Boulder detection | 
MBES Deptr 
VMBES combined 
MBES Sie 
BES. BS 
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Fig. 8: Test area composed of fine sediments with a marked impact of bottom trawling activity. 
Because of the fine sediment composition, it can be assumed no boulders are present in this area. 
he model working on slope data detects several false positives in the area, while models running 
on backscatter, depth and the combined multi-band image report no false positives. Refer to the 
northwest of Fig. 2 for location 
0 75 15m 
X 
a 
in acoustic data, and - except for obvious instanc- 
as - the interpretation of a human interpreter of 
what is and what is not a boulder varies based on 
nis/her experience, with no possibility to Judge 
what is the correct interpretation. The appearance 
and visibility of boulders in backscatter data can 
Change with swath width and incidence angle (Pa- 
ogenmeier et al. 2020; von Rönn et al. 2019). While a 
methodological description on how to assess geo- 
genic reefs exists (Heinicke et al., in press), it de 
fines no sufficient criteria to decide which objects 
are to be identified as boulders in acoustic data. 
Still, our case study allows qualitative insight 
'nto the advantages and disadvantages of 5SS 
and MBES-based boulder mapping by neural net 
works. To mitigate the impact on AP for the dif 
ferent models, a single person confirmed all sam- 
oles in the training database used for this study. 
"herefore, model performance is only compared 
relative to the interpretation of the acoustic data 
py one human expert and not to the true seafloor 
zonditions. Both SSS and MBES systems supply 
packscatter information. A problem of 5SS-based 
boulder detection are artefacts (Wilken et al. 2012), 
2.g., near the nadir or in areas of water column 
stratification that can in their structure resemble 
small boulders (Fig. 6). Due to the requirements to 
detect tiny objects comprising only 7 to 9 pixels in 
the examples shown here and even less if objects 
af 25 cm in size are to be detected in acoustic data 
(von Rönn et al. 2019), there is limited information 
to differentiate between artefacts and real objects. 
This causes a trade-off during the training of side- 
scan sonar-based models: if the sensitivitv of the 
mNodel to detect small boulders — as required by 
egulations — is increased, the amount of false 
Dositive identifications increases as well. Because 
af the absence of well ground-truthed reference 
;ites, a calculation of meaningful precision-recall 
zurves to find optimal threshold values is not pos- 
sible. Tuning the threshold level of the model to 
'he local conditions (e.g., the number of artefacts 
n the data) is done manually, which is a subjec- 
“ive procedure. A possible solution is to include 
ıadir and water column stratification effects as 
Jistinct classes and define these areas as insuffi- 
zient for boulder detection. While MBES snippet 
Jderived backscatter data is not affected by water 
zolumn stratification and is used for object detec 
ion (e.g., Kunde et al. 2018), individual boulders 
are not displayed in the specular regime (Fig. 7) at 
ıear-vertical incidence angle and are resolved in 
ess detail compared to side-scan sonar images in 
"he data (Fig. 2). The loss of detail may be caused 
>»y a different along-track resolution due to dif 
‘erent opening angles of the used systems (0.5‘ 
at 400 kHz for the Reson 7125, CSS-2000: 0.26° 
at 600 kHz, respectively 0.29° at 410 kHz for the 
4300 MPX). Combined with the less pronounced 
3coustic shadows, the AP of the MBES backscatter 
nodel data set, therefore, is worse compared to 
“he model trained on side-scan sonar backscatter 
data (Table 1). MBES-based backscatter maps can 
10t be recommended as the principal data source 
‘or boulder detection based on our case study. 
An obvious problem related to the use of MBES 
Dathymetry and derived slope values is the re- 
quired thorough cleaning of the data, with outli- 
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