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This paper aims to detect rice field damage from natural disasters inBangladesh using high-resolution satellite imagery. The authors developedground truth data for rice field damage from the field level. At first, NDVIdifferences before and after the disaster are calculated to identify possiblecrop loss. The areas equal to and above the 0.33 threshold are marked as croploss areas as significant changes are observed. The authors also verified croploss areas by collecting data from local farmers. Later, different bands ofsatellite data (Red, Green, Blue) and (False Color Infrared) are useful todetect crop loss area. We used the NDVI different images as ground truth totrain the DeepLabV3plus model. With RGB, we got IoU 0.41 and with FCI, we gotIoU 0.51. As FCI uses NIR, Red, Blue bands and NDVI is normalized differencebetween NIR and Red bands, so greater FCI's IoU score than RGB is expected. ButRGB 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 truthdeveloped in this paper can be used for segmentation models with very highresolution RGB only images such as Bing, Google etc.

Automatic Detection of Natural Disaster Effect on Paddy Field from Satellite Images using Deep Learning Techniques