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Utilizing Deep Learning and Object-Based Image Analysis to Search for Low-Head Dams in Indiana, USA.

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Title: Utilizing Deep Learning and Object-Based Image Analysis to Search for Low-Head Dams in Indiana, USA.
Authors: Crookston, Brian M., Arnold, Caitlin R.
Source: Water (20734441); Mar2025, Vol. 17 Issue 6, p876, 24p
Subject Terms: IMAGE analysis, WATER supply, DAMS, MODEL validation, LEARNING ability
Abstract: Although low-head dams in the USA provide water supply, irrigation, and recreation opportunities, many are unknown by regulators. Unfortunately, hundreds of drownings occur each decade at these dams from an entrapment current that can form immediately downstream. To explore the ability of deep learning to scan large areas of terrain to identify the locations of low-head dams, ArcGIS Pro and embedded deep learning models for object-based image analysis were investigated. The State of Indiana low-head dam dataset was selected for model training and validation. Aerial imagery (leaf-off conditions) captured from 2016 to 2018 for the nearly 94,000 km2 area had a minimum resolution of 304.8 mm. A new Python code was developed that automated the generation of training images and searching was limited to 100 m wide river corridors. Due to bank vegetation, all low-head dams were assigned a visibility score to aid in training and performance analysis. A total of 19 backbone models were considered with single shot detection and options for RetinaNet, Faster R-CNN, and batch normalization. Additional identification classes were incorporated to overcome identification of visually similar objects. After four training iterations, the final trained model was a ResNet RetinaNet backbone model featuring 101 layers with an 83% recall rate for dams with high visibility and a 17% recall rate for those with moderate visibility. [ABSTRACT FROM AUTHOR]
Copyright of Water (20734441) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Biomedical Index
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  Data: Utilizing Deep Learning and Object-Based Image Analysis to Search for Low-Head Dams in Indiana, USA.
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  Data: Water (20734441); Mar2025, Vol. 17 Issue 6, p876, 24p
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  Data: <searchLink fieldCode="DE" term="%22IMAGE+analysis%22">IMAGE analysis</searchLink><br /><searchLink fieldCode="DE" term="%22WATER+supply%22">WATER supply</searchLink><br /><searchLink fieldCode="DE" term="%22DAMS%22">DAMS</searchLink><br /><searchLink fieldCode="DE" term="%22MODEL+validation%22">MODEL validation</searchLink><br /><searchLink fieldCode="DE" term="%22LEARNING+ability%22">LEARNING ability</searchLink>
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  Data: Although low-head dams in the USA provide water supply, irrigation, and recreation opportunities, many are unknown by regulators. Unfortunately, hundreds of drownings occur each decade at these dams from an entrapment current that can form immediately downstream. To explore the ability of deep learning to scan large areas of terrain to identify the locations of low-head dams, ArcGIS Pro and embedded deep learning models for object-based image analysis were investigated. The State of Indiana low-head dam dataset was selected for model training and validation. Aerial imagery (leaf-off conditions) captured from 2016 to 2018 for the nearly 94,000 km<superscript>2</superscript> area had a minimum resolution of 304.8 mm. A new Python code was developed that automated the generation of training images and searching was limited to 100 m wide river corridors. Due to bank vegetation, all low-head dams were assigned a visibility score to aid in training and performance analysis. A total of 19 backbone models were considered with single shot detection and options for RetinaNet, Faster R-CNN, and batch normalization. Additional identification classes were incorporated to overcome identification of visually similar objects. After four training iterations, the final trained model was a ResNet RetinaNet backbone model featuring 101 layers with an 83% recall rate for dams with high visibility and a 17% recall rate for those with moderate visibility. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Water (20734441) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3390/w17060876
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 24
        StartPage: 876
    Subjects:
      – SubjectFull: IMAGE analysis
        Type: general
      – SubjectFull: WATER supply
        Type: general
      – SubjectFull: DAMS
        Type: general
      – SubjectFull: MODEL validation
        Type: general
      – SubjectFull: LEARNING ability
        Type: general
    Titles:
      – TitleFull: Utilizing Deep Learning and Object-Based Image Analysis to Search for Low-Head Dams in Indiana, USA.
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            NameFull: Crookston, Brian M.
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            NameFull: Arnold, Caitlin R.
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              M: 03
              Text: Mar2025
              Type: published
              Y: 2025
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              Value: 17
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