Mapping Poverty of Latin American and Caribbean Countries from Heaven Through Night-Light Satellite Images

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. 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]. 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.…”

Socioeconomic development evaluation for Chinese poverty-stricken counties using indices derived from remotely sensed data

“…Overall, poverty studies based on remote sensing have mainly focused on poverty measurements, and research on dynamic monitoring in poor areas is relatively limited. Moreover, most studies have focused on the monitoring and analysis of a single factor, such as land use or nighttime light data, in poverty-stricken counties (Andreano et al, 2020;Li et al, 2020), and comprehensive and quantitative research is lacking. This study proposes a method through which remotely sensed data acquired by Landsat 8 and the NPP VIIRS can be used to monitor and evaluate socioeconomic development in poverty-stricken counties.…”

Changes in Rural Areas of the City of Carazinho (RS) between 2001 and 2020: A Temporal Analysis Using Landsat TM-7 and TM-8 Images

“…Research involving remote sensing, through the analysis of the use of satellite images, has been increasingly gaining global attention, as it makes it possible to collect information that reveals the understanding of physical variations of landscape in land use [1,2]. However, satellite images need to present more precise resolutions, which occur as a result of the development and creation of new geospatial technologies [3,4].…”