Decentralized Decision-Making in Counterswarm Engagements Deep Learning Methods for Decentralized Decision-Making in Counterswarm Engagements is an open-source Naval Postgraduate School thesis by Kurdo A. Sharif. The rise of unmanned technologies has fueled interdisciplinary research into robotic swarm systems, particularly within military applications. These systems draw inspiration from the problem-solving capabilities of biological swarms, offering the benefit of emergent global behavior that arises from local interactions and minimizes the need for centralized control. Traditional methods for creating emergent behavior in robotic swarms depend on predictable and controllable swarm dynamics, clearly defined local rules, and complete knowledge of all agents. https://hubs.la/Q02PPNtk0

👋 Hello LinkedIn connections! After a brief hiatus, I'm thrilled to share some exciting news! 🌟 I recently completed the "AI/ML for Geodata Analysis" course offered by the Indian Institute of Remote Sensing (IIRS), under the umbrella of the Indian Space Research Organization (ISRO)! 🚀 This 5-day intensive journey was packed with incredible insights and hands-on learning: 📖 Introduction to AI, ML, and DL: Laying a solid foundation in these transformative technologies. 🤖 Deep Dives into Learning Methods: Exploring supervised, unsupervised, and reinforcement learning. 🧠 Cutting-Edge Deep Learning Models: Understanding CNN, RNN, SSD, YOLO, and more. 🛰️ Spaceborne Lidar Systems & Earth Engine: Leveraging these for advanced geospatial analysis. 🐍 Python for ML/DL Models: Practical coding sessions for real-world applications. A huge thank you to ISRO and IIRS for crafting such a comprehensive and impactful course. The dedication of the educators and the quality of the content truly exceeded my expectations! 🙏 😇 I’m excited to apply these new skills to future projects and continue exploring the intersection of AI and geospatial analysis. If you’re passionate about these fields too, let’s connect and share our insights! 🤝 #GeospatialAnalysis #ArtificialIntelligence #MachineLearning #DeepLearning #SpaceTech #RemoteSensing #Python #DataScience #ISRO #IIRS #ContinuousLearning

We’ve been working on a way to use deep learning to improve the monitoring and #conservation of natural #ecosystems With a team of computer scientists at Wageningen University & Research (Aneesh Chauhan & Freek Daniels), remote sensing specialists in Indonesia (Pramaditya Wicaksono, Muhammad Hafizt, and Setiawan Djody Harahap) and coastal ecologists (Marjolijn J. A. Christianen Christianen and I), we used deep learning to automatically analyse aerial drone imagery to look at spatial patterns within #seagrass meadows and the distribution of grazing #turtles. By comparing the patterns over a large area and across years, we were able to identify changes in the spatial patterning of a tropical seagrass ecosystem and used this to infer the #resilience of the meadow. More can be read here: https://lnkd.in/dn7nn_cX And to make sure others can do the same and further develop the method, we provide all the scripts in an #open-access format so anyone can try it with their own system! Find the scripts here: https://lnkd.in/dxst8pAY

Exciting Research Announcement! We're thrilled to announce the publication of our latest research paper, "Flood-ResNet50: Optimized Deep Learning Model for Efficient Flood Detection on Edge Device," featured in the 2023 International Conference on Machine Learning and Applications (ICMLA). Floods are among the most devastating natural disasters, causing significant economic losses and endangering countless lives worldwide. To address this pressing issue, Azim Khan and team have developed Flood-ResNet50, a groundbreaking deep learning model designed for efficient flood detection in UAV imagery. By leveraging state-of-the-art technology and innovative methodologies, Flood-ResNet50 accurately classifies flooded and non-flooded areas with unparalleled precision. Through extensive experimentation, our model achieves remarkable performance metrics, including a classification accuracy of 96.43%, F1 score of 86.36%, Recall of 81.11%, and Precision of 92.41%. Moreover, Flood-ResNet50 boasts a compact model size of 98MB and FLOPs of 4.3 billion, ensuring efficient deployment on edge devices like the Jetson Nano. Incorporating additional layers and transfer learning techniques, Flood-ResNet50 surpasses the capabilities of larger models such as VGG16/19, AlexNet, DenseNet161, and vision transformers. This enhanced efficiency makes Flood-ResNet50 an invaluable tool for real-time flood monitoring and disaster response efforts. The global impact of floods underscores the urgency of efficient detection methods. By harnessing the power of AI and UAV technology, Flood-ResNet50 offers a transformative solution to mitigate the devastating effects of floods and safeguard lives and livelihoods. Paper : https://lnkd.in/drFfiR8w CARDS Research: https://cards.umbc.edu/ Published by : Azim Khan, a UMBC PhD student and Research Assistant at CARDS ( Center for Real-time Distributed Sensing and Autonomy).

“Building Curious Machines”, an incredible Machine Learning usecase undertaken by University of Michigan. Do you know that our seafloor covers 139 Million square miles. The MH370 search in a remote and previously unexplored stretch of ocean took 1,046 days and cost more than $129 million to scan 0.03% of the ocean, 46,000 square miles—larger than the state of Pennsylvania. While the search didn’t locate MH370, it did stumble across a lot of valuable information. It revealed a previously unknown underwater landscape of ridges, volcanoes, valleys, shifting tectonic plates and massive landslides. It also showed seamounts—underwater mountains that attract vast quantities of fish and other wildlife—and two previously undiscovered 19th century shipwrecks. The data will be useful to geologists, climate modelers, archaeologists and fishers for decades to come. University of Michigan is combining robotics, naval architecture and computer science building a software system that can trawl through sonar data much as a human would. “In the broadest sense, training a machine learning system to search for shipwrecks or planewrecks is similar to training an autonomous car to safely drive on the streets—makers train both models by showing them images of things they’re likely to encounter out in the real world.” - quoting the author Seabed 2030 project, an initiative that aims to map the entire ocean floor by 2030. Seabed 2030 has more than doubled the amount of seafloor that has been mapped from less than 10% in 2017 to over 20% today. https://lnkd.in/gQY6Rbrv

Harnessing #Deep #Learning in #GIS through the #ESRI #MOOC! Recently, I had the opportunity to deepen my knowledge of deep learning in #GIS through an amazing #ESRI #MOOC. This course provided hands-on experience with cutting-edge technology that is transforming spatial analysis. 💻 What is Deep Learning? #Deep #learning is a type of machine learning that leverages multiple layers of nonlinear processing to identify patterns and features in data. In the context of #ArcGISPro, deep learning can be used for advanced analyses, such as object detection, which automates the identification of specific features within imagery—saving time and effort from manual data collection. 🏊 My Project: As part of the #MOOC, I applied deep learning tools to detect all the swimming pools in a specific area of Los Angeles! The ability to train a model for #object #detection is invaluable for tasks like urban planning, infrastructure monitoring, and environmental management. This experience has really shown me the potential for deep learning to revolutionize how we interact with geospatial data. 🔍 Why This Matters: By automating object detection, #deep #learning helps professionals across industries make more informed decisions faster and with greater accuracy. I’m excited about the future of #GIS and the role that deep learning will continue to play in it! Thanks to #ESRI for #offering such a powerful learning experience, and I look forward to applying these skills to future #projects! #DeepLearning #ArcGIS #MachineLearning #ESRI #MOOC #ObjectDetection #GIS #UrbanPlanning #SpatialAnalysis #Innovation

🚀 New learning experience in AI/ML for Geodata Analysis with ISRO! 🚀 I’m excited to announce that I’ve successfully completed ISRO 144th Course on AI/ML for Geodata Analysis , conducted by the Indian Institute of Remote Sensing (IIRS). This comprehensive program has provided me with a deep understanding of the latest advancements in AI and ML applied to geospatial data. The course covered a range of fascinating topics, including: ✨ The fundamentals of AI/ML and Deep Learning ✨ Various Machine Learning methods, including Supervised, Unsupervised, and Reinforcement Learning ✨ Deep Learning techniques such as CNN, RNN, R-CNN, Faster RCNN, SSD, YOLO, and their use in Spaceborne Lidar Systems ✨ Utilizing Google Earth Engine for Machine Learning applications ✨ Python for developing Machine/Deep Learning models What truly stood out was the opportunity to learn from renowned experts like Dr. Hina Pande, Dr. Poonam Seth Tiwari, Dr. Kamal Pandey, and Shri Ravi Bhandari. Their exceptional knowledge and guidance have been immensely valuable. A heartfelt thank you to the Department Of Space 🚀, Government of India (GoI),Indian Institute of Remote Sensing (IIRS), Indian Space Research Organization (ISRO) and the brilliant faculty for this enriching experience! Your dedication and hard work have made this learning journey truly memorable. Thank you once again to everyone involved for making this learning journey so impactful and inspiring! #ISRO #AI #MachineLearning #DeepLearning #Geospatial #python #IIRS #GeodataAnalysis #ContinuousLearning #SpaceTech #IIRS #ML #Python #RemoteSensing #SpaceEducation #CareerDevelopment #DataAnalysis #SpaceScience #SpaceExploration

🎓 Excited to share that I've successfully completed the IIrs ISRO program in AI & ML! 🚀 This journey has deepened my understanding of artificial intelligence and machine learning, and I am eager to apply the knowledge in real-world projects. A big thank you to the incredible mentors and peers who made this experience truly enriching. Looking forward to the opportunities ahead! 💡 #AI #MachineLearning #ISRO #Tech #Innovation #AIforGood #Learning #Growth

I'm thrilled to have successfully completed the "AI/ML for Geodata Analysis" course offered by Indian Institute of Remote Sensing (IIRS), Indian Space Research Organization (ISRO) . This comprehensive course has deepened my understanding of AI, machine learning, and deep learning, specifically in the context of geospatial data processing. Key learnings from this course include: 1) In-depth knowledge of AI, ML, and DL concepts 2)Hands-on experience with Machine Learning methods: Supervised, Unsupervised, and Reinforcement Learning 3)Understanding advanced Deep Learning models like CNN, RNN, R-CNN, YOLO, and their applications in geospatial analysis 4)Applying machine learning techniques using Google Earth Engine 5)Practical implementation using Python A big thank you to the instructors and the entire team at IIRS for this opportunity. Looking forward to applying these skills to real-world geospatial challenges! #AI #MachineLearning #DeepLearning #GeospatialAnalysis #Geodata #Python #RemoteSensing #ISRO #IIRS