FlySight’s Post

GEOINT is defined as any intelligence relating to human activity on and around our planet, obtained by associating gathered data with an Earth-referenced location. For decades, GEOINT from space was the preserve of a limited number of superpower-operated assets using optical imagery, Synthetic Aperture Radar (SAR), and limited forms of RF signals intelligence (SIGINT). Generally, however, most RF intelligence was still gathered from air or ground-based assets. These – often manned – platforms are time-constrained and closer to theatre, with the associated vulnerabilities. With the growth of unmanned aerial systems (UAS), overhead persistence is increased, and risks are reduced, but UAS still have their operational limitations. Two major factors are triggering a new and broad expansion of space-based RF GEOINT assets. Firstly, the demand for such information has grown exponentially; from big tech to finance to science, there is huge commercial demand. On the military frontier, ongoing conflicts are demonstrating the advantage provided by ubiquitous RF GEOINT, and at a government & infrastructure level the data gathered is invaluable in tackling smuggling, piracy and maintaining border security. Secondly, technology has advanced to the extent that the satellites themselves are smaller, cheaper to buy, easier to launch, and more accessible than ever before; no longer limited to governments of the most powerful countries. The processing of data, with more powerful computing and useful AI/ML capabilities now enables the interpretation of data in ways that were impossible only a decade ago. All this leads us to a boom in space-based RF intelligence payloads. MPG have already provided our tunable filter technologies for on-orbit ELINT front ends, and our latest EW receiver solutions have the potential to facilitate sensitive broadband observations in small and efficient ELINT payload. MPG have developed a compact 0.5 GHz – 18.0 GHz tuner/converter with maximum probability of intercept in mind; holding up to the demands of airborne RESM and complex ELINT needs alike, whilst fitting within a 3U form factor and using a SOSA-aligned Open VPX interface. With a 1 GHz instantaneous bandwidth, the unit incorporates MPG’s proprietary tuneable filter technology into a novel LO architecture driven by DDS, resulting in a SFDR of >50 dB and excellent phase noise performance, combined with <10 us tuning to a 1 MHz resolution. The 1.7 GHz IF is also bandwidth-selectable down to 250 MHz. Whether your use case is tracking AIS-dark shipping via comms interception; locating & identifying RADAR; or homing in and attribution of spoofing & jamming emitters, MPG can provide leading performance for your ELINT system. Learn more about our EW solutions here: https://ow.ly/f2IA50SVQ7u

ISR operations are based on concepts founded over 20 years ago, namely IRM & CM. If the battle space has changed in those years, why hasn't ISR Ops adapted? Adapting IRM&CM to contemporary warfare demands a multifaceted approach. Here are key strategies: Real-Time Adjustments: Implement dynamic tasking and adaptive collection plans to swiftly adjust to new intelligence. For instance, real-time updates allow UAVs to track detected enemy movements instantly. Advanced Technologies: Utilise AI and automation for rapid data processing and decision-making. AI-driven analytics can flag unusual activity in real-time, while automated tasking systems re-direct ISR assets based on real-time data. Decentralised ISR Management: Empower field units and establish forward-deployed ISR cells for quick decisions. Field commanders can make immediate ISR tasking decisions, and ISR cells at brigade levels can rapidly interpret intelligence. Improved Communication: Enhance secure communication networks and integrate C2 systems for real-time coordination. Secure, high-bandwidth links ensure rapid information sharing, while systems like DCGS provide a comprehensive operational picture. Agile ISR Assets: Deploy rapidly adaptable UAVs and multi-role platforms. Portable UAVs can be quickly launched and re-deployed, and multi-role platforms can adapt to various missions without extensive reconfiguration. Continuous Training: Conduct training and simulations to refine dynamic IRM&CM processes. Realistic scenarios and virtual simulations help ISR teams practice rapid responses to changing battlefield conditions. These strategies enhance ISR operations, ensuring timely intelligence and responsiveness in dynamic environments.

#PELCANresearch | The presence of #macroplastic (MP) is having serious consequences on natural ecosystems, directly affecting biota and human wellbeing. Given this scenario, estimating MPs’ abundance is crucial for assessing the issue and formulating effective waste management strategies. 🗑️♻️ In this context, the main objective of this critical review is to analyze the use of #MachineLearning techniques, with a particular interest in #DeepLearning approaches, to detect, classify and quantify MPs in aquatic environments, supported by datasets such as satellite or aerial images and video recordings taken by unmanned aerial vehicles. 📷 This article provides a concise overview of artificial intelligence concepts, followed by a bibliometric analysis and a critical review. 👨🏻💻 🔗Learn more: https://lnkd.in/e9Jxuetc

𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐲𝐨𝐮 𝐞𝐯𝐞𝐫 𝐰𝐚𝐧𝐭𝐞𝐝 𝐭𝐨 𝐤𝐧𝐨𝐰 𝐚𝐛𝐨𝐮𝐭 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐧𝐠 𝐀𝐞𝐫𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐔𝐬𝐢𝐧𝐠 𝐚 𝐃𝐫𝐨𝐧𝐞 .. 𝐛𝐮𝐭 𝐰𝐞𝐫𝐞 𝐚𝐟𝐫𝐚𝐢𝐝 𝐭𝐨 𝐚𝐬𝐤! I thought this a really interesting talk on drones and data collection. The presenter Justin Roberts is a passionate GIS/mapping guy. Using his words introducing this video: "If you want to use a drone to collect aerial data or gain a new perspective on the world, I’ll give you a roadmap of where to start, what data you can collect, how to conduct flights, how to process the data, and how to have fun through the whole process." Watch the full presentation here: https://lnkd.in/gjKA-9z7 𝐓𝐞𝐥𝐥𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐫𝐞𝐦𝐨𝐭𝐞 𝐬𝐞𝐧𝐬𝐢𝐧𝐠 𝐬𝐭𝐨𝐫𝐲 We are experts are telling geospatial solution stories. Now leveraging generative AI we work with sales and marketing teams to help power growth. Find out more by signing up for a free consultation: https://lnkd.in/gYhrCF-f Download the eBook "𝐘𝐨𝐮𝐫 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐒𝐚𝐥𝐞𝐬 & 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐆𝐞𝐧𝐀𝐈": https://lnkd.in/ghJ457MU #spatial #geospatial #drone #sales #marketing #growth

Combining Visible and Infrared Spectrum Imagery using Machine Learning for Small Unmanned Aerial System Detection is a research paper by Vinicius G. Goecks, Grayson Woods, and John Valasek of Texas A&M University. Compared to the readily accessible visible spectrum sensors, LWIR sensors exhibit lower resolution and may generate increased false positives when subjected to heat sources like birds. This research suggests a solution by combining the strengths of LWIR and visible spectrum sensors through machine learning for the vision-based detection of small unmanned aerial systems (sUAS) https://hubs.la/Q02nGNvH0

𝐇𝐚𝐫𝐧𝐞𝐬𝐬𝐢𝐧𝐠 𝐀𝐈 𝐚𝐧𝐝 𝐔𝐀𝐕𝐬 𝐟𝐨𝐫 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐖𝐢𝐥𝐝𝐟𝐢𝐫𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 Advanced algorithms are transforming Active-Fire Management, particularly through the integration of AI-enabled unmanned aerial systems (UAVs). The article I recently encountered categorizes these algorithms into 𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐀𝐠𝐞𝐧𝐭-𝐁𝐚𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠. Supervised Learning includes classification and regression algorithms like Naïve Bayes, Decision Trees, and Artificial Neural Networks, which are instrumental in tasks such as fire detection, severity assessment, and fuel analysis. Unsupervised Learning techniques, such as K-Means and Principal Component Analysis, help discover hidden patterns in wildfire data, facilitating smoke modeling and hotspot detection. Agent-Based Learning algorithms, including Reinforcement Learning and Genetic Algorithms, are crucial for decision-making and optimization tasks, enhancing fire control, resource allocation, and evacuation planning. 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐑𝐋) is revolutionizing Active-Fire Management, particularly when integrated with unmanned aerial systems (UAVs). The article highlights the role of RL within Agent-Based Learning, showcasing its critical importance in decision-making and optimization tasks for wildfire control. RL algorithms, such as Q-Learning, Deep Q-Networks, and Actor Critic Methods, are pivotal in enhancing fire control, resource allocation, and evacuation planning. By enabling intelligent agents to learn from their environment and make real-time decisions, RL significantly improves the efficiency and effectiveness of wildfire management strategies. The combination of RL and UAV technology marks a significant advancement in the field. AI-driven UAV systems, equipped with RL capabilities, enable real-time fire detection, monitoring, and post-fire recovery, offering unprecedented precision and adaptability. Leveraging these advanced technologies allows for better prediction, monitoring, and control of wildfires, ultimately leading to improved protection of lives, property, and ecosystems. The comprehensive application of these algorithms and UAVs represents a promising future for more effective and efficient wildfire management strategies. Reference: https://lnkd.in/gU8A-9c5 #WildfireManagement #AI #UAV #MachineLearning #DataScience #FireDetection #FireControl #EmergencyManagement #Innovation #Technology

✈️ 2024 is the year uncrewed aviation operations take off! With a growing number of drones sharing the skies, trust and collaboration will be critical – as InterUSS Platform Program Manager Chris Clark emphasised in his recent interview with Airspace Magazine.   The InterUSS Platform is leading the charge in developing open-source solutions for strategic conflict detection. This approach allows uncrewed operations to safely share airspace by enabling them to detect and avoid each other in real time 🚨   Our platform provides a suite of open-source tools, including #automatedtesting that's already being used by operators in the busy North Texas market. These tools not only ensure safe operations but also empower public authorities with a reliable system for monitoring and compliance.   🔗 Read the full article here: https://lnkd.in/evWNaUjZ   Thanks, CANSO! #UTM #conflictdetection #uncrewedaviation #drones

🌲🫴 TREEADS presents Artificial Intelligence for mission planning, an innovative wildfire solution showcased at the TREEADS Knowledge Marketplace Repository: https://lnkd.in/dDBWURQM . ✅ This cutting-edge software solution is intended to simplify and maximize the planning and carrying out of UAV missions. 🔥 Building upon the widely recognized #DARP (Divide Areas Algorithm for Optimal Multi-Robot Coverage Path Planning) algorithm and utilizing artificial intelligence, this module expands the capabilities of UAV operators and provides notable benefits in terms of efficiency, safety, and adaptability. 🚀 The dynamic TREEADS Knowledge Marketplace Repository is a platform that serves as a comprehensive resource center for wildfire #prevention, #preparedness, #detection, #response, #restoration, and #adaptation strategies as well as a promotional channel for innovative wildfire solutions and capabilities developed during the TREEADS project. ✍️ Register now: https://lnkd.in/d6Ni7uKP #TREEADS #AI #WildfireManagement #EnvironmentalResilience #Innovation

Published! 🎉 Realization of vision-based drone navigation in GNSS-denied situations 🛰 Check it out to explore how we combine the power of AI with classical methods to navigate drones in environments without GNSS. Using deep learning techniques to extract high-level features reduces the image-based localization problem to a pattern-matching problem. Flight experiments demonstrate the effectiveness of the proposed system in real-world environments.

Exciting Updates from Arkensight! After 6 months of hard work, we're thrilled to share some key milestones: • 𝐑𝐞𝐯𝐚𝐦𝐩𝐞𝐝 𝐖𝐞𝐛𝐬𝐢𝐭𝐞: Learn more about what we are building: https://lnkd.in/d9fftg4Z • 𝐉𝐨𝐢𝐧𝐞𝐝 𝐍𝐕𝐈𝐃𝐈𝐀 𝐈𝐧𝐜𝐞𝐩𝐭𝐢𝐨𝐧 𝐏𝐫𝐨𝐠𝐫𝐚𝐦: Proud to be part of the world's cutting-edge startup community. • 𝐁𝐮𝐢𝐥𝐭 𝐀𝐫𝐤𝐞𝐧𝐬𝐢𝐠𝐡𝐭 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥: Currently processing both RGB and thermal imagery. • 𝐅𝐢𝐞𝐥𝐝 𝐓𝐞𝐬𝐭𝐬: Successfully deployed for various use cases, from searching missing people in forests to detecting road cracks. • 𝐅𝐢𝐫𝐬𝐭 𝐆𝐨𝐯𝐞𝐫𝐧𝐦𝐞𝐧𝐭 𝐂𝐨𝐧𝐭𝐫𝐚𝐜𝐭: Signed our first contract to enhance public emergency response in the region. But this is just the beginning. At Arkensight, we're on a mission to build the best UAV multimodal AI models, complemented by world-class software, enabling operators worldwide to effortlessly extract any information from their aerial data. We're preparing to launch the Arkensight private beta waitlist for drone operators and companies eager to supercharge their aerial data analysis. Stay tuned for more updates! #Arkensight #NVIDIAInception #AI #Drones