Detection of anomalously emitting ships through deviations from predicted TROPOMI NO2 retrievals

02/24/2023
by   Solomiia Kurchaba, et al.
0

Starting from 2021, more demanding NO_x emission restrictions were introduced for ships operating in the North and Baltic Sea waters. Since all methods currently used for ship compliance monitoring are financially and time demanding, it is important to prioritize the inspection of ships that have high chances of being non-compliant. The current state-of-the-art approach for a large-scale ship NO_2 estimation is a supervised machine learning-based segmentation of ship plumes on TROPOMI images. However, challenging data annotation and insufficiently complex ship emission proxy used for the validation limit the applicability of the model for ship compliance monitoring. In this study, we present a method for the automated selection of potentially non-compliant ships using a combination of machine learning models on TROPOMI/S5P satellite data. It is based on a proposed regression model predicting the amount of NO_2 that is expected to be produced by a ship with certain properties operating in the given atmospheric conditions. The model does not require manual labeling and is validated with TROPOMI data directly. The differences between the predicted and actual amount of produced NO_2 are integrated over different observations of the same ship in time and are used as a measure of the inspection worthiness of a ship. To assure the robustness of the results, we compare the obtained results with the results of the previously developed segmentation-based method. Ships that are also highly deviating in accordance with the segmentation method require further attention. If no other explanations can be found by checking the TROPOMI data, the respective ships are advised to be the candidates for inspection.

READ FULL TEXT
research
03/14/2022

Supervised segmentation of NO2 plumes from individual ships using TROPOMI satellite data

Starting from 2021, the International Maritime Organization significantl...
research
12/22/2020

Designing an Interactive Visualization System for Monitoring Participant Compliance in a Large-Scale, Longitudinal Study

Frequent monitoring of participant compliance is necessary when conducti...
research
02/26/2018

Interpreting Complex Regression Models

Interpretation of a machine learning induced models is critical for feat...
research
09/20/2023

From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring

Monitoring surface cracks in infrastructure is crucial for structural he...
research
03/11/2021

Pavement Distress Detection and Segmentation using YOLOv4 and DeepLabv3 on Pavements in the Philippines

Road transport infrastructure is critical for safe, fast, economical, an...
research
01/17/2022

Who supervises the supervisor? Model monitoring in production using deep feature embeddings with applications to workpiece inspection

The automation of condition monitoring and workpiece inspection plays an...
research
12/05/2020

Different Approaches Towards Vertical Track Irregularity Prediction – A Comparative Study

Railway systems require regular manual maintenance, a large part of whic...

Please sign up or login with your details

Forgot password? Click here to reset