Scaling up to National/Regional Urban Extent Mapping Using Landsat Data

Trianni, G.; Lisini, Gianni; Angiuli, Emanuele; Moreno, Enrique; Dondi, Piercarlo; Gaggia, Alessandro; Gamba, Paolo

doi:10.1109/jstars.2015.2398032

Cited by 44 publications

(20 citation statements)

References 19 publications

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“…With appropriate ground-truth data, GEE can serve as an accessible and feasible platform for image classification and analysis of large and geographically diverse regions. Though GEE has been used in previous studies for various applications, including population [70,75] and forest cover [76] mapping, ours is the first to provide comprehensive open-source ground-truth data that can serve as a training set for supervised classification of built-up land cover and for evaluation/validation of existing classifiers and classification products.…”

Section: Discussionmentioning

confidence: 99%

Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine

et al. 2016

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Urbanization often occurs in an unplanned and uneven manner, resulting in profound changes in patterns of land cover and land use. Understanding these changes is fundamental for devising environmentally responsible approaches to economic development in the rapidly urbanizing countries of the emerging world. One indicator of urbanization is built-up land cover that can be detected and quantified at scale using satellite imagery and cloud-based computational platforms. This process requires reliable and comprehensive ground-truth data for supervised classification and for validation of classification products. We present a new dataset for India, consisting of 21,030 polygons from across the country that were manually classified as "built-up" or "not built-up," which we use for supervised image classification and detection of urban areas. As a large and geographically diverse country that has been undergoing an urban transition, India represents an ideal context to develop and test approaches for the detection of features related to urbanization. We perform the analysis in Google Earth Engine (GEE) using three types of classifiers, based on imagery from Landsat 7 and Landsat 8 as inputs. The methodology produces high-quality maps of built-up areas across space and time. Although the dataset can facilitate supervised image classification in any platform, we highlight its potential use in GEE for temporal large-scale analysis of the urbanization process. Our methodology can easily be applied to other countries and regions.

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Section: Discussionmentioning

confidence: 99%

Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine

et al. 2016

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“…However, applying the proposed method in areas with large differences in background environment still requires further discussion. In addition, Google Earth Engine (GEE) stores a large number of data from satellite images and other earth observation databases and provides sufficient computing power to process these data [57]. When the network environment allows, we will try to use this method to update urban land areas in large areas on the GEE platform.…”

Section: Discussionmentioning

confidence: 99%

Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns

et al. 2019

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Rapid and accurate updating of urban land areas is of great significance to the study of environmental changes. Although there are many urban land products (ULPs) at present, such as GlobeLand30, Global Urban Footprint (GUF), and Global Human Settlement Layer (GHSL), these products are all static data of a certain year, and are not able to provide high-accuracy updating of urban land areas. In addition, the accuracies of these data and their application value in the update of urban land areas need to be urgently proven. Therefore, we proposed an approach to quickly and accurately update urban land areas in the Kuala Lumpur region of Malaysia, and assessed the accuracies of urban land products in different urban landscape patterns. The approach combined the advantages of multi-source data including existing ULPs, OpenStreetMap (OSM) data, Landsat Operational Land Imager (OLI), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images. Three main steps make up this approach. First, the urban land training samples were selected in the urban areas consistent with GlobeLand30, GUF, and GHSL, and samples of bare land, vegetation, water bodies, and road auxiliary data were obtained by GlobeLand30 and OSM. Then, the random forest was used to extract urban land areas according to the object's features in the OLI and PALSAR images. Last, we assessed the accuracies of GlobeLand30, GUF, GHSL, and the results of this study (ULC) by using point and area validation methods. The results showed that the ULC had the highest overall accuracy of 90.18% among the four products and could accurately depict urban land in different urban landscapes. The GHSL was the second most accurate of the four products, and the accuracy in urban areas was much higher than that in rural areas. The GUF had many omission errors in urban land areas and could not delineate a large area of complete spatial information of urban land, but it could effectively extract scattered residential land with small patches. GlobeLand30 had the lowest accuracy and could only express rough, large-scale urban land. The above conclusions provide evidence that ULPs and the approach proposed in this study have a great application potential for high-accuracy updating of urban land areas.

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“…We are, of course, not the first to face these challenges-the authors of references [16,[40][41][42] have all suggested solutions.…”

Section: Dealing With the Inevitable Instabilities Arising From Diffementioning

confidence: 99%

Combining Measurements of Built-up Area, Nighttime Light, and Travel Time Distance for Detecting Changes in Urban Boundaries: Introducing the BUNTUS Algorithm

Luqman

Gurney

2019

Remote Sensing

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This paper introduces a new algorithm (BUNTUS-Built-up, Nighttime Light, and Travel time for Urban Size) using remote sensing techniques to delineate urban boundaries. The paper is part of a larger study of the role of urbanisation in changing fossil fuel emissions. The method combines estimates of land cover, nighttime lights, and travel times to classify contiguous urban areas. The method is automatic, global and uses data sets with enough duration to establish trends. Validation using ground truth from Landsat-8 OLI images revealed an overall accuracy ranging from 60% to 95%. Thus, this approach is capable of describing spatial distributions and giving detailed information of urban extents. We demonstrate the method with examples from Brisbane, Australia, Melbourne, Australia, and Beijing, China. The new method meets the criteria for studying overall trends in urban emissions.In social science, the areas with high population densities are regarded as urban or city areas [8]. Economics defines the city by its political, cultural, and economic characteristics [9].This work forms the first part of a study of the global distribution of trends in urban fossil fuel emissions and related quantities. That study requires a definition of urban extent so we can attribute emissions as urban or not. The needs of the overall study provide some important requirements for the algorithm we use for the urban extent. We will use these so often throughout the paper that we label them here: R1The study is global, so we may only choose globally consistent and available datasets. R2 We wish to study enough cities to establish patterns, so the algorithm must be computationally efficient enough for large-scale use. R3 Changes in urban extent can be small, so the algorithm must work at high resolution, no more than 1 km. R4 The study must be long enough to establish trends. We estimate this requires two decades of data.In this paper, we propose a novel and multiple-step approach which satisfies these requirements. We define an urban area as a contiguous (i.e., simply connected) and compact region including a pre-defined urban center and which satisfies several criteria relating to density and surface properties. The urban boundary is the bounding polygon for this region. The compactness requirement means that gaps such as green belts (but not water) should be included in the urban area. The criteria which define the urban area must be deducible from datasets satisfying R1, R3, and R4.The outline of the paper is as follows. In Section 2, we review existing methods for determining urban extent, focusing on their utility for our task. In Section 3, we describe our methodology and its underlying data sets. In Section 3, we also present available validation for the method and three case studies of different cities. In Section 4, we validate and discuss the results with a particular focus on the uncertainties of the method and summary of the main results. Section 5 comprises of discussion and Section 6 comprises of conclusion.

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Scaling up to National/Regional Urban Extent Mapping Using Landsat Data

Cited by 44 publications

References 19 publications

Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine

Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine

Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns

Combining Measurements of Built-up Area, Nighttime Light, and Travel Time Distance for Detecting Changes in Urban Boundaries: Introducing the BUNTUS Algorithm

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