{
  "id": 1713686,
  "title": "After Operation Sindoor, Indian Startup Digantara Unveils AI-Powered MOSAIC Space Surveillance Network | VIDEO",
  "url": "https://urgent.news/2026/08/18/after-operation-sindoor-indian-startup-digantara-unveils-ai-powered",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-18T12:13:06.000Z",
  "source": {
    "name": "Free Press Journal",
    "slug": "free-press-journal",
    "url": "https://www.freepressjournal.in/india/after-operation-sindoor-indian-startup-digantara-unveils-ai-powered-mosaic-space-surveillance-network-video"
  },
  "original_language": "en",
  "account": "New Delhi, August 18, 2026: Indian startup Digantara has introduced MOSAIC, an AI-powered optical sensor network designed to track objects in Low Earth Orbit, as India seeks to lessen its dependence on foreign space surveillance data. This announcement follows Operation Sindoor, which saw India utilize a combination of domestic satellites and commercial imagery for target validation and post-strike assessment. Though this strategy was successful, it highlighted the necessity of having autonomous access to real-time space data. Introducing MOSAIC, Digantara presents a Wide Area Sensing Architecture capable of simultaneously searching, detecting, and tracking numerous objects, transforming observations into orbital intelligence. Built in India to meet the scale of contemporary demands, MOSAIC is a fully indigenous network designed for continuous orbital awareness. The initial deployment consists of five interconnected sensor nodes that function collectively as a coordinated surveillance system. Each node incorporates an Optical Head Unit for high-resolution sky imaging and an Electronics Head Unit managing onboard processing, communications, timing, and autonomous operations. Designed to function in challenging environments, the system is solar-powered with battery backup and can withstand temperatures from the scorching Thar Desert to the freezing Himalayan winters. Central to MOSAIC is an AI-driven detection system that identifies stars and Resident Space Objects (RSOs), including faint satellites and orbital debris. Using advanced \"Lost-in-Space\" attitude estimation techniques, the system can swiftly identify and catalog targets even without prior tracking data. This enables rapid detection and cataloguing of targets, with potential military applications. Beyond space monitoring, the technology could provide celestial navigation in areas without Global Positioning System (GPS) coverage, offering significant advantages in military scenarios. MOSAIC expands Digantara's existing space domain awareness infrastructure by integrating ground-based sensors, space-based tracking systems, and AI-powered data fusion tools. This integrated approach aims to assist India in monitoring satellites, debris, and potential threats across orbital regions from Low Earth Orbit to Geostationary Orbit. As geopolitical tensions rise, MOSAIC's independent space surveillance capabilities could bolster India's strategic autonomy in the burgeoning security domain of space.",
  "summary": "New Delhi, August 18, 2026: Indian space technology startup Digantara has unveiled MOSAIC, an artificial intelligence-powered optical sensor network designed to track objects in Low Earth Orbit in real time, as India looks to reduce its reliance on foreign space surveillance data. The development comes months after Operation Sindoor, during which India relied on a mix of domestic satellite…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "The Hindu",
        "title": "Bengaluru space start-up unveils AI-powered optical sensor network for monitoring objects in Low Earth Orbit",
        "url": "https://urgent.news/2026/08/18/bengaluru-space-start-up-unveils-ai-powered-optical-sensor-network",
        "published": "2026-08-18T14:58:54.000Z"
      }
    ]
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}