Detecting global urban expansion over the last three decades using a fully convolutional network

The effective detection of global urban expansion is the basis of understanding urban sustainability. We propose a fully convolutional network (FCN) and employ it to detect global urban expansion from 1992–2016. We found that the global urban land area increased from 274.7 thousand km2–621.1 thousand km2, which is an increase of 346.4 thousand km2 and a growth by 1.3 times. The results display a relatively high accuracy with an average kappa index of 0.5, which is 0.3 higher than those of existing global urban expansion datasets. Three major advantages of the proposed FCN contribute to the improved accuracy, including the integration of multi-source remotely sensed data, the combination of features at multiple scales, and the ability to address the lack of training samples for historical urban land. Thus, the proposed FCN has great potential to effectively detect global urban expansion.


Introduction
Urban expansion represents the transformation of non-urban land to urban land, which results in increases of the areas of cities . In the context of rapid urban population growth and socioeconomic development, the world has undergone large-scale urban expansion (Wu 2014, Angel et al 2016. Between 2000-2010, the global urban land area increased from 0.6 million km 2 to 0.7-0.9 million km 2 , and these values reflect growth rates of 18.1%-44.2% (Angel et al 2011). By 2050, the global urban land area is projected to expand to 1.2-3.1 million km 2 , which is 1.6-3.6 times the global urban land area in 2010 (Angel et al 2011). Global urban expansion has caused many ecological and environmental issues, such as losses of biodiversity, increases in carbon emissions, water scarcity, regional warming, and environmental pollution (Grimm et al 2008, McDonald et al 2008, Seto et al 2012, Liu et al 2016b, Bren d'Amour et al 2017, van Vliet et al 2017. To evaluate such effects of urban expansion, reliable information on global urban expansion is indispensable . Thus, how to effectively detect global urban expansion has become a crucial issue in understanding urban sustainability (Zhang and Seto 2011, Wu 2014, Angel et al 2016. At present, data on global urban expansion come from eight widely referenced sources (table 1). These data represent global urban expansion during six periods, 10000 BC-2016BC- , 1975BC- -2014BC- /2015BC- , 1990BC- -2010BC- , 1992BC- -2015BC- , 2000BC- -2010BC- and 2010BC- -2017 (table 1). Of these sources, the HYDE describes global urban expansion from 10000 BC-2016(Klein Goldwijk et al 2010. The GHS data reveal global urban expansion from 1975-2014/2015(Pesaresi et al 2013. The MGIS data represent global urban expansion from 1990-2010 (Liu et al 2018). The global land cover data derived from the ESACCI describe global urban expansion from 1992-2015 (UCL-Geomatics 2017). The GLC30 dataset and the IMPSA dataset represent global urban expansion between 2000-2010 (Elvidge et al 2007, Chen et al 2015. The FROM-GLC data reveal global urban expansion from 2010-2017 (Gong et al 2013). These data have been widely used to understand the ecological and environmental effects of urban expansion across the world (Klein Goldwijk et al 2010, Pesaresi et al 2013, UCL-Geomatics 2017. However, the HYDE data have a low resolution of 10 km and thus do not provide adequate spatial information. The GHS data, the FROM-GLC data and the ESACCI data are limited by their low accuracy; the kappa value of the GHS data is approximately 0.3 (Pesaresi et al 2016), the overall accuracy (OA) of the FROM-GLC data is approximately 60% (Gong et al 2013), and the producer's accuracy of the ESACCI data is approximately 51% (UCL-Geomatics 2017). The MGIS, GLC30 and IMPSA datasets lack information on global urban expansion after 2010. Thus, developing effective methods of detecting global urban expansion is still worthwhile (Esch et al 2017).
Fully convolutional network (FCN)-based methods provide a novel approach to the detection of global urban expansion. The FCN is a deep learning structure that employs the 'pixel-to-pixel' image recognition mode developed by Long et al (2015) on the basis of the convolutional neural network (CNN). FCN-based models inherit the basic structure of CNN; their primary components include convolutional layers, pooling layers, activation layers, and concatenation layers (Long et al 2015). Of these layers, convolutional layers can identify the local features of images; pooling layers compress numerous features to extract the main features; activation layers effectively express nonlinear features; and concatenation layers integrate features over multiple scales (LeCun et al 1990, 1998, 2015, Szegedy et al 2014. Through transmitting information between multiple convolutional layers, pooling layers, activation layers and concatenation layers, FCNs effectively integrate remote sensing data from multiple sources and features over multiple scales to identify objects accurately (Chen et al 2017, Yang et al 2017. Furthermore, FCNs have the ability to transfer leaning, which enables the extraction of dynamic information using parameters obtained from training samples in a single period. Recently, researchers have started to use FCNbased methods to detect urban land at different scales. For example, Maggiori et al (2016) employed FCNbased methods to quantify the patterns of urban land in Boston, United States, whereas Fu et al (2017) applied FCN-based methods to extract urban land in the northeastern parts of Beijing, China. However, FCNs have not been used to detect global urban expansion.
Our objective is to propose a new FCN to detect global urban expansion from 1992 to 2016 at 1 km. To achieve this goal, we first develop an FCN to integrate remotely sensed data from multiple sources effectively. We then use the proposed method to extract the global urban land in 1992, 1996, 2000, 2006, 2010, and 2016 using nighttime light (NTL) data, normalized difference vegetation index (NDVI) data, and land surface temperature (LST) data. Our method provides an effective means of detecting global urban expansion over large scales.
2. Study area and data 2.1. Study area Our study area is located from 65°S-75°N latitude worldwide, identical to the areal coverage of the NTL data (figure 1). This zone contains 0.13 billion km 2 of land surface area and accounts for 89.26% of the global land surface area. It includes all of Asia, Africa, Latin America and the Caribbean, and Oceania, and most of Europe and Northern America (figure 1).

Data
This study makes use of several data sources with global coverage, specifically the NTL, NDVI, and LST data mentioned above, to detect urban expansion. The 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Urban Land Cover (MOD500) data are used to select the training samples. Urban population statistics, Landsat images, and existing GHS data and the ESACCI global land cover data were also obtained.
The NTL data were obtained from the NOAA/ NGDC (http://ngdc.noaa.gov/eog, accessed 20 September 2017). These data were collected by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) and include uncalibrated NTL data acquired in 1992 and radiance-calibrated NTL data from 1996, 2000, 2006, and 2010 at a resolution of 1 km. The monthly NTL data from 2016 at a resolution of 742 m acquired by the Suomi National Polar-orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) were also obtained. The approach introduced by Elvidge et al (2009) is applied to intercalibrate the uncalibrated NTL data collected in 1992 to improve their continuity and comparability, and the approach developed by Elvidge et al (2013) is performed to produce the annual mean value composite of the 2016 VIIRS NTL data. To reduce the influence of gas flares, we follow Zhou et al (2014Zhou et al ( , 2015 to exclude gas flare pixels from the NTL data using the gas flare mask obtained from NOAA/NGDC. The NDVI data include the MODIS NDVI data for 2000, 2006, 2010, and 2016 and the NOAA-AVHRR NDVI data for 1992 and 1996 at a resolution of 1 km. The MODIS NDVI data were obtained from the MODIS 16 d composite products (http://ladsweb. nascom.nasa.gov, accessed 20 September 2017). The NOAA-AVHRR NDVI data were obtained from the USGS website (https://lta.cr.usgs.gov/NDVI, accessed 20 September 2017). Using the maximum value composite (MVC) approach introduced by Holben (1986), an annual MVC of the NDVI data for each year is produced to reduce cloud contamination.
The LST data in 2000LST data in , 2006LST data in , 2010, and 2016 at a resolution of 1 km are derived from the MODIS eightday composite product (MOD11A2) (http://ladsweb. nascom.nasa.gov, accessed 20 September 2017). The nighttime LST data in this product are used because the LST in nighttime generally has better performance for distinguishing between urban and non-urban land (Buyantuyev andWu 2010, Zakšek andOštir 2012). Consistent with Mildrexler et al (2009), an annual MVC of the LST data for each year is also produced.
The MOD500 data used to select training samples were obtained from the Center for Sustainability and the Global Environment at the University of Wisconsin-Madison (http://www.sage.wisc.edu/, accessed 20 September 2017). The MOD500 data are produced through supervised classification of MODIS multispectral data circa 2001 (Schneider et al , 2010. The data have an OA of 93%, which is a relatively accurate representation of the global urban land area (Schneider et al , 2010. All of the NTL, NDVI, LST, and MOD500 data are resampled to a spatial resolution of 1 km. In addition, all of the data layers are geometrically corrected with reference to the MOD500 data.
In addition, the statistical data on the global urban population used for the accuracy assessment were obtained from the World Urbanization Prospects

Developing the FCN
Following the basic FCN framework presented by Long et al (2015), we develop a new FCN for detecting global urban expansion (figure 2). The FCN mainly includes one input layer, one pooling layer, three convolutional layers, one concatenation layer, and one output layer, and each convolutional layer follows an activation layer ( figure 2(b)). The input layer is used to integrate the NTL, NDVI, and LST data to provide both socioeconomic and physical information. Longitudes and latitudes are also entered to provide information on the location of urban land. The convolutional and pooling layers are used to obtain feature information from the data at the pixel, neighborhood and urban region scales. The concatenation layer is used to integrate the feature information at the three scales to identify urban land (figure 2(b)).
When extracting global urban land in 2000 and after, the input layers include five bands, specifically NTL, NDVI, LST, longitude, and latitude. However, when extracting urban land for 1992 and 1996, the input layer includes only four bands, specifically NTL, NDVI, longitude, and latitude. That is, the MODIS LST data are unavailable.
The three convolutional layers and the pooling layer employ different numbers and sizes of convolutional kernels to obtain information on urban land at three scales of pixel, neighborhood and urban region. Both the numbers and sizes of the convolutional kernels are determined following previous studies and trial-and-error procedures (LeCun et al 1990, Szegedy et al 2014, Long et al 2015. The first convolutional layer contains 80 convolutional kernels with a size of 1×1 pixel and is used to obtain information at the pixel scale. Its relationship with the input layer can be represented by the equation (1): where Conv i j , 1 and Input I j , denote the DN values in the first convolution layer and the input layer respectively; i and I indicate the band; j indicates the pixel; W I i , 1 is the weight of this convolution layer; Bias i 1 is the bias; and N 0 is the number of bands.
The second convolutional layer contains 40 convolutional kernels with a size of 3×3 pixels and is used to obtain information at the neighborhood scale. Its relationship with the input layer can be represented by the equation (2):   is the weight of this convolution layer; Bias i 2 is the bias; and K 1 is the size of the neighborhood.
The third convolutional layer (which contains 20 convolutional kernels with a size of 1×1 pixel) and a pooling layer with a kernel size of 51×51 pixels are combined to obtain information at the scale of urban regions. The kernel size of 51×51 pixels used in the pooling layer is determined based on the largest urban region in the world (Angel et al 2016). The relationship between the third convolutional layer and the pooling layer can be represented by the equations (3), (4): where Conv i j , 3 and Pool I j , denote the DN values in the third convolution layer and the pooling layer respectively; W I i , 3 is the weight of this convolution layer; where Input I j g , , denotes the DN value of the gth pixel within the urban region of the jth pixel in the Ith band in the input layer; and K 2 denotes the size of the urban region.
The concatenation layer is used to integrate the information obtained by the three convolutional layers, and their relationships can be represented by the equation (5): where Conc i j , denotes the DN value in the concatenation layer, and ( ) f x denotes the activation function, which can be represented by the equation (6) The relationship between the concatenation layer and the output layer can be represented by the equation (7): where Urban j denotes the class value in the output layer, in which 1 represents urban land whereas 0 represents non-urban land. Output j urban and -Output j non urban denote the probability that the jth pixel represents urban land or non-urban land respectively. These probabilities can be calculated using the following equations:

Calibrating the FCN
The objective of calibration is to obtain the weights used in the FCN. Essentially, calibration involves iteratively estimating these weights using optimization algorithms with the goal of minimizing the loss function. This process includes three main steps: selecting training samples, determining the loss function, and setting the optimization algorithm and iteration parameters. First, we select training samples that represent urban and non-urban land from the MOD500 data and NTL data that cover the same period. The selection of training samples can be calculated using the equation (10): where Train i and Mod i denote the class of the ith pixel in the training sample data and the MOD500 data respectively. A value of 1 denotes urban land, a value of 0 denotes non-urban land, and a value of −1 denotes the background, which is not included in the calibration to improve efficiency. PUL i denotes the class value in a potential urban land data, which takes values of 1 (potential urban land) or 0 (not potential urban land). The pixels with DN values greater than 1 nW cm −2 sr −1 in the NTL data were recognized as potential urban land (Dou et al 2017). Further, we calculate the loss function after the output layer based on the equation (11) where z denotes the inputs to the loss function; ( ) l y z , denotes the loss function of z; z j denotes the result of estimating the jth class; and y denotes the class of z in the training samples.
Finally, following Long et al (2015), we utilize the stochastic gradient descent algorithm to perform the optimization. The iteration step size is 1×10 -7 , and the maximum number of iterations is 1 million. The parameters of gamma and momentum both are 0.99. The weight decay is 0.0005.

Detecting global urban expansion from 1992-2016
First, we calibrate four FCNs with different remote sensing data using the Caffe deep learning platform so that the urban land at different times can be detected effectively . Specifically, the first FCN applied for detecting urban land in 1992, is calibrated using the intercalibrated DMSP-OLS NTL data. The second FCN applied for detecting urban land in 1996, is calibrated on the basis of the radiance-calibrated DMSP-OLS NTL data. The third FCN applied for detecting urban land in 2000, 2006, and 2010, is calibrated on the basis of the radiance-calibrated DMSP-OLS NTL and LST data. The fourth FCN applied for detecting urban land in 2016, is calibrated on the basis of the NPP-VIIRS NTL and LST data. All of the FCNs involve NDVI, longitude, and latitude. We then use the calibrated FCNs to extract the global urban land for 1992, 1996, 2000, 2006, 2010, and 2016, respectively. We follow this step with the post-classification processing described below to ensure that the extracted global urban lands in different years are comparable. Because urban land has relatively high population densities, stronger NTL, and lower NDVI values, the areas of urban expansion generally show increased population densities and NTL and decreased NDVI values (Elvidge et al 2007. In contrast, areas that have been transformed from urban land to non-urban land usually show decreased population densities and NTL and increased NDVI values. Therefore, we correct the extracted global urban land using the NTL, NDVI, and population densities from the HYDE dataset. The procedure can be represented by equation (12):   Figure 4. An accuracy assessment using finer-resolution remote sensing data. * The ten selected cities with populations above 10 million are listed alphabetically by names. Please refer to table 2 for the accuracy associated with all of the selected cities.

Accuracy assessment
We first evaluate the estimated urban expansion area resulting from this study at the national level using population census data (Sutton 2003, Zhang and. Using census data provided by the United Nations, we analyze the relationships between the estimated growth of urban land and the growth of urban population at the national level for 1992-2000, 2000-2010, 2010-2016, and 1992-2016. The estimated urban land expansion is strongly correlated (R 2 0.5) with the census data at a statistically significant level of 0.01 ( figure 3).
Following the procedure presented by Potere et al (2009), we then use finer-resolution remote sensing data (Landsat imagery with a 30 m resolution) to evaluate the estimated urban expansion ( figure 4). During this process, we select a total of 34 cities as samples that represent various levels of socioeconomic conditions in different regions using the stratified sampling approach introduced by Angel et al (2016) based on urban population and location (figure 1). The selected cities include two cities from group 1 (with populations greater than 10 million), two cities from group 2 (with populations between 1 million-10 million), and two cities from group 3 (with populations less than 1 located in each of the five following regions: Asia, Africa, Europe, Northern America, and Latin America and the Caribbean. We also selected two cities from group 2 and two cities from group 3 in Oceania, where no city has a population greater than 10 million (figure 1). We then extract the areas of urban expansion between 1992-2016 for the selected cities through visual interpretation of the Landsat images as reference data. In comparing the estimated urban expansion data obtained using the FCN and the reference data derived from the Landsat imagery, we calculate the OA, the kappa coefficient, the commission error (CE), and the omission error (OE) according to Olofsson et al (2014) (table 2, figure 4) In addition, we compare the accuracy of our results with that of existing global urban expansion data with reference to Schneider et al (2010) and Zhou et al (2015). Among the six existing datasets that contain global urban expansion data (table 1), we first select the GHS built-up data, the GHS SMOD data, and the ESACCI data for comparison; these datasets have spatial resolutions and temporal coverage that are similar to those used in this study (tables 1-2, figure 4). We then assess the accuracy of these datasets using the reference data extracted from Landsat imagery for the selected cities and compare them with our results. This comparison shows that the accuracy of the results produced by this study is much higher than those of the existing datasets (table 2, figure 4). Specifically, the GHS built-up data display the average values of 90.1%, 0.20%, 49.7%, and 82.5% for OA, kappa, CE, and OE, respectively (table 2). The GHS SMOD data display the average values of 87.4%, 0.15%, 67.0%, and 83.3% for OA, kappa, CE, and OE, respectively (table 2). The ESACCI data display the average values of 90.5%, 0.19%, 49.7%, and 84.5% for OA, kappa, CE, and OE, respectively (table 2). The average OA and kappa of the results from this study are 0.4%-3.5% and 0.27-0.32 higher than those of these three global urban expansion datasets, respectively, whereas the average CE and OE obtained in this study are 6.7%-24.0% and 32.7%-34.6% lower (table 2).

Global urban expansion from 1992 to 2016
The world has experienced rapid urban expansion. The global urban land increased from 274.7 thousand km 2 to 621.1 thousand km 2 ; these values correspond to an urban expansion area of 346.4 thousand km 2 and an annual growth rate of 3.5% ( figure 5, table 3).
Asia displays the largest urban expansion area. Its urban land area has increased from 65.6 thousand km 2 -209.8 thousand km 2 . These values correspond to an increase in urban area of 144.2 thousand km 2 , which accounts for 41.6% of the global urban expansion area ( figure 5(b), table 3). Among the countries in Asia, China displays the largest urban expansion area of 72.2 thousand km 2 ; this value is 20.8% of the global urban expansion area. The urban areas in India and Turkey increased by 14.4 thousand km 2 and 7.4 thousand km 2 , and these values account for 4.2% and 2.2% of the global urban expansion area, respectively. The urban expansion areas of five countries, Iran, Saudi Arabia, Vietnam, Indonesia, and Malaysia, contribute between 1%-2% of the global urban expansion area.
In Northern America, the urban land area increased by 69.7 thousand km 2 (from 78.9 thousand km 2 -148.5 thousand km 2 ), and this increase accounts for 20.1% of the global urban expansion area ( figure 5(b), table 3). In particular, the United States increased its urban land area by 66.4 thousand km 2 , which accounts for 19.2% of the global urban expansion area. In Latin America and the Caribbean, the urban land area increased from 36.2 thousand km 2 -99.4 thousand km 2 . This change corresponds to a net gain of 63.2 thousand km 2 , which accounts for 18.2% of the global urban expansion area ( figure 5(b), table 3). Within this region, Brazil, Argentina, and Mexico increased their urban land areas by 28.8 thousand km 2 , 11.4 thousand km 2 , and 7.9 thousand km 2 and account for 8.3%, 3.3%, and 2.3% of the global urban expansion area, respectively.
In Europe, the urban land area increased from 81.5 thousand km 2 -121.3 thousand km 2 . This increase corresponds to an urban expansion area of 39.9 thousand km 2 , which accounts for 11.5% of the worldwide growth ( figure 5(b), table 3). Three countries in Europe, Italy, France, and Spain, increased their urban areas by between 5.0 thousand km 2 -5.5 thousand km 2 , taking up approximately 1.5% of global urban expansion area. Russia expanded its urban areas by 3.6 thousand km 2 , taking up 1.1% of global urban expansion area.
The areas of urban expansion in Africa and Oceania account for less than 10% of the worldwide growth ( figure 5(b), table 3). In Africa, the urban land area grew from 7.7 thousand km 2 to 27.6 thousand km 2 , an additional 19.9 thousand km 2 , which accounts for 5.7% of the global growth. Within this region, South Africa increased its urban land area by 5.2 thousand km 2 , which accounts for 1.5% of the global expansion. In Oceania, the urban land area increased from 4.8 thousand km 2 -14.5 thousand km 2 . This change corresponds to an urban expansion area of 9.7 thousand km 2 , which accounts for 2.8% of the worldwide growth. Within this region, Australia increased its urban land area by 8.4 thousand km 2 , which corresponds to 2.4% of the global expansion.  (2017) applied an unsupervised classification method to study global urban expansion from 1992-2015. However, effective methods to support the study of global urban expansion are still lacking. For example, the POK method requires large amounts of manual work and time. Unsupervised classification methods and symbolic machine learning methods ignore the features of urban land over multiple scales, such as shape, texture, and background information, resulting in the confusion of urban land and barren land. In addition, the lack of training samples of historical urban land areas around the world has led to relatively low accuracy and inconsistencies among the global urban land areas extracted using classification methods.
The proposed FCN has advantages in integrating remote sensing data from multiple sources, combining features over multiple scales, and making up for the lack of historical training samples. First, FCNs inherit the abilities of artificial neural networks and permits the adaptive processing of massive data from multiple sources (LeCun et al 2015). The FCN used in this study employs large numbers of neurons in the convolutional layers to obtain information from different data sources, including NTL, NDVI, LST, longitude, and latitude. The fusion of various types of data is made possible through the use of a nonlinear transformation function in the activation layers. The method then selects appropriate information to effectively characterize the socioeconomic and natural attributes of urban land and provides abundant and reliable information for urban land extraction (figures 2(b) and 6(a)). Second, the FCN uses three convolutional layers and one pooling layer to obtain urban land information at the pixel, neighborhood, and urban region scales. It effectively distinguishes urban built-up areas from barren land (figures 2(b), 6(b)). Third, when calibrated using training samples at one time, the FCN can be applied repeatedly due to its repetitive utilization of weights, which it inherits from deep learning models (Long et al 2015). This advantage solves the problems associated with a lack of historical training samples of urban land around the world but also improves the continuity and comparability of the extracted urban land at different times (figure 6(c)). Consequently, the proposed FCN can accurately extract urban land areas at one time and has the ability to integrate data from multiple sources and features over multiple scales (figures 6(a), (b)). For instance, the extraction of urban land in Beijing in 2016 performed using the FCN is associated with a kappa index of 0.79 and an OA of 95.1% (figures 6(a), (b)). Furthermore, we can effectively identify urban land at different times by repeatedly using the FCN. The detected urban expansion in Beijing from 1992 to 2016 is associated with a kappa index of 0.62 and an OA of 93.8% ( figure 6(c)). In addition, the proposed FCN can automatically extract urban land areas from remote sensing data, which save large amounts of manual work and time. Thus, it provides an effective approach for detecting global urban expansion.
Several long-term time series of surface reflectance data are available for the study of land use dynamics. These datasets include those collected by MERIS, AVHRR, PROBA-V, and SPOT-VGT and the highresolution radar data obtained by TerraSAR-X, Tan-DEM-X, and Sentinel 1, as well as crowd-sourced data, such as OpenStreetMap (Esch et al 2017, UCL-Geomatics 2017. These data are obtained from multiple sources and have different spatial and temporal resolutions. The proposed FCN method can easily adapt to process these datasets. Thus, it has great potential to be widely used to detect global urban land expansion using time series data.

Future perspectives
In this study, we produced a new global urban expansion dataset using the proposed FCN. This dataset has several merits over existing datasets (table 1). First, it provides adequate spatial information with 1 km resolution, which is much more precise than the HYDE dataset. Second, our dataset includes continuously comparable urban land data from 1992-2016, filling gaps in existing datasets of MGIS, GLC30 and IMPSA. Third, our dataset reveals higher accuracy than the GHS dataset, the FROM-GLC dataset and the ESACCI dataset.
In addition, the inconsistent definitions of urban land have been recognized as a primary reason for the discrepancies of existing global urban land datasets . In this study, we followed Schneider et al (2009Schneider et al ( , 2010 and defined urban land as built-up area, i.e. the area dominated (more than 50% in cover) by non-vegetated, human-constructed elements, such as roads, buildings, runways, and industrial facilities . Such definition makes our dataset comparable with most existing datasets with similar resolution (table 1).
However, our study also has some limitations. For example, the blooming effects of NTL data and the discontinuity between the DMSP-OLS data and the NPP-VIIRS data still result in relatively high OE and CE in some cities (e.g. Los Angeles, New York, and Victoria) (table 2).
In the future, we will attempt to adjust the number of convolutional layers and the size of the convolutional kernels of our FCN using high-resolution data (Long et al 2015, Fu et al 2017 to enable the detection of global urban expansion at resolutions finer than 1 km. We will also quantify the spatiotemporal patterns of global urbanization based on the results , Liu et al 2016a. In addition, we will use the ecological models of Carnegie-Ames-Stanford approach, Integrated Valuation of Ecosystem Services and Tradeoffs, and Service Path Attribution Networks to evaluate the impacts of global urban expansion on ecosystems (

Conclusions
We developed an FCN-based method to perform detection of global urban expansion. The effectiveness of this method lies in its ability to integrate remote sensing data from multiple sources, to combine features over multiple scales and to address the lack of training samples of historical urban land. Given the richness of several types of remotely sensed data, the proposed FCN has great potential to be widely used.
We obtain global urban expansion information covering the last three decades using the proposed FCN. An accuracy assessment based on census data reveals that the extracted urban land displays a strong correlation (R 2 0.5) with population growth at a significance level of 0.01. An accuracy assessment based on finer-resolution Landsat data indicates that our results display an average OA of 90.9% and an average kappa value of 0.47. The accuracy of our results is higher than those of existing global datasets, specifically the GHS built-up data, the GHS SMOD data, and the ESACCI data; compared to these existing datasets, the average OA and average kappa values improve by 0.4%-3.5% and 0.27-0.32, respectively.
The results show that the world has experienced large-scale urban expansion from 1992-2016. The global urban land area increased from 274.7 thousand km 2 -621.1 thousand km 2 , which is an increase of 346.4 thousand km 2 and a growth by 1.3 times. Among the six world regions, Asia displays the largest urban expansion area of 144.2 thousand km 2 and accounts for 41.6% of the worldwide growth. Among all of the countries in the world, China displays the largest urban expansion area of 72.2 thousand km 2 and accounts for 20.8% of the global increase.