Abstract
Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC), was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS) in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.
Highlights
Urban expansion is one of the most important issues in development problems (Angel et al, 2005; Foley et al, 2005)
Monitoring urban formation is needed for urban management which is connected to other issues, such as disaster risk management (Doocy et al, 2007; Dasgupta et al, 2009), public health (Brooker et al, 2006; Omumbo et al, 2005), transportation networks (Schneider et al, 2003), and food security (Balk et al, 2005)
As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER (Miyazaki et al, 2014), Landsat (European Commision Joint Research Centre, 2014), and TerraSAR-X (Esch et al, 2013), development of time-series data would be suggested to be the goal because such data can contribute to studies on urban growth processes (Taubenböck et al, 2014) which could be closely connected with socioeconomy, disaster risk management, public health, transport and other development issues
Summary
Urban expansion is one of the most important issues in development problems (Angel et al, 2005; Foley et al, 2005). Automation of the mapping is important to achieve finer human settlement maps, which are especially needed in developing countries. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER (Miyazaki et al, 2014), Landsat (European Commision Joint Research Centre, 2014), and TerraSAR-X (Esch et al, 2013), development of time-series data would be suggested to be the goal because such data can contribute to studies on urban growth processes (Taubenböck et al, 2014) which could be closely connected with socioeconomy, disaster risk management, public health, transport and other development issues. The preliminary results on the developed system is presented
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