Multi-label Land Cover Indices Classification of Satellite Images Using Deep Learning

Aung, Su Wit Yi; Khaing, Soe Soe; Aung, Shwe Thinzar

doi:10.1007/978-981-13-0869-7_11

Cited by 8 publications

(1 citation statement)

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“…The accuracy of LUCC is the basis for studying the changes and mechanisms of LUCC and is a prerequisite for the scientific application of LUCC. The lack of an integrated model for accurate annual reconstruction and prediction makes it difficult to provide scientific data support for land planning and global environmental change research [27]. In this context, exploring a novel modeling method to solve these problems is an urgent need.…”

Section: Introductionmentioning

confidence: 99%

An Integrated CNN Model for Reconstructing and Predicting Land Use/Cover Change: A Case Study of the Baicheng Area, Northeast China

et al. 2021

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Land use and land cover change (LUCC) modeling has continuously been a major research theme in the field of land system science, which interprets the causes and consequences of land use dynamics. In particular, models that can obtain long-term land use data with high precision are of great value in research on global environmental change and climate impact, as land use data are important model input parameters for evaluating the effect of human activity on nature. However, the accuracy of existing reconstruction and prediction models is inadequate. In this context, this study proposes an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model (CLRPM), which meets the demand for fine-scale LUCC reconstruction and prediction. This model applies the deep learning method, which far exceeds the performance of traditional machine learning methods, and uses CNN to extract spatial features and provide greater proximity information. Taking Baicheng city in Northeast China as an example, we verify that CLRPM achieved high-precision annual LUCC reconstruction and prediction, with an overall accuracy rate 9.38% higher than that of the existing models. Additionally, the error rate was reduced by 49.5%. Moreover, this model can perform multilevel LUCC classification category reconstructions and predictions. This study casts light on LUCC models within the high-precision and fine-grained LUCC categories, which will aid LUCC analyses and help decision-makers better understand complex land-use systems and develop better land management strategies.

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Section: Introductionmentioning

confidence: 99%