TY - JOUR
T1 - Automatic detection of bulldozer-induced changes on a sandy beach from video using YOLO algorithm
AU - Barbero-García, Inés
AU - Kuschnerus, Mieke
AU - Vos, Sander
AU - Lindenbergh, Roderik
PY - 2023
Y1 - 2023
N2 - Sandy beaches are subject to changes due to multiple factors, that are both natural (e.g. storms) and anthropogenic. Great efforts are being made to monitor these ecosystems and understand their dynamics in order to assure their conservation. The identification of anthropogenic changes and its differentiation from natural ones is an important task for coastal monitoring. In this study, we present a methodology for the detection of anthropogenic changes in a coastal ecosystem by automatically detecting active bulldozers in continuous beach video data. PCA is used to highlight changes in consecutive images due to moving objects. Next, the YOLO object detection algorithm is used to identify the bulldozers in the change images. YOLO was specifically trained for the task, obtaining a precision of 0.94 and a recall of 0.81. An automatic tool was developed, and the process was carried out on two months of video data, consisting of approximately 19 000 images. The resulting information was compared with changes derived from 3D data obtained from a permanent laser scanner. The correlation among the results of the two methodologies was computed. For a validation area and daily time frame a correlation of 0.88 was obtained between the number of detected bulldozers and the area affected by changes in height larger than 0.3 m.
AB - Sandy beaches are subject to changes due to multiple factors, that are both natural (e.g. storms) and anthropogenic. Great efforts are being made to monitor these ecosystems and understand their dynamics in order to assure their conservation. The identification of anthropogenic changes and its differentiation from natural ones is an important task for coastal monitoring. In this study, we present a methodology for the detection of anthropogenic changes in a coastal ecosystem by automatically detecting active bulldozers in continuous beach video data. PCA is used to highlight changes in consecutive images due to moving objects. Next, the YOLO object detection algorithm is used to identify the bulldozers in the change images. YOLO was specifically trained for the task, obtaining a precision of 0.94 and a recall of 0.81. An automatic tool was developed, and the process was carried out on two months of video data, consisting of approximately 19 000 images. The resulting information was compared with changes derived from 3D data obtained from a permanent laser scanner. The correlation among the results of the two methodologies was computed. For a validation area and daily time frame a correlation of 0.88 was obtained between the number of detected bulldozers and the area affected by changes in height larger than 0.3 m.
KW - Anthropogenic changes
KW - Coastal monitoring
KW - Object detection
KW - Principal components analysis
UR - http://www.scopus.com/inward/record.url?scp=85146183518&partnerID=8YFLogxK
U2 - 10.1016/j.jag.2023.103185
DO - 10.1016/j.jag.2023.103185
M3 - Article
AN - SCOPUS:85146183518
VL - 117
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
SN - 0303-2434
M1 - 103185
ER -