# The Big Picture: An Improved Method for Mapping Shipping Activities
## 2 Background
### Satellite Images  
As stated above, there are situations where the AIS protocols are not enough to capture the presence of a vessel in an area. These may be caused by the nature of the AIS system, such as packet collisions for dense areas or coverage issues, as well as the intentional switch-off of the transmitter. To overcome such issues and to have a more complete situational awareness, additional data sources should be included to complement AIS data.  
One of the most prominent data sources that can be used for monitoring an area are satellite images, both optical and Synthetic Aperture Radar (SAR). Being a non-cooperative source, it does not rely on the vessels themselves for receiving data. Additionally, since such data are collected by satellites, global coverage can be considered a possibility [43; 44]. Of course, as in all cases, relying solely on satellite imagery for monitoring an area comes with its own disadvantages. More specifically, optical imagery is often affected by weather conditions and environmental effects, while SAR data have limited spatial resolution. Moreover, a common issue with satellite images are long periods between consecutive visits of the satellite to the target area, resulting in sparse data [45]. Nevertheless, both optical and SAR images can be greatly useful--complementing AIS data--while trying to detect vessels in an area of interest, especially when dealing with gaps in the respective ship trajectories.  
In the related literature, the problem of vessel detection in satellite imagery has been the focus of many academic and industrial research activities [43; 44]. These activities can be classified into two broad categories:  
* The approaches that are based on the employment of threshold-based algorithms, such as the Constant False Alarm Rate (CFAR) algorithms [46]. The CFAR is a group of adaptive algorithms that vary the detection threshold as a function of the sensed environment, rather than a single value, in order to try to fix the probability of false alarms due to noise or jamming at a predefined value. Different tools were presented for SAR imagery through CFAR algorithms over the years, as mentioned in [47]. Amongst them, the European Commission's Joint Research Centre has released the Search for Unidentified Maritime Objects (SUMO), a tool specifically designed for detecting vessels in such images [48].
* AI-based approaches that employ Neural Networks (NNs) in order to detect vessels based on trained models [49].  
Ref. [43] presents a detailed, up-to-date overview of approaches for vessel detection in optical imagery covering both categories. The processing chain that we used to perform vessel detection in satellite imagery for the needs of this project is based on our previous work that belongs to the AI-based category and is partially covered in [50]. However, in order to address the known scalability problems that come with the use of NNs in large volumes of satellite data [51], in this work, we further extend the approach described in [50] and developed a hybrid approach that employs both threshold-based mechanisms and CNNs, in order to obtain the best of both worlds: high accuracy results provided by CNNs while addressing scalability issues by using thresholding to filter out redundant image tiles.