Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors
In 2014, through the World-Class Tanker Safety System (WCTSS) initiative, the Government of Canada launched the Northern Marine Transportation Corridors (NMTC) concept. The corridors were created as a strategic framework to guide Federal investments in marine transportation in the Arctic. With new g...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/9/6/383 |
| _version_ | 1797565662624743424 |
|---|---|
| author | René Chénier Mesha Sagram Khalid Omari Adam Jirovec |
| author_facet | René Chénier Mesha Sagram Khalid Omari Adam Jirovec |
| author_sort | René Chénier |
| collection | DOAJ |
| description | In 2014, through the World-Class Tanker Safety System (WCTSS) initiative, the Government of Canada launched the Northern Marine Transportation Corridors (NMTC) concept. The corridors were created as a strategic framework to guide Federal investments in marine transportation in the Arctic. With new government investment, under the Oceans Protection Plan (OPP), the corridors initiative, known as the Northern Low-Impact Shipping Corridors, will continue to be developed. Since 2016, the Canadian Hydrographic Service (CHS) has been using the corridors as a key layer in a geographic information system (GIS) model known as the CHS Priority Planning Tool (CPPT). The CPPT helps CHS prioritize its survey and charting efforts in Canada’s key traffic areas. Even with these latest efforts, important gaps in the surveys still need to be filled in order to cover the Canadian waterways. To help further develop the safety to navigation and improve survey mission planning, CHS has also been exploring new technologies within remote sensing. Under the Government Related Initiatives Program (GRIP) of the Canadian Space Agency (CSA), CHS has been investigating the potential use of Earth observation (EO) data to identify potential hazards to navigation that are not currently charted on CHS products. Through visual interpretation of satellite imagery, and automatic detection using artificial intelligence (AI), CHS identified several potential hazards to navigation that had previously gone uncharted. As a result, five notices to mariners (NTMs) were issued and the corresponding updates were applied to the charts. In this study, two AI approaches are explored using deep learning and machine learning techniques: the convolution neural network (CNN) and random forest (RF) classification. The study investigates the effectiveness of the two models in identifying shoals in Sentinel-2 and WorldView-2 satellite imagery. The results show that both CNN and RF models can detect shoals with accuracies ranging between 79 and 94% over two study sites; however, WorldView-2 images deliver results with higher accuracy and lower omission errors. The high processing times of using high-resolution imagery and training a deep learning model may not be necessary in order to quickly scan images for shoals; but training a CNN model with a large training set may lead to faster processing times without the need to train individual images. |
| first_indexed | 2024-03-10T19:16:11Z |
| format | Article |
| id | doaj.art-9640b558513c42f0ba8ba46b937b07b2 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T19:16:11Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-9640b558513c42f0ba8ba46b937b07b22023-11-20T03:24:18ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-06-019638310.3390/ijgi9060383Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping CorridorsRené Chénier0Mesha Sagram1Khalid Omari2Adam Jirovec3Canadian Hydrographic Service, 200 Kent Street, Ottawa, ON K1A 0E6, CanadaCanadian Hydrographic Service, 200 Kent Street, Ottawa, ON K1A 0E6, CanadaCanadian Hydrographic Service, 200 Kent Street, Ottawa, ON K1A 0E6, CanadaCanadian Hydrographic Service, 200 Kent Street, Ottawa, ON K1A 0E6, CanadaIn 2014, through the World-Class Tanker Safety System (WCTSS) initiative, the Government of Canada launched the Northern Marine Transportation Corridors (NMTC) concept. The corridors were created as a strategic framework to guide Federal investments in marine transportation in the Arctic. With new government investment, under the Oceans Protection Plan (OPP), the corridors initiative, known as the Northern Low-Impact Shipping Corridors, will continue to be developed. Since 2016, the Canadian Hydrographic Service (CHS) has been using the corridors as a key layer in a geographic information system (GIS) model known as the CHS Priority Planning Tool (CPPT). The CPPT helps CHS prioritize its survey and charting efforts in Canada’s key traffic areas. Even with these latest efforts, important gaps in the surveys still need to be filled in order to cover the Canadian waterways. To help further develop the safety to navigation and improve survey mission planning, CHS has also been exploring new technologies within remote sensing. Under the Government Related Initiatives Program (GRIP) of the Canadian Space Agency (CSA), CHS has been investigating the potential use of Earth observation (EO) data to identify potential hazards to navigation that are not currently charted on CHS products. Through visual interpretation of satellite imagery, and automatic detection using artificial intelligence (AI), CHS identified several potential hazards to navigation that had previously gone uncharted. As a result, five notices to mariners (NTMs) were issued and the corresponding updates were applied to the charts. In this study, two AI approaches are explored using deep learning and machine learning techniques: the convolution neural network (CNN) and random forest (RF) classification. The study investigates the effectiveness of the two models in identifying shoals in Sentinel-2 and WorldView-2 satellite imagery. The results show that both CNN and RF models can detect shoals with accuracies ranging between 79 and 94% over two study sites; however, WorldView-2 images deliver results with higher accuracy and lower omission errors. The high processing times of using high-resolution imagery and training a deep learning model may not be necessary in order to quickly scan images for shoals; but training a CNN model with a large training set may lead to faster processing times without the need to train individual images.https://www.mdpi.com/2220-9964/9/6/383Canadian Hydrographic Serviceartificial intelligencemachine learningEarth ObservationNorthern Low-Impact Shipping Corridorssafety to navigation |
| spellingShingle | René Chénier Mesha Sagram Khalid Omari Adam Jirovec Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors ISPRS International Journal of Geo-Information Canadian Hydrographic Service artificial intelligence machine learning Earth Observation Northern Low-Impact Shipping Corridors safety to navigation |
| title | Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors |
| title_full | Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors |
| title_fullStr | Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors |
| title_full_unstemmed | Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors |
| title_short | Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors |
| title_sort | earth observation and artificial intelligence for improving safety to navigation in canada low impact shipping corridors |
| topic | Canadian Hydrographic Service artificial intelligence machine learning Earth Observation Northern Low-Impact Shipping Corridors safety to navigation |
| url | https://www.mdpi.com/2220-9964/9/6/383 |
| work_keys_str_mv | AT renechenier earthobservationandartificialintelligenceforimprovingsafetytonavigationincanadalowimpactshippingcorridors AT meshasagram earthobservationandartificialintelligenceforimprovingsafetytonavigationincanadalowimpactshippingcorridors AT khalidomari earthobservationandartificialintelligenceforimprovingsafetytonavigationincanadalowimpactshippingcorridors AT adamjirovec earthobservationandartificialintelligenceforimprovingsafetytonavigationincanadalowimpactshippingcorridors |