Fuzzy machine learning model for class-based flood damage assessment from planetscope temporal data

. Natural disasters are calamitous events with causes and effects that need to be examined. Among them, floods are one of the most devastating natural disasters that demand constant monitoring. The flood in Pakistan in 2022 caused massive destruction of lives and property due to heavy rainfall. The utilization of remote sensing technology is crucial for managing these disastrous events. This research was carried out to map class-level damages caused by the 2022 Pakistan flood using the fuzzy-based modified possibilistic c-means (MPCM) classifier with the individual sample as mean (ISM) training approach. This study targeted seven different flood-affected classes: fields with crop submerged by August 8 and 29, 2022; fields with crop and later fallow submerged by August 8 and 29 2022; open areas with vegetation submerged by August 29, 2022; roads and parks submerged by August 29, 2022; and ponds submerged by August 29, 2022. The temporal class-based sensor independent normalized difference vegetation index (CBSI-NDVI) database prepared using pre-flood, flood, and post-flood images of PlanetScope was used for this research work. The classified outputs were evaluated using Mean Membership Difference (MMD) and F-score. Based on the MMD values of 0.01 to 0.02, and an F-score of nearly 1, the classifier indicated a good classification result. This study claimed that the MPCM algorithm with the ISM training approach handled heterogeneity within the class and produced effective flood-based damage extraction results. Furthermore, it was found that the utilization of temporal images reduced the spectral overlap between classes. This research offers insights to study and better understand the applicability of the MPCM algorithm with the ISM training approach in disaster studies. Moreover, this technique of assessing damaged classes is an ingenious single-step class-specific approach performed with optimized temporal images.