Abstract
Prompt updates of land cover maps are important, as spatial information of land cover is widely used in many areas. However, current manual digitizing methods are time consuming and labor intensive, hindering rapid updates of land cover maps. The objective of this study was to develop an artificial intelligence (AI) based land cover classification model that allows for rapid land cover classification from high-resolution remote sensing (HRRS) images. The model comprises of three modules: pre-processing, land cover classification, and post-processing modules. The pre-processing module separates the HRRS image into multiple aspects by overlapping 75% using the sliding window algorithm. The land cover classification module was developed using the convolutional neural network (CNN) concept, based the FusionNet network and used to assign a land cover type to the separated HRRS images. Post-processing module determines ultimate land cover types by summing up the separated land cover result from the land cover classification module. Model training and validation were conducted to evaluate the performance of the developed model. The land cover maps and orthographic images of 547.29 km2 in area from the Jeonnam province in Korea were used to train the model. For model validation, two spatial and temporal different sites, one from Subuk-myeon of Jeonnam province in 2018 and the other from Daseo-myeon of Chungbuk province in 2016, were randomly chosen. The model performed reasonably well, demonstrating overall accuracies of 0.81 and 0.71, and kappa coefficients of 0.75 and 0.64, for the respective validation sites. The model performance was better when only considering the agricultural area by showing overall accuracy of 0.83 and kappa coefficients of 0.73. It was concluded that the developed model may assist rapid land cover update especially for agricultural areas and incorporation field boundary lineation is suggested as future study to further improve the model accuracy.
Highlights
Land cover changes continuously as the urbanization progresses, and the land use changes affect various aspects including human behavior, ecosystem, and climate [1,2,3]
Research has been conducted on a variety of land cover classification methods for producing highly accurate land cover maps using remote sensing data
The R, G, B values of classified image pixels are the output by the classification model that were trained with land cover map color
Summary
Land cover changes continuously as the urbanization progresses, and the land use changes affect various aspects including human behavior, ecosystem, and climate [1,2,3]. Recent advancements in remote sensing technology enable us to acquire high-resolution remote sensing (HRRS) images of extensive area This increases their potential to be used as basic data for creating a highly accurate real-time land cover map [10]. To produce accurate land cover maps rapidly, studies have been conducted utilizing mathematical algorithms and artificial neural network (ANN). Several CNN models are applied on land cover classification, which segments land use per each pixel of the entire image, using different training data sets. As the neural network deepens, a gradient vanishing problem occurs To solve this issue, we used the summation-based skip connection in the residual layer, which integrates the past data with the currently processing data in the module, and configured the layer to enable a more exquisite classification [36]. Four long skip connections connect the layer of the encoding path to the decoding path, which are the same size
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