  This paper aims to detect rice field damage from natural disasters in
Bangladesh using high-resolution satellite imagery. The authors developed
ground truth data for rice field damage from the field level. At first, NDVI
differences before and after the disaster are calculated to identify possible
crop loss. The areas equal to and above the 0.33 threshold are marked as crop
loss areas as significant changes are observed. The authors also verified crop
loss areas by collecting data from local farmers. Later, different bands of
satellite data (Red, Green, Blue) and (False Color Infrared) are useful to
detect crop loss area. We used the NDVI different images as ground truth to
train the DeepLabV3plus model. With RGB, we got IoU 0.41 and with FCI, we got
IoU 0.51. As FCI uses NIR, Red, Blue bands and NDVI is normalized difference
between NIR and Red bands, so greater FCI's IoU score than RGB is expected. But
RGB does not perform very badly here. So, where other bands are not available,
RGB can use to understand crop loss areas to some extent. The ground truth
developed in this paper can be used for segmentation models with very high
resolution RGB only images such as Bing, Google etc.
