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Remote Sens. 2016, 8(8), 634; doi:10.3390/rs8080634

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

1
School of Global Policy and Strategy, University of California, San Diego, CA 92093, USA
2
Department of Economics, University of California, San Diego, CA 92093, USA
3
Columbia Business School, Columbia University, New York, NY 10027, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Soe Myint and Prasad S. Thenkabail
Received: 26 April 2016 / Revised: 6 July 2016 / Accepted: 26 July 2016 / Published: 1 August 2016
View Full-Text   |   Download PDF [11508 KB, uploaded 1 August 2016]   |  

Abstract

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. View Full-Text
Keywords: Google Earth Engine; Landsat; remote sensing; urbanization; built-up land cover; pixel-based image classification Google Earth Engine; Landsat; remote sensing; urbanization; built-up land cover; pixel-based image classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Goldblatt, R.; You, W.; Hanson, G.; Khandelwal, A.K. Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine. Remote Sens. 2016, 8, 634.

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