ProcessingBathymetric Data

Mikko Ojala

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Data courtesy of FTA and Meritaito Ltd

● Examples generated from data provided byFinnish Transport Agency andMeritaito Ltd

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Bathymetric data

● Bathymetric data can be regarded more sensitive than topographic data

1) Results are harder to verify

2) Coastal areas can have military interest

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Processing Bathymetric LiDAR data

● Common practises can be adapted from topographic LiDAR processing

➢ For example low point search and ground classification routines

➢ With a combination of classification routines a good automatic result can be achieved

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Processing Bathymetric LiDAR data

● Automatic vs. manual classification

➢ Automatic classification routines can speed up production but consideration has to be put into the excecution due to the nature of the data

➢ Underwater objects vs. visible objects on land→ no room for error

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Processing Bathymetric LiDAR data

● Example of automated water surface noise classification

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Processing Bathymetric LiDAR data

● Example of automated sea bottom classification

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Processing MBES (sonar) data

● Again some practises can be adapted from topographic (and bathymetric) LiDAR processing

● Deep water vs. shallow water mapping

➢ Deep water datasets can have much less noise when compared to shallow water datasets

➢ Reasons for this can be e.g. water turbidity and bottom material in shallow areas

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Processing MBES (sonar) data

● Deep water automatic MBES data processing:

➢ Most of the data can be regarded as valid sea bottom

→ Classification can focus mainly in finding false echoes and noise points

→ Examples of errors found in MBES data:

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Processing MBES (sonar) data

● Deep water automatic MBES data processing:

➢ It's possible to find a general routine that can be applied to different datasets by adjusting the parameters

➢ Improvement in processing speed of the data can be significant

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Processing MBES (sonar) data

● Shallow water automatic MBES data processing:

➢ Data can be very noisy which makes automatic data processing difficult

→ Even for a human it can be difficult to distinguish features from these datasets

→ Computer algorithms can help in making sense of the data

→ Improving automatic classification result requires more development

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Combining LiDAR and MBES data

● LiDAR

➢ more cost efficient than MBES in shallow areas

➢ able to reach areas where no ship could enter

● MBES ➢ able to reach depths that LiDAR couldn't

➢ less dependent on environmental factors

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Combining LiDAR and MBES data

● Complex areas with LiDAR, rest with MBES

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Combining LiDAR and MBES data

● Important use case of bathymetric LiDAR

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Combining LiDAR and MBES data

● Example of DTM created of MBES and LiDAR data

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Combining LiDAR and MBES data

● Example of DTM created of LiDAR data

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Combining LiDAR and MBES data

● Example of DTM created of MBES and LiDAR data

TerrasolidKanavanranta 7 B 28 00160 HelsinkiFinlandwww.terrasolid.com

Mikko OjalaTechnical Support Engineer