Dr Maximilian Rodger


Research Fellow
PhD, BSc (Hons), Diploma in Industrial Studies (DIS)

Academic and research departments

Remote sensing and Earth observation, Surrey Space Centre.

About

Publications

Maximilian Rodger, Raffaella Guida (2023)Revealing Dark Vessels in the Mauritius Exclusive Economic Zone (EEZ) Using Multi-Temporal SAR and AIS Data, In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposiumpp. 2077-2080 IEEE

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).

Maximilian Rodger, Raffaella Guida (2023)Damage Assessment Mapping in Mariupol (Ukraine) with Multi-Temporal Synthetic Aperture Radar (SAR), In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposiumpp. 7186-7189 IEEE

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.

Maximilian Rodger, Raffaella Guida, (2022)MAPPING DARK SHIPPING ZONES USING MULTI-TEMPORAL SAR AND AIS DATA FOR MARITIME DOMAIN AWARENESS, In: 2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)pp. 3672-3674 IEEE

The monitoring of ships which do not report their Automatic 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 Synthetic 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 distribution of `dark' zones (i.e. areas where repeated unassignments 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-1 Interferometric Wide (IW) swath mode products and AIS data acquired from the English Channel, UK.

Additional publications