Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa

• Main Authors: Davy Uwizera (10967343), Charles Ruranga (10967774), Patrick McSharry (3890116)
• Format: Report
• Language: unknown
• Published: 2021
• Subjects: Signal Processing and Analysis; Aerospace; Computing and Processing; Deep Learning Applications; image processing applications; Remote sensing imagery; Arctic; Arctic Ocean; Scripps
• Online Access: https://doi.org/10.36227/techrxiv.14779371.v1

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.