ARTURO will provide a solution to fulfil future operational needs based on extended use of emerging technologies for advanced radar technologies in Europe.
Falconers is a proud contributor to SPIDER - a feasibility study addressing developments of multi-mission affordable satellites constellations dedicated to space-based Intelligence/Surveillance/Reconnaissance (ISR) for defence use-cases compatible with national and EU initiatives. The project ambition is to provide high reactivity, including autonomous re-tasking, short revisit periods and short end-to-end system latency.
Falconers is taking part in STORE - optimizing AI-based integrated image recognition systems and developing a shared European database on annotated defence images. STORE aims at tackling two critical challenges for the European defence community: the optimal development and benchmark of AI-based integrated image recognition systems, and the creation a shared European database on annotated defence images. STORE will offer short and medium-term keys to detect and recognize new threats and to counter their evolution.
European Space Agency (ESA) Business incubator (BIC) is a startup accelerator for space themed startup ideas. Falconers was admitted to the accelerator with the project FORSAIT (FOrest Remote Sensing AI Twin).The project lasted from June 2022 to June 2024. FORSAIT is a Earth observation and data fusion project where a map application was developed to fuse Sentinel-1 and Sentinel-2 data creating intelligence to visualize forest health, assess forest mass damages and predict wildfires in Estonia. In the next development phases we are seeking to improve the wildfire prediction algorithms with machine learning and adding additional data layers. The product has been presented to Estonian rescue board, private forest owners and the State Forest Management Centre (RMK) who were interested in integrating the end-product into their operations.
During the ”Kõõrdsilm” project we developed a stereo vision system capable of fast and automatic calibration using machine vision techniques. A stereo vision system determines the position of objects in 3-dimensional space by determining their position on each of the camera feeds and using knowledge of the relative distance and direction of the cameras. We used the ORB algorithm to find features from objects on the image and then the RANSAC algorithm to match the features from the two images to determine the distance and direction of the objects.
The aim of the HawkEye project is to see the unseen. In other words, the goal is to detect targets even when the atmospheric conditions are difficult or the targets are otherwise undetectable from the landscape. For that, the data acquired from high resolution multispectral cameras is combined with image enhancing algorithms, deep neural networks and other sensor data to give the user the most critical information as fast and as accurately as possible.
