Illegal Unreported and Unregulated (IUU) fishing is responsible for considerable economic losses in developing countries due to poor governance and limited resources for surveillance, especially in presence of vast Exclusive Economic Zones (EEZs). Recent studies conducted on behalf of the Food and Agriculture Organization (FAO) show that global figures of economic losses are inappropriate due to the varying availability of resources and regulations from country to country while methodologies and indicators applied locally, informing about the magnitude and impact of IUU fishing activities in a prescribed region, would be more robust. Following FAO guidelines, a novel methodology based on anomalies found in Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) data is here proposed to estimate losses to IUU fishing in Mauritius EEZ. Results from an exercise, conducted in Saya de Malha (Mauritius) from June to November 2023, show a worst-case scenario of potentially more than four-thousand tonnes of IUU caught which would translate in economic losses of more than 21 millions of US dollars.

Illegal, Unreported and Unregulated (IUU) fishing is a major global issue, costing up to £23 billion in the global economy per year [1]. Although the detection of vessels using Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) data has gained a lot of attention in recent years, the identification of illegal activities still remains largely unaddressed. This paper presents a model for the detection and identification of anomalous AIS shut-offs and transhipments around the Exclusive Economic Zone (EEZ) at Mauritius as part of the NEREUS project of the University of Surrey. The model combines, in a novel way, various techniques established in literature that consist of target detection in SAR images, vessel classification, construction and extrapolation of vessel tracklines using AIS data and AIS-SAR data association. The proposed model is preliminarily validated using NovaSAR data, as proof of concept, with varied results, that recommend for a change in the detection block.

This research proposes an anomaly detection workflow intended primarily for integration into satellite-based tip and cue services for maritime surveillance. The workflow is centred around a ship trajectory prediction model which is designed to respond to anomalous events such as AIS "shut-off" events. This is important for accurately predicting the trajectories of potentially suspicious vessels that are moving, and subsequently scheduling the tasking of satellite acquisitions to monitor these vessels. The research implements a ship trajectory prediction model based on AIS data using Recurrent Neural Networks (RNNs). The efficacy of the model is evaluated based on the mean great circle distance between predicted and actual vessel trajectories, demonstrating satisfactory performance for typical motion patterns while acknowledging certain limitations in prediction accuracy. Overall, this study represents a significant step forward in the integration of imaging satellites and AIS data for maritime surveillance, offering a promising approach for anomaly detection and improving the efficiency of satellite-based monitoring systems. A GitHub repository containing the source code and related materials for this work is made available.

Illegal Unreported and Unregulated (IUU) fishing is a major threat to ocean biodiversity and preservation. A UK-Mauritius team is joining forces to develop a satellite-based monitoring solution that can improve maritime domain awareness in Mauritius Exclusive Economic Zone (EEZ) since official records of authorized fishing vessels are outdated or incomplete. This paper applies a previously developed methodology for Automatic Identification System (AIS) and Synthetic Aperture Radar (SAR) data matching through Artificial Intelligence (AI) to the case study area showing the potential of the technologies and techniques in detecting anomalies.

The United Nations Development Programme (UNDP) launched the Ocean Innovators program to combat illegal fishing and destructive fishing practices, benefiting Small Island Developing States (SIDS) and Least Developed Countries (LDCs). One of the selected projects, ‘Nereus’, currently being developed by Surrey Space Centre (SSC) and Mauritius Research and Innovation Council (MRIC), utilises AI and satellite data fusion to monitor fishing vessel activity in Mauritius’ Exclusive Economic Zone (EEZ) and Marine Protected Areas (MPAs). The project combines various satellite technologies, including Synthetic Aperture Radar (SAR), Automatic Identification System (AIS) and Vessel Monitoring System (VMS). This paper analyses multi-temporal SAR and AIS data to identify "dark" ships that are not transmitting AIS signals. The methodology is applied to the Mauritius EEZ and MPAs, providing authorities with valuable information for informed decision-making and effective Maritime Domain Awareness (MDA).

The use of multi-temporal Synthetic Aperture Radar (SAR) imagery to assess damage caused by directed attacks in conflict zones is explored. This paper focuses on the Russia-Ukraine war as an example and emphasises the need for a reliable method to measure damage to urban infrastructure. The study presents a methodology that utilises a technique called Coherent Change Detection (CCD) using SAR imagery from Sentinel-1 to assess damaged areas in the city of Mariupol, Ukraine. The authors acquired SAR images before and after an artillery shelling event and measured the change in coherence between these images to assess the damage. They also compared the SAR results with contextual information from media reports and community-based projects to validate the findings. The paper provides specific examples of damage level classification maps for various types of infrastructure, such as a metallurgical factory, shopping mall and a maternity hospital. The results show good visual correlation between the bomb impact locations and the severity of damage. The authors conclude that multi-temporal SAR can complement other sensors in damage assessment mapping, especially in adverse weather conditions. Future work will focus on improving the damage assessment index and validating the damage level thresholds.

The monitoring of ships which do not report their Au-tomatic Identification System (AIS) information is important for Maritime Domain Awareness (MDA). In this paper, an improved maritime picture is generated by presenting a new methodology to map these so-called ‘dark’ ships over time. Firstly, a robust and accurate data association between Syn-thetic Aperture Radar (SAR) ship detections and AIS data is carried out on multi-temporal SAR imagery and AIS data. Subsequently, Kernel Density Estimation (KDE) is applied to unassigned SAR ship detections to reveal the spatial distri-bution of ‘dark’ zones (i.e. areas where repeated unassign-ments occur). This analysis helps identify areas where ships frequently do not report, which can help guide authorities in the best way to respond. The methodology is validated using Sentinel-l Interferometric Wide (IW) swath mode products and AIS data acquired from the English Channel, UK.

The use of airborne Mid-Wave InfraRed (MWIR) imagery for maritime applications such as ship detection is evaluated for future spaceborne missions. A flight campaign was carried out on 30 October 2020 over the Solent (UK) to provide a dataset of airborne MWIR that was acquired at the same time as a spaceborne Synthetic Aperture Radar (SAR) product. The thermal infrared imagery is compared to SAR to determine its suitability for ship detection. Automatic Identification System (AIS) data was also acquired and used to both identify and validate the ship detections where available. The results indicate that high spatial resolution satellite thermal infrared imagery can be a useful data source for ship detection.

A novel SAR-AIS data association technique is proposed consistent with being used in dense shipping environments, where association of SAR and AIS datasets is non-trivial and SAR false alarm rates are typically high. A ship classification model based on transfer learning classifies ship types in SAR imagery. The classification results are subsequently used in the SAR-AIS data association, which uses a rank-ordered assignment technique. The methodology is validated using a Sentinel-1 SAR product and terrestrial-based AIS product acquired from the Gulf Coast, USA. Results show optimal data association which is improved using class (i.e. ship type) information.

In this research the best techniques of fusion for near-contemporaneous Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) datasets are studied to simulate the expected performance from NovaSAR-1. Specifi-cally, data association techniques are quantitatively compared by performing a series of Monte Carlo tests. The evaluation has been carried out using a satellite-based AIS dataset acquired from the English Channel on 07 June 2016, and SAR ship detections are simulated by reckoning the AIS dataset forward in time along a geodesic on a WGS84 reference ellipsoid. Accurate data association is achieved using an m-best multidimensional assignment technique, which is consistent with being used in an operational environment, especially in high-density shipping areas.