Using Histogram Extracted from Satelite Imagery and Convolutional Network to Predict GRDP in Java Region
Abstract
Inequality is one of the problems faced by all countries in the world, including Indonesia. The data used to measure development inequality between regions mostly use GRDP data. However, the GRDP data issued by BPS has a deficiency, it was released after the current year, and this figure is provisional. Therefore, a new data source is needed that can be used to estimate the value of economic activity so that it can be used to measure the level of inequality in development in a region. Nighttime Light (NTL) satellite imagery data can be an alternative to see socioeconomic activity in an area and have been shown to have a strong correlation with socioeconomic activity. In this study, we used VIIRS NTL satellite imagery data and Dynamic World land cover data to estimate GRDP. Rather than using statistical features for each area of interest, we use features in the form of histograms extracted from NTL images and land cover images for each area of interest. Using a histogram, we do not lose spatial information from satellite imagery. Then we proposed a deep learning method in the form of a one-dimensional convolutional neural network using the Huber loss function. This model obtained good precision with an R-square value of 0.8549, beating the baseline method with two-dimensional convolutional networks. The use of the Huber loss function can improve the performance of the model, which has a smaller total loss and has a smoother gradient.
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References
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