Predicting Poverty Index using Deep Learning on Remote Sensing and Household Data
Parth Agarwal1, Nandishwar Garg2, Pratibha Singh3
1Parth Agarwal, Department of Computer Science & Engineering, ABES Engineering College, Ghaziabad, U.P., India.
2Nandishwar Garg, Department of Computer Science & Engineering, ABES Engineering College, Ghaziabad, U.P., India.
3Pratibha Singh, Department of Computer Science & Engineering, ABES Engineering College, Ghaziabad, U.P., India.
Manuscript received on 5 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP: 164-168 | Volume-8 Issue-3 September 2019 | Retrieval Number: C3918098319/19©BEIESP | DOI: 10.35940/ijrte.C3918.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The main challenge for approving and implementing policies aiming at sustainable development of country is correct prediction of socioeconomic condition. Deep learning algorithms in recent researches have been identified as potential resource to be applied in this domain. Another challenge is availability of sufficient amount of data which is solved using transfer learning in Convolutional Neural Network (CNN). We used pre-trained Inception Net-v3 and Ridge regression model to estimate poverty level using publicly available dataset comprising of daylight images, nightlight images and survey data. Each cluster of samples contains households between 1- 28. Its mean is 21.09, median 21 and a standard deviation is 1.36. Proposed deep learning inspired model estimates wealth-score for 28393 clusters with an r value i.e. Pearson Correlation Coefficient of 0.73, signifying r2 value i.e. Coefficient of determination of 0.54. It shows that daytime satellite images, nightlight intensity and demographic data available can be utilized for precise evaluations about the spatial scattering of monetary prosperity crosswise over different nations.
Keywords: Convolutional Neural Network, Daylight, Nightlight, Regression, Satellite Images, Survey Data
Scope of the Article: Deep Learning