Classifying Economic Areas for Urban Planning using Deep Learning and
Satellite Imagery in East Africa
Abstract
In this research we use data from a number of different sources of
satellite imagery. Below we describe and visualize various metrics of
the datasets being considered. Satellite imagery is retrieved from
Google earth which is supported by Data SIO (Scripps Institution of
Oceanography), NOAA (National Oceanic and Atmospheric Administration),
US. Navy (United States Navy), NGA (National Geospatial-Intelligence
Agency), GEBCO (General Bathymetric Chart of the Oceans), Image Landsat,
and Image IBCAO (International Bathymetric Chart of the Arctic Ocean).
Using random sampling of spatial area in Kigali per target area, 342,843
thousands images were retrieved under the five categories: residential
high income (78941), residential low income(162501), residential middle
income(101401), commercial building, (67400) and industrial
zone,(24400). For the industrial zone, we also included some images from
Nairobi, Kenya industrial spatial area. The average number of samples
for a category is 86929. The size of the sample per category is
proportional to the size of the spatial target area considered per
category. Kigali is located at latitude:-1.985070 and
longitude:-1.985070, coordinates. Nairobi is located at
latitude:-1.286389 and longitude:36.817223, coordinates.