Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Big Earth Data Pub Date : 2021-09-20 DOI:10.1080/20964471.2021.1948178
Zahra Assarkhaniki, S. Sabri, A. Rajabifard
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引用次数: 5

Abstract

ABSTRACT The detection of informal settlements is the first step in planning and upgrading deprived areas in order to leave no one behind in SDGs. Very High-Resolution satellite images (VHR), have been extensively used for this purpose. However, as a cost-prohibitive data source, VHR might not be available to all, particularly nations that are home to many informal settlements. This study examines the application of open and freely available data sources to detect the structure and pattern of informal settlements. Here, in a case study of Jakarta, Indonesia, Medium Resolution satellite imagery (MR) derived from Landsat 8 (2020) was classified to detect these settlements. The classification was done using Random Forest (RF) classifier through two complementary approaches to develop the training set. In the first approach, available survey data sets (Jakarta’s informal settlements map for 2015) and visual interpretation using High-Resolution Google Map imagery have been used to build the training set. Throughout the second round of classification, OpenStreetMap (OSM) layers were used as the complementary approach for training. Results from the validation test for the second round revealed better accuracy and precision in classification. The proposed method provides an opportunity to use open data for informal settlements detection, when: 1) more expensive high resolution data sources are not accessible; 2) the area of interest is not larger than a city; and 3) the physical characteristics of the settlements differ significantly from their surrounding formal area. The method presents the application of globally accessible data to help the achievement of resilience and SDGs in informal settlements.
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利用开放数据检测非正式住区的结构和模式:支持实现包容性可持续发展目标的开端
发现非正规住区是规划和改造贫困地区的第一步,目的是实现可持续发展目标,不让任何人掉队。高分辨率卫星图像(VHR)已被广泛用于这一目的。然而,作为一种成本过高的数据来源,自愿登记档案可能并非所有国家都能获得,特别是拥有许多非正式住区的国家。这项研究审查了开放和免费提供的数据源的应用,以发现非正式住区的结构和模式。本文以印度尼西亚雅加达为例,对来自Landsat 8(2020)的中分辨率卫星图像(MR)进行分类,以检测这些定居点。通过两种互补的方法开发训练集,使用随机森林(RF)分类器进行分类。在第一种方法中,使用现有的调查数据集(雅加达2015年非正式住区地图)和使用高分辨率谷歌地图图像的视觉解释来构建训练集。在第二轮分类中,OpenStreetMap (OSM)层被用作训练的补充方法。第二轮验证试验结果表明,该方法在分类上具有较好的准确性和精密度。提出的方法为使用开放数据进行非正式定居点检测提供了机会,当:1)更昂贵的高分辨率数据源无法访问;2)利益范围不大于一个城市;聚落的物理特征与其周围的正式区域存在显著差异。该方法提出了全球可访问数据的应用,以帮助实现非正式住区的复原力和可持续发展目标。
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来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
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