Population Estimation Mining Using Satellite Imagery

Abstract

Abstract. Many countries around the world regularly collect census data. This census data provides statistical information regarding populations to in turn support decision making processes. However, traditional approaches to the collation of censes data are both expensive and time consuming. The analysis of high resolution satellite imagery provides a useful alternative to collecting census data which is significantly cheaper than traditional methods, although less accurate. This paper describes a technique for mining satellite imagery, to extract census information, founded on the use of classification techniques coupled with a graph based representation of the relevant imagery. The fundamental idea is to build a classifier that can label households according to "family size" which can then be used to collect census data. To act as a focus for the work training data obtained from villages lying some 300km to the northwest of Addis Ababa in Ethiopia was used. The nature of each household in the segmented training data was captured using a tree-based representation. Each tree represented household had a "family size" class label associated with it. This data was then used to build a classifier that can be used to predict household sizes according to the nature of the tree-based structure.

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

“…Generally, remote sensing and specifically satellite imagery has been widely used as an alternative source of data to monitor urban areas. In recent decades, the use of satellite imagery has been increasingly investigated to estimate urban population and density (Wang, Fan, & Wang, 2020;Lu, Jungho, Quackenbush, & Halligan, 2010;Hillson et al, 2019;Ogrosky, 1975;Dittakan, Coenen, Christley, & Wardeh, 2013), mapping land cover and land-use in urban areas (Carleer & Wolff, 2006;Gamba et al, 2007;Movia, Beinat, & Crosilla, 2016;Novack et al, 2003;Ouyang et al, 2011;Voorde, Van, William, & Canters, 2007), and also for tracking changes in the urban landscape (Volpi, Tuia, Bovolo, Kanevski, & Bruzzone, 2013;Zhang, Peng, & Huang, 2018;Lv, Liu, & Benediktsson, 2020). Remote sensing has also been used to detect informal settlements in urban areas, which is the first step to upgrade the most vulnerable communities (Fallatah, Jones, & Mitchell, 2020;Weir, McQuillan, & Francis, 2019).…”

Validation of the Global Human Settlement Layer and NASA Population Data for Ukraine

“…The world practices combined methods of keeping statistics on calculating the number of inhabitants. One of these involves the involvement of satellite imagery [1]. Artificial intelligence uses special algorithms to process the satellite image and returns the approximate number of inhabitants in the area captured in the image.…”

“…This article will check the accuracy of the satellite based population products to determine its feasibility in Ukraine. This work acknowledge the funding received by the National Research Foundation of Ukraine from the state budget 1 .…”

Analysis of land use and land cover maps suitability for modeling population density of urban areas – redistribution to new spatial units based on the object classification of rapideye data

“…Then, on the Urban Atlas database, Pirowski and Pomietłowska (2017). Information is also obtained directly from satellite data, generating the necessary information on land cover by classifying buildings in terms of population mapping (Dittakan et al 2013, Lo, 2003. The results are improved by data on the heights of buildings from laser scanning (Upegui and Viel, 2012), and detection of impervious areas accompanying buildings (Wu andMurray, 2005, Bajat et al, 2013).…”