Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data

Abstract

Urbanization is a substantial contributor to anthropogenic environmental change, and often occurs at a rapid pace that demands frequent and accurate monitoring. Time series of satellite imagery collected at fine spatial resolution using stable spectral bands over decades are most desirable for this purpose. In practice, however, temporal spectral variance arising from variations in atmospheric conditions, sensor calibration, cloud cover, and other factors complicates extraction of consistent information on changes in urban land cover. Moreover, the construction and application of effective training samples is time-consuming, especially at continental and global scales. Here, we propose a new framework for satellite-based mapping of urban areas based on transfer learning and deep learning techniques. We apply this method to Landsat observations collected during 1984-2016 and extract annual records of urban areas in four cities in the temperate zone (Beijing, New York, Melbourne, and Munich). The method is trained using observations of Beijing collected in 1999, and then used to map urban areas in all target cities for the entire 1984-2016 period. The method addresses two central challenges in long term detection of urban change: temporal spectral variance and a scarcity of training samples. First, we use a recurrent neural network to minimize seasonal urban spectral variance. Second, we introduce an automated transfer strategy to maximize information gain from limited training samples when applied to new target cities in similar climate zones. Compared with other state-of-the-art methods, our method achieved comparable or even better accuracy: the average change detection accuracy during 1984-2016 is 89% for Beijing, 94% for New York, 93% for Melbourne, and 89% for Munich, and the overall accuracy of single-year urban maps is approximately 96 ± 3% among the four target cities. The results demonstrate the practical potential and suitability of the proposed framework. The method is a promising tool for detecting urban change in massive remote sensing data sets with limited training data.

Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images

“…; Lyu et al. ) also show high performance and can cover larger areas. However, in the case of a regional study using one or two very high resolution Worldview images and with the objective of mapping objects inside a particular vegetation class, such as in this study, we recommend producing the vegetation class model with U‐net.…”

“…We acknowledge that the U-net model is not necessarily the best method to produce vegetation/land cover mapping larger than the regional scale. Recent work using mainly Landsat images and non-deep learning methods (Hansen et al 2013;MapBiomas, 2018) or other deep learning methods (Jia et al 2017;Kussul et al 2017;Lyu et al 2018) also show high performance and can cover larger areas. However, in the case of a regional study using one or two very high resolution Worldview images and with the objective of mapping objects inside a particular vegetation class, such as in this study, we Figure 10.…”

Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

“…For example, Lyu et al [53] employed RNN to use sequential properties such as spectral correlation and intra-bands variability of multispectral data. They further used the LSTM model to learn a combined spectral-temporal feature representation from an image pair acquired at two different dates for change detection [54].…”

Building Instance Classification using Social Media Images

“…Since labeled data in remote sensing on deep learning is very scarce, Marmanis et al [9] used features extracted from pre-trained networks on ImageNet [10] to classify aerial images. Beside CNNs, recurrent neural networks and generative adversarial networks are also employed for different remote sensing tasks, for example in [11]- [13].…”