Abstract
This paper presents the development and implementation of learning-based detection and tracking methods using multibeam data to detect marine litter in the water column. The presented work encompasses (i) the creation of acoustic videos and the application of multiple post-processing techniques; (ii) the training of multiple You Only Look Once (YOLO) detection models, specifically YOLOv8, across different variants, acoustic frequencies, and input types (both raw and post-processed); (iii) and the development of a marine litter tracking system based on DeepSORT. The results include a multibeam multi-frequency data study demonstrating the potential of acoustic image sensing for detecting and tracking marine litter materials in the water column.

Abstract
The impact of wildfires is increasing worldwide. The root causes of these effects are manifold, encompassing among others climate change and the accumulation of fuels and increasing settlements in wildland-urban interfaces (WUI). Reports and initiatives to better understand and govern these developments have been launched and call for more integrated approaches to wildfire risk management, including the use of targets or Key Performance Indicators (KPIs). However, despite some examples such as Portugal, wildfire risk management targets are still mainly lacking in Europe. This is surprising since they find wider application in the U.S. and are also more widely applied for flooding in Europe. This perspective hence takes a closer look at the use of targets in reducing disaster risk for different hazards worldwide and reflects about the opportunities and challenges for wildfire risk reduction targets for Europe. It concludes with some suggestions for the application of wildfire risk reduction targets for Europe.

Abstract
We present a pilot study where spectral unmixing is applied to hyperspectral images captured in a controlled environment with a threefold purpose in mind: validation of our experimental setup, of the data processing pipeline, and of the usage of spectral unmixing algorithms for the aforementioned research avenue. Results from this study show that classical techniques such as VCA and FCLS can be used to distinguish between plastic and nonplastic materials, but struggle significantly to distinguish between spectrally similar plastics, even in the presence of multiple pure pixels.

Abstract
This paper explores the potential use of acoustic imaging and the use of a multi-frequency multibeam-echosounder (MBES) for monitoring marine litter in the water column. The main goal is to perform a test and validation setup using a simulation and actual experimental setup to determine if the MBES data can detect marine litter in a water column image (WCI) and if using multi-frequency MBES data will allow to better distinguish and characterize marine litter debris in detection applications. Results using simulated HoloOcean Environment and actual marine litter data revealed the successful detection of objects commonly found in ocean litter hotspots at various ranges and frequencies, enablingthe pursue of novel means of automatic detection and classification in MBES WCI data while using multi-frequency capabilities.

Abstract
This paper introduces an advanced acoustic imaging system leveraging multibeam water column data at various frequencies to detect and classify marine litter. This study encompasses (i) the acquisition of test tank data for diverse types of marine litter at multiple acoustic frequencies; (ii) the creation of a comprehensive acoustic image dataset with meticulous labelling and formatting; (iii) the implementation of sophisticated classification algorithms, namely support vector machine (SVM) and convolutional neural network (CNN), alongside cutting-edge detection algorithms based on transfer learning, including single-shot multibox detector (SSD) and You Only Look once (YOLO), specifically YOLOv8. The findings reveal discrimination between different classes of marine litter across the implemented algorithms for both detection and classification. Furthermore, cross-frequency studies were conducted to assess model generalisation, evaluating the performance of models trained on one acoustic frequency when tested with acoustic images based on different frequencies. This approach underscores the potential of multibeam data in the detection and classification of marine litter in the water column, paving the way for developing novel research methods in real-life environments.