Pre-Flood and Post-Flood Classification of Remote Sensed Data-Review of Existing Methods and Future Research Scopes
Keywords:
Pre-Flood, Post Flood, Classification, Flood Mapping, Deep Learning, Machine Learning, Change detection, Remote sensed dataAbstract
There are so many natural disasters and Flood is among that which affects the humankind in large way. Basically, temperate, and tropical regions are the majorly affected area by food. It is harmful for various properties and plants, and also sometimes we loss human lives. So, it is mandatory to identify flood affected area or to get instant data of flood-affected area. Nowadays remote sensing is very popular and appropriate practice to identify flood or flood affected area without close contact of land. Identification of Pre-Flood and Post-Flood images form very large amount of remote sensing data is very critical task. In this paper, we review different recent research papers, realize the gap of knowledge, and discuss future research scope in same area. Here we try to focus on different types of techniques which is used for mapping the flood. In this paper we have compared result of different approaches in terms of accuracy it shows that deep learning models are far better than the traditional method. We can also use other factors apart from the accuracy such as Recall, Precision, F1 factor etc.
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