Effective surveillance of coastal border areas requires the use of very sensitive and advanced radar systems and processing software. SIGNALIS and ATLAS ELEKTRONIK have jointly developed an interface for Cerberus Mod 2 Diver Detection Sonar (DDS), providing a fully integrated underwater surveillance and security system.
, the SIGNALIS Integrated Maritime Surveillance and Security software technology, collects, processes, fuses, and displays data from a wide range of external sensors, such as radars, AIS, RDF, cameras, weather stations, and now the Cerberus Mod 2 DDS. In this particular application, STYRIS provides protection and situational awareness in the underwater domain. Using unique software algorithms, the system detects, tracks, and correlates vessels and small above and underwater targets in the maritime environment. Multiple sensors can be integrated to present what is known as a Recognized Maritime Picture. The STYRIS technology processes and displays data from multiple radar systems, using the advanced processing algorithms to track data, analyze target patterns, and identify suspicious behavior.
Cerberus Mod 2, the latest offering of ATLAS ELEKTRONIK UK diver detection sonar, detects and classifies open and closed circuit divers, swimmers, swimmer delivery vehicles (SDVs), and underwater vehicles. The lightweight technology, qualified for military use, can be configured for various environments and applications, such as permanent seabed installation for 24/7 port and harbor security and surveillance. The Cerberus Mod 2 DDS also allows operators to detect, track, and classify potential underwater intruders in busy harbors and ports. Cerberus uses advanced sonar processing algorithms to spot underwater targets at ranges of over 900 m in favorable sonar conditions.
Superyacht Underwater Security. Diver Detection And Deterrent System
The integration of the Cerberus Portable Diver Detection Sonar into the STYRIS product provides a DDS underwater picture with above-water sensors. The combination of technologies enables surveillance and tracking of surface and subsurface targets, sensor integration, and a broader overall security system.
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Drones have the potential to revolutionize malaria vector control initiatives through rapid and accurate mapping of potential malarial mosquito larval habitats to help direct field Larval Source Management (LSM) efforts. However, there are no clear recommendations on how these habitats can be extracted from drone imagery in an operational context. This paper compares the results of two mapping approaches: supervised image classification using machine learning and Technology-Assisted Digitising (TAD) mapping that employs a new region growing tool suitable for non-experts. These approaches were applied concurrently to drone imagery acquired at seven sites in Zanzibar, United Republic of Tanzania. Whilst the two approaches were similar in processing time, the TAD approach significantly outperformed the supervised classification approach at all sites (t = 5.1, p < 0.01). Overall accuracy scores (mean overall accuracy 62%) suggest that a supervised classification approach is unsuitable for mapping potential malarial mosquito larval habitats in Zanzibar, whereas the TAD approach offers a simple and accurate (mean overall accuracy 96%) means of mapping these complex features. We recommend that this approach be used alongside targeted ground-based surveying (i.e., in areas inappropriate for drone surveying) for generating precise and accurate spatial intelligence to support operational LSM programmes.
The availability of suitable aquatic habitats is a crucial part of the life cycle of a malarial mosquito [1]. Indeed, an established strategy for controlling malaria vector mosquito populations is Larval Source Management (LSM) which involves the systematic treatment or management of an aquatic mosquito breeding site: pools of water that are suitable for mosquito oviposition and larval development through to emergence as an adult mosquito. For diseases such as malaria, there is a long history of the successful use of LSM for controlling or even eliminating the disease [2, 3, 4, 5]. Yet, this intervention remains relatively underused, compared to indoor interventions such as Long-Lasting Insecticidal bed Nets (LLINs) and Indoor Residual Spraying (IRS) of insecticide, due, in part, to the effort needed to develop and maintain reliable maps of potential larval habitats. For cost-effective deployment of LSM we need reliable and efficient ways of providing baseline maps of potential mosquito larval habitats to treatment teams [6].
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Drones technology has the potential to transform the way that modern LSM is delivered. This, in large part, is due to their ability to capture high precision (<10 cm resolution) images of the ground below in a timely and economical manner. In the case of malaria, important mosquito breeding sites can often be smaller than 1 m and therefore generating maps with high precision is crucial, this representing a significant drawback of most satellite Earth Observation based solutions [7]. Perhaps more significantly, drones can be flown under the clouds, overcoming a major limitation of optical satellite imaging solutions, especially in tropical and temperate locations where cloud cover is persistent [7, 8, 9, 10]. Additionally, due to the relatively low-cost and ease-of-use of many drone systems, there is greater potential for them to be owned and operated by disease control managers (e.g., National Malaria Control programs) rather than relying on external organizations, often from the Global North. In this sense, there is a potential for drones to help in the transition towards the democratization of key technologies.
) in a single day (although this figure varies considerably according to flying height, camera type, drone-use restrictions, drone type). This imagery can be used to generate maps of potential mosquito habitats [7, 8, 9, 11, 12], that can subsequently be used by ground-based teams to direct their LSM programme. This approach can revolutionize a key component of LSM where maps are produced conventionally through ground-based efforts that require a large number of fieldworkers, with coverage often being limited by accessibility issues due to private land/property or challenging terrain (e.g., dense thicket or forest, extensive swamp or flooded areas such as rice paddies). In this respect, the use of drones could provide a step-change in the way LSM is delivered.
Specifically, in terms of mapping potential malarial mosquito larval habitats, previous studies have focussed on the use of supervised image classification approaches [9, 11], which offers a number of benefits. Following the manual delineation of training data, a trained classifier can be used to rapidly identify features over the entire image, representing efficiency gains over a manual digitizing approach where a human operator is expected to manually delineate every feature of interest (i.e., a potential breeding site) in the image. Sophisticated machine learning methods are able to exploit complex relationships between the dependent variable (i.e., surface water bodies that are considered to be potential or actual larval habitats) and the independent variables–typically, independent variables are the spectral bands that the drone-mounted sensor has, or some combination of these bands in the form of ratios or normalized difference ratios.
Daily News 2022 05 27 (digital)
Furthermore, once a classifier is trained, it can be transferred and applied to a new image, negating the need to carry out the time-consuming task of manually delineating training data, therefore representing an automated, scalable solution for mapping potential larval habitats. For this to work reliably, the originally trained classifier must be representative of any new site: for tropical landscapes this is a challenge as they are often heterogenous in nature with dynamic hydrological conditions, i.e., the same site will have very different land cover characteristics between the wet and dry seasons. In Malawi, Stanton et al. (2021) assessed the quality of a Random Forest supervised classification to create maps of potential malarial mosquito larval habitats. Although the overall results demonstrated good agreement with test samples (mean overall accuracy 91%), when the trained classifier was applied outside the initial calibration area, the accuracy noticeably reduced (mean overall accuracy 76%). As such, the supervised classification approach may not necessarily represent the best mapping approach to adopt for operational mapping of potential malarial mosquito larval habitats. Specifically, the implication of mapping errors needs to be carefully considered. For instance, an accuracy score of ~90% may be considered successful in many
Drones technology has the potential to transform the way that modern LSM is delivered. This, in large part, is due to their ability to capture high precision (<10 cm resolution) images of the ground below in a timely and economical manner. In the case of malaria, important mosquito breeding sites can often be smaller than 1 m and therefore generating maps with high precision is crucial, this representing a significant drawback of most satellite Earth Observation based solutions [7]. Perhaps more significantly, drones can be flown under the clouds, overcoming a major limitation of optical satellite imaging solutions, especially in tropical and temperate locations where cloud cover is persistent [7, 8, 9, 10]. Additionally, due to the relatively low-cost and ease-of-use of many drone systems, there is greater potential for them to be owned and operated by disease control managers (e.g., National Malaria Control programs) rather than relying on external organizations, often from the Global North. In this sense, there is a potential for drones to help in the transition towards the democratization of key technologies.
) in a single day (although this figure varies considerably according to flying height, camera type, drone-use restrictions, drone type). This imagery can be used to generate maps of potential mosquito habitats [7, 8, 9, 11, 12], that can subsequently be used by ground-based teams to direct their LSM programme. This approach can revolutionize a key component of LSM where maps are produced conventionally through ground-based efforts that require a large number of fieldworkers, with coverage often being limited by accessibility issues due to private land/property or challenging terrain (e.g., dense thicket or forest, extensive swamp or flooded areas such as rice paddies). In this respect, the use of drones could provide a step-change in the way LSM is delivered.
Specifically, in terms of mapping potential malarial mosquito larval habitats, previous studies have focussed on the use of supervised image classification approaches [9, 11], which offers a number of benefits. Following the manual delineation of training data, a trained classifier can be used to rapidly identify features over the entire image, representing efficiency gains over a manual digitizing approach where a human operator is expected to manually delineate every feature of interest (i.e., a potential breeding site) in the image. Sophisticated machine learning methods are able to exploit complex relationships between the dependent variable (i.e., surface water bodies that are considered to be potential or actual larval habitats) and the independent variables–typically, independent variables are the spectral bands that the drone-mounted sensor has, or some combination of these bands in the form of ratios or normalized difference ratios.
Daily News 2022 05 27 (digital)
Furthermore, once a classifier is trained, it can be transferred and applied to a new image, negating the need to carry out the time-consuming task of manually delineating training data, therefore representing an automated, scalable solution for mapping potential larval habitats. For this to work reliably, the originally trained classifier must be representative of any new site: for tropical landscapes this is a challenge as they are often heterogenous in nature with dynamic hydrological conditions, i.e., the same site will have very different land cover characteristics between the wet and dry seasons. In Malawi, Stanton et al. (2021) assessed the quality of a Random Forest supervised classification to create maps of potential malarial mosquito larval habitats. Although the overall results demonstrated good agreement with test samples (mean overall accuracy 91%), when the trained classifier was applied outside the initial calibration area, the accuracy noticeably reduced (mean overall accuracy 76%). As such, the supervised classification approach may not necessarily represent the best mapping approach to adopt for operational mapping of potential malarial mosquito larval habitats. Specifically, the implication of mapping errors needs to be carefully considered. For instance, an accuracy score of ~90% may be considered successful in many
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