scholarly journals Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

2018 ◽  
Vol 10 (7) ◽  
pp. 1132 ◽  
Author(s):  
Markku Similä ◽  
Mikko Lensu
Keyword(s):  
Mean Squared Error ◽  
Forest Tree ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Marine Traffic ◽  
Ice Drift ◽  
Random Forest Tree ◽  
Ice Conditions ◽  
Sar Imagery

The automatic identification system (AIS) was developed to support the safety of marine traffic. In ice-covered seas, the ship speeds extracted from AIS data vary with ice conditions that are simultaneously reflected by features in synthetic aperture radar (SAR) images. In this study, the speed variation was related to the SAR features and the results were applied to generate a chart of expected speeds from the SAR image. The study was done in the Gulf of Bothnia in March 2013 for ships with ice class IA Super that are able to navigate without icebreaker assistance. The speeds were normalized to dimensionless units ranging from 0 to 10 for each ship. As the matching between AIS and SAR was complicated by ice drift during the time gap (from hours to two days), we calculated a set of local statistical SAR features over several scales. Random forest tree regression was used to estimate the speed. The accuracy was quantified by mean squared error and by the fraction of estimates close to the actual speeds. These depended strongly on the route and the day. The error varied from 0.4 to 2.7 units2 for daily routes. Sixty-five percent of the estimates deviated by less than one speed unit and 82% by less than 1.5 speed units from the AIS speeds. The estimated daily mean speeds were close to the observations. The largest speed decreases were provided by the estimator in a dampened form or not at all. This improved when the ice chart thickness was included as a predictor.

scholarly journals Estimating the Speed of Icegoing Ships by Integrating SAR Imagery and Ship Data From Automatic Identification System

2018 ◽  
Author(s):  
Markku Simila ◽  
Mikko Lensu
Keyword(s):  
Mean Squared Error ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Squared Error ◽  
Ice Drift ◽  
Gulf Of Bothnia ◽  
Ice Conditions ◽  
Time Gap ◽  
Sar Imagery

Ship speeds extracted from AIS data vary with ice conditions. We extrapolated this variation with SAR data to a chart of expected icegoing speed. The study is for the Gulf of Bothnia in March 2013 and for ships with ice class 1A Super that are able to navigate without icbreaker assistance. The speed was normalized to 0-10 for each ship. As the matching between AIS and SAR was complicated by ice drift during the time gap, from hours to two days, we calculated a set of local SAR statistics over several scales. We used random tree regression to estimate the speed. The accuracy was quantified by mean squared error (MSE), and the fraction of estimates close to the actual speeds. These depended strongly on the route and the day. MSE varied from 0.4 to 2.7 units2 for daily routes. 65 % of the estimates deviated less than one unit and 82 % less than 1.5 units from the AIS speeds. The estimated daily mean speeds were close to the observations. Largest speed decreases were provided by the estimator in a dampened form or not at all. This improved when ice chart thickness was included as one predictor.

ESTIMATION OF EXHAUST SHIP EMISSION FROM MARINE TRAFFIC IN THE STRAITS OF SINGAPORE AND BATAM WATERWAYS USING AUTOMATIC IDENTIFICATION SYSTEM (AIS) DATA

2015 ◽  
Vol 77 (23) ◽  
Author(s):  
Hendra Saputra ◽  
Mufti Fathonah Muvariz ◽  
Sapto Wiratno Satoto ◽  
Jaswar Koto
Keyword(s):  
Exhaust Emission ◽  
Automatic Identification ◽  
Identification System ◽  
Exhaust Gas ◽  
Emission Rates ◽  
Gas Emission ◽  
Automatic Identification System ◽  
Marine Traffic ◽  
Exhaust Gas Emission ◽  
Ship Emission

This study focuses on the Strait of Singapore and Batam Waterways area because it is one of the world’s most congested straits used for international shipping. The study aims to estimate exhaust gas emission and the concentration of emission to several areas around the strait. This is accomplished by evaluating the density of shipping lanes in the strait by using the data which obtained by Automatic Identification System (AIS). MEET methodology is used to estimate emissions from ships. There were 1269 total number of ships through the strait on September 27, 2014 at 06.00 am-08.00 am produces total exhaust emission for NOx, CO, CO2, VOC, PM and SOx were about 12595.35 g/second (15.99%), 25725.19 g/second (32.66%), 11832.31 g/second (15.02%), 5973.23 g/second (7.58%), 443.71 g/second, (0.56%), 22185.57 g/second (28.17%), respectively. The ships under the Singapore flag contribute approximately 22.78% of total emissions in the Strait of Singapore and Batam Waterways followed by Panama, Indonesia and Malaysia 14.47%, 3.67%, 1.91%, respectively. Based on the total emission rates hips under Indonesia and Malaysia rank of seventh and eighth respectively.

scholarly journals Estimating AIS Coverage from Received Transmissions

2012 ◽  
Vol 65 (3) ◽  
pp. 409-425 ◽  
Author(s):  
T. R. Hammond ◽  
D. J. Peters
Keyword(s):  
Mean Squared Error ◽  
New Method ◽  
Automatic Identification ◽  
Identification System ◽  
Binomial Model ◽  
Automatic Identification System ◽  
Squared Error ◽  
Final Estimate ◽  
Spatial Grid

This paper proposes a new method for estimating Automatic Identification System (AIS) coverage empirically from received transmissions. The method is appropriate for stationary coverage assets, as distinct from aircraft and satellites. The key idea behind the method is to interpolate probabilistically between AIS reports in order to reconstruct where the missed transmissions might have occurred. These hypothetical missed transmissions then supplement the received ones in a coverage estimate based on a Bayesian treatment of a binomial model of reception. The final estimate of the coverage is implemented over a spatial grid. The method is demonstrated on simulated AIS data and was found to have lower mean squared error than a previously published method. Assumptions and potential weaknesses of the new method are discussed.

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