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Estimating Poverty from Aerial Images Using Convolutional Neural Networks Coupled with Statistical Regression Modelling
Policy makers and the government rely heavily on survey data when making policyrelated decisions. Survey data is labour intensive, costly and time consuming, hence it cannot be frequently or extensively collected. The main aim of this research is to demonstrate how Convolutional Neural Network (CNN)...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2020
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| _version_ | 1867613344246005760 |
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| access_status_str | Open Access |
| author | Maluleke, Vongani |
| author2 | Er, Sebnem |
| author_browse | Er, Sebnem Maluleke, Vongani |
| author_facet | Er, Sebnem Maluleke, Vongani |
| author_sort | Maluleke, Vongani |
| collection | Thesis |
| description | Policy makers and the government rely heavily on survey data when making policyrelated decisions. Survey data is labour intensive, costly and time consuming, hence it cannot be frequently or extensively collected. The main aim of this research is to demonstrate how Convolutional Neural Network (CNN) coupled with statistical regression modelling can be used to estimate poverty from aerial images supplemented with national household survey data. This provides a more frequent and automated method for updating data that can be used for policy making. This aerial poverty estimation approach is executed in two phases; aerial classification and detection phase and poverty modelling phase. The aerial classification and detection phase use CNN to perform settlement typology classification of the aerial images into three broad geotype classes namely; urban, rural and farm. This is then followed by object detection to detect three broad dwelling type classes in the aerial images namely; brick house, traditional house, and informal settlement. Mask Region-based Convolutional Neural Network (Mask R-CNN) model with a resnet101 CNN backbone model is used to perform this task. The second phase, poverty modelling phase, involves using NIDS data to compute the poverty measure Sen-Shorrocks-Thon (SST) index. This is followed by using regression models to model the poverty measure using aggregated results from the aerial classification and detection phase. The study area for this research is Kwa-Zulu Natal (KZN), South Africa. However, this approach can be extended to other provinces in South Africa, by retraining the models on data associated with the location in question. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31742 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:34:39.078Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31742 Estimating Poverty from Aerial Images Using Convolutional Neural Networks Coupled with Statistical Regression Modelling Maluleke, Vongani Er, Sebnem Williams, Quentin Advanced Analytics Policy makers and the government rely heavily on survey data when making policyrelated decisions. Survey data is labour intensive, costly and time consuming, hence it cannot be frequently or extensively collected. The main aim of this research is to demonstrate how Convolutional Neural Network (CNN) coupled with statistical regression modelling can be used to estimate poverty from aerial images supplemented with national household survey data. This provides a more frequent and automated method for updating data that can be used for policy making. This aerial poverty estimation approach is executed in two phases; aerial classification and detection phase and poverty modelling phase. The aerial classification and detection phase use CNN to perform settlement typology classification of the aerial images into three broad geotype classes namely; urban, rural and farm. This is then followed by object detection to detect three broad dwelling type classes in the aerial images namely; brick house, traditional house, and informal settlement. Mask Region-based Convolutional Neural Network (Mask R-CNN) model with a resnet101 CNN backbone model is used to perform this task. The second phase, poverty modelling phase, involves using NIDS data to compute the poverty measure Sen-Shorrocks-Thon (SST) index. This is followed by using regression models to model the poverty measure using aggregated results from the aerial classification and detection phase. The study area for this research is Kwa-Zulu Natal (KZN), South Africa. However, this approach can be extended to other provinces in South Africa, by retraining the models on data associated with the location in question. 2020-04-30T16:23:33Z 2020-04-30T16:23:33Z 2019 2020-04-30T14:29:38Z Master Thesis Masters MSc https://hdl.handle.net/11427/31742 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Advanced Analytics Maluleke, Vongani Estimating Poverty from Aerial Images Using Convolutional Neural Networks Coupled with Statistical Regression Modelling |
| thesis_degree_str | Master's |
| title | Estimating Poverty from Aerial Images Using Convolutional Neural Networks Coupled with Statistical Regression Modelling |
| title_full | Estimating Poverty from Aerial Images Using Convolutional Neural Networks Coupled with Statistical Regression Modelling |
| title_fullStr | Estimating Poverty from Aerial Images Using Convolutional Neural Networks Coupled with Statistical Regression Modelling |
| title_full_unstemmed | Estimating Poverty from Aerial Images Using Convolutional Neural Networks Coupled with Statistical Regression Modelling |
| title_short | Estimating Poverty from Aerial Images Using Convolutional Neural Networks Coupled with Statistical Regression Modelling |
| title_sort | estimating poverty from aerial images using convolutional neural networks coupled with statistical regression modelling |
| topic | Advanced Analytics |
| url | https://hdl.handle.net/11427/31742 |
| work_keys_str_mv | AT malulekevongani estimatingpovertyfromaerialimagesusingconvolutionalneuralnetworkscoupledwithstatisticalregressionmodelling |