Monitoring the spatiotemporal dynamics of poor counties in China: Implications for global sustainable development goals

Fine-Grained Large-Scale Vulnerable Communities Mapping via Satellite Imagery and Population Census Using Deep Learning

“…Establishing a baseline of comparison with other classical [8,12,16,22] or modern approaches [13,14,21,23,24] is most challenging. For one, the data sources, dense [8] or sparse [13], may have local and unique components; the scope may be a citywide [18], countrywide [26], or regionwide [15,25] interest. At any rate, this research demonstrates a strong performance with publicly available satellite information and census data at the fine-grained resolution of residential block and countrywide scale.…”

“…Thus, there has been a considerable interest on mapping vulnerable regions from remote sensing, particularly employing the images provided by the satellites LandSat [8,9], Sentinel-2 [10,11], QuickBird-2 [11,12], TerraSAR-X [12,13], Pleiades [14], and the National Oceanic and Atmospheric Administration (NOAA) Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) [15][16][17][18][19][20]. Both high-resolution [10,12,14] and low-resolution [10,11,[15][16][17] images have been considered in rural [9] and urban areas in multispectral [9,10,17], Synthetic Aperture Radar (SAR) [11,13], and color [19,21] images.…”

“…Both high-resolution [10,12,14] and low-resolution [10,11,[15][16][17] images have been considered in rural [9] and urban areas in multispectral [9,10,17], Synthetic Aperture Radar (SAR) [11,13], and color [19,21] images. Multiple machine learning methods including traditional [8,12,16,18,22] approaches such as Random Forest (RF), gradient boosting, Support Vector Machines (SVM), and modern techniques [11,13,14,19,21,23,24] such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been deployed. Some open-source code has been made available to the research community [25].…”

“…Some open-source code has been made available to the research community [25]. The scope of assessing vulnerability scales up from the city level [18], to the country level [26], and to the many countries level [15,25]. A critical issue that remains to be explored, which is the object of this paper, is developing techniques for countrywide assessment of vulnerability at the residential block level using the UN-habitat criteria [27] and census data.…”

Regional Poverty and Inequality in the Xiamen-Zhangzhou-Quanzhou City Cluster in China Based on NPP/VIIRS Night-Time Light Imagery

“…Reducing poverty and inequality is increasingly a critical issue and challenge for most countries. As the Due to the above advantages of NTL data, some scholars have used night-time light data to study regional poverty and inequality [5][6][7]9,10,26,27]. These studies suggest that NTL data can be used to estimate poverty and socioeconomic disparities.…”