Spatio-temporal characterisation of underwater noise through semantic trajectories

TL;DR

This paper introduces a model for analyzing underwater noise pollution from vessels by reconstructing their trajectories from AIS data, enriching them with semantic info, and studying noise propagation in a highly exploited marine area.

Contribution

The paper presents a novel spatio-temporal model that combines vessel trajectory reconstruction with semantic enrichment to analyze underwater noise impact.

Findings

Effective analysis of noise propagation in the Northern Adriatic Sea

Demonstrates the flexibility of the approach with AIS data and semantic info

Provides insights into the impact of fishing activities on underwater noise

Abstract

Underwater noise pollution from human activities, particularly shipping, has been recognised as a serious threat to marine life. The sound generated by vessels can have various adverse effects on fish and aquatic ecosystems in general. In this setting, the estimation and analysis of the underwater noise produced by vessels is an important challenge for the preservation of the marine environment. In this paper we propose a model for the spatio-temporal characterisation of the underwater noise generated by vessels. The approach is based on the reconstruction of the vessels' trajectories from Automatic Identification System (AIS) data and on their deployment in a spatio-temporal database. Trajectories are enriched with semantic information like the acoustic characteristics of the vessels' engines or the activity performed by the vessels. We define a model for underwater noise propagation and use the trajectories' information to infer how noise propagates in the area of interest. We develop our approach for the case study of the fishery activities in the Northern Adriatic sea, an area of the Mediterranean sea which is well known to be highly exploited. We implement our approach using MobilityDB, an open source geospatial trajectory data management and analysis platform, which offers spatio-temporal operators and indexes improving the efficiency of our system. We use this platform to conduct various analyses of the underwater noise generated in the Northern Adriatic Sea, aiming at estimating the impact of fishing activities on underwater noise pollution and at demonstrating the flexibility and expressiveness of our approach.