Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

Abstract

ABSTRACT:With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

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“…TensorFlow is an open-source machine learning framework developed by Google. It can be used for Federated learning in the context of smart cities, enabling the use of the Federated approach for various applications such as traffic optimization and environmental monitoring [173] . The data stays stored on the local devices, while only the changes to the model are exchanged.…”

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“…TensorFlow also offers different operations that are suitable for Neural Networks such as softmax, sigmoid, relu, convolution2D and maxpool [26]. Thus, this framework is used in different applications such as image processing, image classification, object detection and semantic segmentation [27]. remote sensing image classification.…”

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“…Moreover, auto-encoder is a neural network model of data-driven and unsupervised learning for improving satellite remote sensing tasks [55]. Such as classification [56] and object understanding because of the superior multiple-layer channel interconnection with high indepth learning features and classifiers from different architecture. Machine learning and deep learning methods can be applied for complex tasks such as hyperspectral anomaly detection [57] and light detection and ranging (LiDAR) data for land cover classification tasks [58].…”