  There is a small number of case studies of automatic land cover
classification on the coastal area. Here, I test extraction of seagrass beds,
sandy area, oyster farming rafts at Mangoku-ura Lagoon, Miyagi, Japan by
comparing manual tracing, simple image segmentation, and image transformation
using deep learning. The result was used to extract the changes before and
after the earthquake and tsunami. The output resolution was best in the image
transformation method, which showed more than 69% accuracy for vegetation
classification by an assessment using random points on independent test data.
The distribution of oyster farming rafts was detected by the segmentation
model. Assessment of the change before and after the earthquake by the manual
tracing and image transformation result revealed increase of sand area and
decrease of the vegetation. By the segmentation model only the decrease of the
oyster farming was detected. These results demonstrate the potential to extract
the spatial pattern of these elements after an earthquake and tsunami. Index
Terms: Great East Japan Earthquake of 2011, Land use land cover (LULC),
Zosteracea seagrass, cultured oyster, deep learning, Mangoku Bay
