遥感、地理空间数据和机器学习在获取可访问性和位置信息以促进印度尼西亚可持续发展方面的机遇与挑战

Terry Devara
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摘要

随着技术的进步,数据收集方法也在不断改进,从而产生了大量、快速和多样化的数据流。印度尼西亚统计局(BPS)也鼓励利用这一点,开始收集受访者和公共设施的地理空间信息。为了跟上这一步伐,需要改变处理方法,以适应以机器学习等不同形式收集的海量、高维和多形式数据。这一进步也为解决各种统计数据问题(如可达性和位置数据)提供了新的机遇。遥感是大数据源之一,其变化很大,这体现在高空间和时间分辨率卫星图像的可用性,再加上 BPS 地理标记数据,在土地利用分类和地理空间分析方面大有可为。尽管如此,在遥感和其他地理空间数据利用方面仍存在一些挑战。本文旨在研究利用遥感、地理空间数据和机器学习获取可达性和位置信息的机遇和挑战。在本文中,我们探讨了将其应用于可持续发展目标中涉及可及性和位置的指标(如指标 9.1.1、11.1.1、11.2.1、11.3.1 和 11.7.1)的可能性和局限性,包括计算所需的其他变量,如公共设施的可及性。此外,与使用简单的比率相比,我们使用地理标记数据进行的实验显示了改进比例估算的潜力。根据联合国的定义,我们的 DEGURBA 使用机器学习 LULC 来绘制asymetric 地图,与现有数据相比,也提供了更多的洞察力。我们可以得出这样的结论:应用遥感和其他地理空间数据来监测印尼的可达性和位置,以进一步促进可持续发展,是大有可为的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Opportunities and Challenges of Remote Sensing, Geospatial Data, and Machine Learning in Obtaining Accessibility and Location Information for Sustainable Development in Indonesia
With the advancement of technologies so does the data collection method which creates a large, rapid, and diverse stream of data. Statistic Indonesia (BPS) has also encouraged to utilize this by starting to collect geospatial information on respondents and public facilities. To keep up with this a change needs to be made in processing methods to accommodate massive, high-dimensional, and multiform data collected in different forms such as machine learning. This progression also opens up a new opportunity for tackling various statistical data problems such as accessibility and location data. Remote sensing is one of the big data sources that undergoes a lot of changes shown in the high spatial and temporal resolution satellite imagery availability, together with the BPS geotagging data shows great promise in classifying land use and geospatial analysis. Even so, there are still some challenges in remote sensing as well as other geospatial data utilization. The goals of this review paper are to study the opportunities and challenges in utilizing remote sensing, geospatial data, and machine learning for accessibility and location information. In this paper, we explore the possibilities and limitations in its implementation into SDGs indicators that involve accessibility and location such as indicators 9.1.1, 11.1.1, 11.2.1, 11.3.1, and 11.7.1 including other variables needed for the calculation like access to public facilities. Moreover, our experiment using geotagging data shows potential in improving proportion estimation when compared to using a simple ratio. Our DEGURBA following the UN definition using machine learning LULC for dasymetric mapping also provides more insight compared to the existing data. We can conclude that there are great opportunities in applying remote sensing and other geospatial data to monitor the accessibility and location to further sustainable development in Indonesia.
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