The significant increase in the amount of satellite data in recent years along with the increase in computing resources has opened up new possibilities for Earth Observation (EO) data analysis. However, the main obstacle to obtaining highquality products is the limited amount of labeled data. Among the methods that can help solve this type of problem, transfer learning (TL) has gained enormous interest in recent years. It involves using a model trained on one problem to solve another problem. This article reviews TL methods as applied to EO data analyses. It provides a structured overview of methods in relation to applications, publication statistics, and summarizes the advantages and disadvantages noted of previous research.

Police and various security services use video analysis for securing public space, mass events, and when investigating criminal activity. Due to a huge amount of data supplied to surveillance systems, some automatic data processing is a necessity. In one typical scenario, an operator marks an object in an image frame and searches for all occurrences of the object in other frames or even image sequences. This problem is hard in general. Algorithms supporting this scenario must reconcile several seemingly contradicting factors: training and detection speed, detection reliability, and learning from small data sets. In the system proposed here, we use a two-stage detector. The first region proposal stage is based on a Cascade Classifier while the second classification stage is based either on a Support Vector Machines (SVMs) or Convolutional Neural Networks (CNNs). The proposed configuration ensures both speed and detection reliability. In addition to this, an object tracking and background-foreground separation algorithm is used, supported by the GrabCut algorithm and a sample synthesis procedure, in order to collect rich training data for the detector. Experiments show that the system is effective, useful, and applicable to practical surveillance tasks.

The goal of the research reported here was to investigate whether the design methodology utilising embodied agents can be applied to produce a multi-modal human–computer interface for cyberspace events visualisation control. This methodology requires that the designed system structure be defined in terms of cooperating agents having well-defined internal components exhibiting specified behaviours. System activities are defined in terms of finite state machines and behaviours parameterised by transition functions. In the investigated case the multi-modal interface is a component of the Operational Centre which is a part of the National Cybersecurity Platform. Embodied agents have been successfully used in the design of robotic systems. However robots operate in physical environments, while cyberspace events visualisation involves cyberspace, thus the applied design methodology required a different definition of the environment. It had to encompass the physical environment in which the operator acts and the computer screen where the results of those actions are presented. Smart human–computer interaction (HCI) is a time-aware, dynamic process in which two parties communicate via different modalities, e.g., voice, gesture, eye movement. The use of computer vision and machine intelligence techniques are essential when the human is carrying an exhausting and concentration demanding activity. The main role of this interface is to support security analysts and operators controlling visualisation of cyberspace events like incidents or cyber attacks especially when manipulating graphical information. Visualisation control modalities include visual gesture- and voice-based commands.