Land cover classification in Thailand's Eastern Economic Corridor (EEC) using convolutional neural network on satellite images

Abstract

Land cover plays an integral role in urban management as a source of information to support authorities’ decision making. Recently, computer vision methods, machine learning algorithms in particular, are increasingly used for land cover and land use mapping, which can help make these processes more efficient and more affordably. To this end, this paper focuses on leveraging artificial intelligence using convolutional neural network (CNN) to propose a new method for land cover mapping. A first CNN model is trained with a large number of 4 image classes to obtain a land cover model. We then apply the model directly on satellite images that are cropped into images of the same size as the training images. The results of the direct application of the model is not satisfactory; in particular, it confuses water and forested areas. The paper propose a different approach where land cover model is obtained by combining 2 models in series. The first model is a binary-class CNN model which contains classification of two classes (water, land) while the second model is a three-class CNN model containing classification of land, excluding non-water areas. Global accuracy obtained is 98% and 91% for the binary- and three-class CNN model respectively. This approach is used successfully on large area satellite images.