A comparative study of different deep learning models for land use and land cover mapping of flood detention basin

Author:

Li Nan,Ma Jianwei,Huang Shifeng,Zhu He,Sun Yayong,Hu Mengcheng

Abstract

Abstract Flood detention basin (FDB) is an important part of the flood control system in the basin. It is of great significance for scientific flood control to obtain the land use and land cover (LULC) classification map of FDB with higher accuracy quickly and accurately. In recent years, deep learning has shown great potential in LULC classification. In this study, we make LULC training dataset and explore three state-of-the-art (SOTA) DL architectures: Unet++, ResUnet++, DeepLab v3+ across Mengwa FDB. The experiments show all methods used in this study are available for LULC classification, which overall accuracy is ResUnet++(95.11%), Unet++(91.92%), DeepLab v3+ (91.04%) and kappa coefficient is ResUnet++(0.91), Unet++(0.85), DeepLab v3+ (0.83). Deep learning methods can be used for automatic extraction of LULC from high-resolution satellite images, providing data support for economic loss assessment and post disaster construction in flood storage areas.

Publisher

IOP Publishing

Subject

General Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Overview of Bio-Inspired and Deep Learning Model for Extraction of Land Use Pattern;2023 6th International Conference on Information Systems and Computer Networks (ISCON);2023-03-03

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