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

• URL: http://arxiv.org/abs/2203.06993v3
• Date: Fri, 7 Apr 2023 10:00:24 GMT
• Title: Supervised segmentation of NO2 plumes from individual ships using TROPOMI satellite data
• Authors: Solomiia Kurchaba, Jasper van Vliet, Fons J. Verbeek, Jacqueline J. Meulman, Cor J. Veenman
• Abstract summary: The shipping industry is one of the strongest anthropogenic emitters of $textNO_text2$ -- substance harmful both to human health and the environment. All the methods currently used for ship emission monitoring are costly and require proximity to a ship. A promising approach is the application of remote sensing.
• Score: 0.07874708385247353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The shipping industry is one of the strongest anthropogenic emitters of $\text{NO}_\text{x}$ -- substance harmful both to human health and the environment. The rapid growth of the industry causes societal pressure on controlling the emission levels produced by ships. All the methods currently used for ship emission monitoring are costly and require proximity to a ship, which makes global and continuous emission monitoring impossible. A promising approach is the application of remote sensing. Studies showed that some of the $\text{NO}_\text{2}$ plumes from individual ships can visually be distinguished using the TROPOspheric Monitoring Instrument on board the Copernicus Sentinel 5 Precursor (TROPOMI/S5P). To deploy a remote sensing-based global emission monitoring system, an automated procedure for the estimation of $\text{NO}_\text{2}$ emissions from individual ships is needed. The extremely low signal-to-noise ratio of the available data as well as the absence of ground truth makes the task very challenging. Here, we present a methodology for the automated segmentation of $\text{NO}_\text{2}$ plumes produced by seagoing ships using supervised machine learning on TROPOMI/S5P data. We show that the proposed approach leads to a more than a 20\% increase in the average precision score in comparison to the methods used in previous studies and results in a high correlation of 0.834 with the theoretically derived ship emission proxy. This work is a crucial step toward the development of an automated procedure for global ship emission monitoring using remote sensing data.

Related papers

• AI for operational methane emitter monitoring from space [14.274401014063018]
  Mitigating methane emissions is the fastest way to stop global warming in the short-term. We present MARS-S2L, an automated AI-driven methane emitter monitoring system for Sentinel-2 and Landsat satellite imagery. Running this system operationally for six months has yielded 457 near-real-time detections in 22 different countries.
  arXiv  Detail & Related papers  (2024-08-08T20:06:37Z)
• A Data-Driven Supervised Machine Learning Approach to Estimating Global Ambient Air Pollution Concentrations With Associated Prediction Intervals [0.0]
  We have developed a scalable, data-driven, supervised machine learning framework to impute missing temporal and spatial measurements. This model is designed to impute missing temporal and spatial measurements, thereby generating a comprehensive dataset for pollutants including NO$, O$_3$, PM$_10$, PM$_2.5$, and SO$. The model's performance across various geographical locations is examined, providing insights and recommendations for strategic placement of future monitoring stations.
  arXiv  Detail & Related papers  (2024-02-15T11:09:22Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• Large-scale Detection of Marine Debris in Coastal Areas with Sentinel-2 [3.6842260407632903]
  Efforts to quantify marine pollution are often conducted with sparse and expensive beach surveys. Satellite data of coastal areas is readily available and can be leveraged to detect aggregations of marine debris containing plastic litter. We present a detector for marine debris built on a deep segmentation model that outputs a probability for marine debris at the pixel level.
  arXiv  Detail & Related papers  (2023-07-05T17:38:48Z)
• Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration [55.41644538483948]
  This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format. The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
  arXiv  Detail & Related papers  (2023-04-27T20:21:18Z)
• Anomalous NO2 emitting ship detection with TROPOMI satellite data and machine learning [0.08602553195689512]
  Starting from 2021, more demanding $textNO_text2$ emission restrictions were introduced for ships operating in the North and Baltic Sea waters. 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 satellite data.
  arXiv  Detail & Related papers  (2023-02-24T16:54:47Z)
• Near Real-time CO$_2$ Emissions Based on Carbon Satellite And Artificial Intelligence [20.727982405167758]
  We propose an integral AI based pipeline that contains both a data retrieval algorithm and a two-step data-driven solution. First, the data retrieval algorithm can generate effective datasets from multi-modal data including carbon satellite, the information of carbon sources, and several environmental factors. Second, the two-step data-driven solution that applies the powerful representation of deep learning techniques to learn to quantify anthropogenic CO$$ emissions.
  arXiv  Detail & Related papers  (2022-10-11T12:01:32Z)
• Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
  The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today. We propose a novel deep learning algorithm to automate this aerial person detection (APD) task. We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
  arXiv  Detail & Related papers  (2021-11-17T21:48:31Z)
• R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes [69.6715406227469]
  Self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches. We present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework.
  arXiv  Detail & Related papers  (2021-08-10T17:57:03Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Real-Time target detection in maritime scenarios based on YOLOv3 model [65.35132992156942]
  A novel ships dataset is proposed consisting of more than 56k images of marine vessels collected by means of web-scraping. A YOLOv3 single-stage detector based on Keras API is built on top of this dataset.
  arXiv  Detail & Related papers  (2020-02-10T15:25:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.