Assessment of Urban Expansion and Identification of Sprawl Through Delineation of Urban Core Boundary

Abstract

Cities are spatially expanding rapidly, leading to urban sprawl. This study aims to understand the nature of the urban expansion of Chennai city, located on India’s southeastern coast, by determining the urban growth pattern and identifying the urban sprawl areas. The urban growth pattern and sprawl areas between 1998 and 2019 are identified using remote sensing data through the delineation of the Urban Core Boundary (UCB). The urban areas were extracted from the Land Use Land Cover (LULC) classification using combined classification technique to delineate the UCB. All the findings were validated using ground truth information. LULC classification performed with an accuracy of more than 90 % for urban land cover revealed an increase in urban cover by 71.77% from 1998 to 2009 and 36.91 % from 2009 to 2019. The delineated UCB’s peripheral distance was measured from the city centre in an anticlockwise direction from 0˚ to 360˚ at every 10˚ interval. It is observed that the urban core boundary expanded to a maximum of 16.02 km along 240˚ and 11.93 km along 220˚ from the city centre, and the lands in the vicinity of the National Highway (NH 32), which is situated between these sectors, experienced maximum urban development. The study also pinpointed the sprawl areas during the study period, revealing that the urban sprawl occurs along the highways, around designated special economic zones, and industrial corridors.

Assessing Human Settlement Sprawl in Mexico via Remote Sensing and Deep Learning

“…Regarding the human settlement problem, our research distances itself, offering a comprehensive evaluation of deep-learning-based methods and establishing a baseline for a dataset that is ample and covers most of Mexico. In the assessment of human settlements sprawl, researchers routinely employ the change of land use alone [37], [38], combined with other descriptors [39]- [42], or an ad hoc sprawl index, sometimes the result of multivariate analysis [35], [46], [47], [70]. These assessments describe aspects that may provide information about the features considered.…”

“…Researchers have employed remote sensing and ML techniques to quantify these impacts. Some researchers [36]- [38] employ land-use change alone to indicate settlement sprawl, while others [39] focus on changes occurring radially from the settlement center. In addition to remote sensing, some researchers [40]- [42] have incorporated census information [43]- [45].…”