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Accurate urban boundary data can directly reflect the expansion of urban space, help us accurately grasp the scale and form of urban space, and play a vital role in urban land development and policy-making. However, the lack of reliable multiscale and high-precision urban boundary data products and relevant training datasets has become one of the major factors hindering their application. The purpose of this study is to combine Sentinel-2 remote-sensing images and supplementary geographic data to generate a reliable high-precision urban boundary dataset for Henan Province (called HNUB2018). First, this study puts forward a clear definition of "urban boundary". Using this concept as its basis, it proposes a set of operable urban boundary delimitation rules and technical processes. Then, based on Sentinel-2 remote-sensing images and supplementary geographic data, the urban boundaries of Henan Province are delimited by a visual interpretation method. Finally, the applicability of the dataset is verified by using a classical semantic segmentation deep learning model. The results show that (1) HNUB2018 has clear and rich detailed features as well as a detailed spatial structure of urban boundaries. The overall accuracy of HNUB2018 is 92.82% and the kappa coefficient reaches 0.8553, which is better than GUB (Henan) in overall accuracy. (2) HNUB2018 is well suited for deep learning, with excellent reliability and scientific validity. The research results of this paper can provide data support for studies of urban sprawl monitoring and territorial spatial planning, and will support the development of reliable datasets for fields such as intelligent mapping of urban boundaries, showing prospects and possibilities for wide application in urban research. [ABSTRACT FROM AUTHOR]

The adjacent cities of Laredo in the US and Nuevo Laredo in Mexico have been growing at an unprecedented pace over the past decades. The trans-boundary flows of commodities and capital between these two cities, as well as significant population growth have created an interdependent system in this area. This characteristic provides an opportunity to thoroughly examine drivers behind landscape changes in the two cities. The aim of this study is to examine urban growth and the influencing factors of Laredo-Nuevo Laredo. Results of initial remote sensing-GIS analysis indicated that there was considerable urban expansion in both cities from 1985 to 2014. Spatially, Laredo demonstrated a more dispersed and rapid growth pattern compared to Nuevo Laredo. Additional results from a logistic regression using data at the pixel level on both sides of the border revealed a number of factors that influenced urban growth for both cites. These factors included: (1) elevation, (2) distance to nearest urban clusters, (3) distance to local roads, and (4) density of urban pixels. In addition, this study found that some dominant variables (population density and density of highways) affected urban growth differently for the two cities. Furthermore, globalization factors related to industrial activities/maquiladoras also played a significant role in affecting urban growth although the trajectories were not completely clear. [ABSTRACT FROM AUTHOR]

• A conceptual framework was proposed to study urban morphology types (UMTs) transition. • UMT maps in 2006, 2011, and 2016 in Austin were mapped. • There has been considerable urban "intensification" and "efficient expansion" after 2000. • Policies on "efficient expansion" played an increasingly important role since the 2000s. Since the 1990s, "new urbanism" and "smart growth" have been advocated in the United States, which promote compact urban forms to cope with the environmental and ecological problems caused by urban sprawl. However, there is no uniformity in terms of the spatiotemporal characterization of urban forms. Lack of detailed 3-D urban landscape information makes it difficult to evaluate urbanization trajectories. In this study, we focused on Austin, Texas, U.S., an area with the highest urbanization and the best ranked standard of living, to characterize its evolution of urban morphology and the transformations between urban morphology types (UMTs) over time. We first proposed a conceptual framework to study the UMT transition and urbanization process. Then, the UMT in 2006, 2011 and 2016 was mapped using a synthesis of data sources, which enables a comprehensive understanding of the urbanization process with analytical methods, considering "intensification," "sprawling expansion," and "efficient expansion." Results indicate that Lidar facilitates the multidimensional characterization of urban morphology and its transformation between 2006 and 2016, as maps of UMT were in conformity with the building features of the study area. The urban expansion analysis in different periods (1992–2001, 2001–2006, 2006–2011, and 2011–2016) showed that there has been considerable "intensification" and "efficient expansion" after 2000, indicating the increasingly important role of compact development. In spite of the plans and policies on urban development since the 2000s, we found that there was more sprawl than intensification and efficient expansion. This study demonstrates the advantage of applying Lidar data to characterize urban 3-D morphology and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. [ABSTRACT FROM AUTHOR]