Mapping Africa Settlements: High Resolution Urban and Rural Map by Deep Learning and Satellite Imagery
It provides a continent-wide map to support decision-making for policymakers and researchers in Africa, but is incremental as it builds on existing methods like DeepLabV3.
This study tackled the problem of inaccurate urban and rural classifications in Land Use and Land Cover maps for Africa by developing a deep learning model using satellite imagery, resulting in a high-resolution 10-meter map that enhances distinction between urban, rural, and non-human settlement areas.
Accurate Land Use and Land Cover (LULC) maps are essential for understanding the drivers of sustainable development, in terms of its complex interrelationships between human activities and natural resources. However, existing LULC maps often lack precise urban and rural classifications, particularly in diverse regions like Africa. This study presents a novel construction of a high-resolution rural-urban map using deep learning techniques and satellite imagery. We developed a deep learning model based on the DeepLabV3 architecture, which was trained on satellite imagery from Landsat-8 and the ESRI LULC dataset, augmented with human settlement data from the GHS-SMOD. The model utilizes semantic segmentation to classify land into detailed categories, including urban and rural areas, at a 10-meter resolution. Our findings demonstrate that incorporating LULC along with urban and rural classifications significantly enhances the model's ability to accurately distinguish between urban, rural, and non-human settlement areas. Therefore, our maps can support more informed decision-making for policymakers, researchers, and stakeholders. We release a continent wide urban-rural map, covering the period 2016 and 2022.