptors with various classifiers. The experiments demonstrate that window-based descriptors perform well, especially when combined with a novel spatial pyramid partitioning scheme. Why it matters: The new dataset and analysis of descriptors will help advance Arabic OCR and handwritten text recognition systems.
KACST and KAUST are collaborating on two research projects using KACST's Saker 4 drone. The first project prototypes a UAV-based flash flood monitoring system using disposable microsensors. The second augments UAV navigation with vision to improve takeoff and landing via a new runway detection algorithm. Why it matters: This collaboration showcases the growing sophistication of Saudi Arabia's indigenous drone capabilities for environmental monitoring and advanced navigation research.