LAST Autonomous Surveillance Tower is a dual-use, autonomous ISR/early-warning tower delivering persistent situational awareness for wildfire prevention and other disaster-risk hazards in remote terrain. It uses edge AI to autonomously detect, classify and geolocate smoke/fire outbreaks in real time by fusing electro-optical and thermal imagery; alerts are geo-referenced and pushed to incident command to accelerate the Observe–Orient–Decide–Act (OODA) cycle. LAST provides 24/7 unattended coverage and a continuously updated common operational picture (COP). It includes a communications/fusion node that can ingest feeds from UAVs and other sensors and distribute prioritised detections, tracks and imagery via satellite connectivity. Configurations span civilian, marine and multi-domain use and can combine sensor mosaics (optical, RF, thermal, radar) to match the operational theatre. This reduces reliance on human spotters and patrols, enables earlier dispatch and safer route planning and supports multi-agency coordination. Operational traction: a LAST tower station has been handed over to the Hellenic Ministry of Climate Crisis and Civil Protection to equip the Fire Service for autonomous fire detection in mountainous terrain. Interoperability/dual-use: outputs (video, metadata, alerts, tracks) are designed for integration into civil C2/EOC platforms; where defence-grade interoperability is required, they can be interfaced via gateways to NATO ISR data exchange practices (e.g., STANREC 4869/SAPIENT for sensor tasking and STANAG 4609/4676 for motion imagery and track products), subject to end-user integration choices. Capability gaps/needs addressed (DIREKTION/IFAFRI): • Real-time detection, monitoring and analysis of threats/hazards. • Remote acquisition of information. • Integration of information into incident command operations. • Actionable intelligence for responder decision-making.
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The objective of this proposal is to field LAST Autonomous Surveillance Tower as a near-to-deployment early-warning and situational-awareness capability for civil protection and fire services operating in remote, hard-to-access areas. LAST is proposed as an operational tool that detects emerging wildfire indicators early, maintains persistent surveillance over priority zones and delivers decision-ready alerts that strengthen incident command from preparedness through initial attack. LAST achieves this by combining electro-optical and thermal sensing with edge AI to autonomously detect, classify and geolocate smoke and fire signatures and to publish geo-referenced alerts and an evolving operational picture for command centres. It is engineered for austere, unattended operation with autonomous power and resilient communications backhaul so that prioritised detections and imagery can be delivered continuously when access is constrained and human lookouts or patrols cannot provide continuous coverage. The system is designed to integrate outputs into existing EOC and incident command workflows, supporting a shared common operational picture and information exchange across agencies. The proposal is highly relevant to Disaster and Resilience societal gaps because wildfire growth is rapid, information is often incomplete and responder safety depends on timely, trusted situational awareness. By enabling real-time hazard detection, remote acquisition of information, integration into incident command workflows and actionable intelligence for dispatch and coordination, LAST supports faster decisions and safer resource allocation; its modular deployment concept supports scaling from single sites to networked coverage as needs expand. LAST is aligned with the Awards’ focus on mature, demonstrable solutions, including operational deployment in Greece and a handover to the Hellenic Ministry of Climate Crisis and Civil Protection to equip the Fire Service.
LAST’s innovation lies in turning persistent, unattended sensing into decision-grade early warning at scale. Instead of relying on constant human monitoring or heavyweight active-surveillance infrastructure, LAST couples electro-optical and thermal sensing with on-tower edge computing to automate detection, tracking, classification and geolocation. A key differentiator is the ability to assign geographic coordinates to image information and classify events of interest without requiring active ranging sensors such as radar or laser rangefinders, enabling a passive approach that is well suited to wide-area forest protection and other disaster-risk prone environments. The system provides a unified picture that fuses feeds from multiple towers and sensors into a single, intuitive, mission-oriented map view, reducing operator workload while transforming raw imagery into geo-referenced alerts and an evolving common operational picture. Innovation is also delivered through architecture and deployment. LAST is modular, allowing configurations to be tailored to mission needs and can fuse inputs from UAVs and other sensors through an associated communications device to extend coverage and enrich the operational picture. Resilient connectivity via GSM and satellite links supports continuity of service in remote or degraded infrastructure conditions, while autonomous, solar-based power with battery storage enables 24/7 operation with minimal logistics and rapid installation without heavy machinery. Finally, LAST is a turnkey autonomous surveillance solution designed for integration enabling seamless situational awareness: its outputs can be ingested into existing emergency management and command-and-control systems, enabling information exchange and shortening the time from detection to dispatch. Together these approaches deliver a step-change in early detection and situational awareness while reducing manpower burden and improving responder safety.
LAST resolves a critical operational gap faced by first responders: the need for continuous, trusted situational awareness over wide high-risk areas with austere terrain & limited communication coverage and where minutes determine incident escalation to a major disaster. In wildfire operations, early detection is often constrained by human lookout coverage, limited patrol capacity, delayed reporting and incomplete visibility in complex terrain. These constraints compress decision time, increase uncertainty for incident command and expose crews to higher risk. By providing persistent, unattended monitoring and automated early warning, LAST shortens the time from ignition to detection and from detection to decision. It continuously surveys designated zones and produces geo-referenced alerts and supporting imagery that can be integrated into command workflows, giving incident command a clearer and timelier operational picture when the incident is still containable. This enables earlier tasking of resources, better prioritisation of competing alerts and safer planning of access routes and staging areas based on where the hazard is developing. In practice, this reduces the reliance on chance visual sightings and decreases the workload on personnel who would otherwise need to watch multiple camera feeds or conduct repeated patrols. LAST also increases operational capacity by enabling scalable coverage. A single site can protect a high-value area, while networked towers can provide layered surveillance across a region, supporting preparedness and surge operations during peak risk periods. Because the system operates autonomously with resilient communications backhaul and autonomous power, it remains effective in remote locations and during infrastructure disruption, maintaining continuity of information flow to decision-makers. The result is improved speed and confidence of incident command, earlier intervention opportunities and better multi-agency coordination.
Delian’s team combines proven autonomy and delivery expertise required for a safe, robust, real-time situational awareness system. The core team brings expertise in AI, machine-perception and sensor-fusion depth, calibration & state estimation. Its members come from organisations like Palantir, Meta, Uber, and Apple, and globally esteemed universities. The factory in Athens is manned with experienced engineers & machinists enabling rapid manufacturing, prototyping and field deployment.
Delian’s team combines proven autonomy and delivery expertise required for a safe, robust, real-time situational awareness system. The core team brings expertise in AI, machine-perception and sensor-fusion depth, calibration & state estimation. Its members come from organisations like Palantir, Meta, Uber, and Apple, and globally esteemed universities. The factory in Athens is manned with experienced engineers & machinists enabling rapid manufacturing, prototyping and field deployment.