Summary of Mapping Africa Settlements: High Resolution Urban and Rural Map by Deep Learning and Satellite Imagery, By Mohammad Kakooei et al.
Mapping Africa Settlements: High Resolution Urban and Rural Map by Deep Learning and Satellite Imagery
by Mohammad Kakooei, James Bailie, Albin Söderberg, Albin Becevic, Adel Daoud
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to constructing high-resolution rural-urban maps using deep learning techniques and satellite imagery. The authors develop a DeepLabV3-based model that is trained on Landsat-8 and ESRI LULC dataset, augmented with human settlement data from GHS-SMOD. The model utilizes semantic segmentation to classify land into detailed categories, including urban and rural areas, at 10-meter resolution. The study demonstrates the importance of incorporating LULC along with urban and rural classifications in distinguishing between different land use types. The findings can support more informed decision-making for policymakers, researchers, and stakeholders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better maps that show where people live and how they impact the environment. They use special computers to look at pictures of the Earth taken from space and create detailed maps of urban and rural areas. This is important because it can help us make good decisions about how to build cities, grow food, and protect nature. |
Keywords
» Artificial intelligence » Deep learning » Semantic segmentation
