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31 January 2020 M.A.G.E.C: machine assisted geometry extraction and creation
Fuzail Palnak, Kshitij Nikhal, Prakhar Verma, Ravi Panchani, Sagar Rohankar
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Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114332Z (2020) https://doi.org/10.1117/12.2559438
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands
Abstract
The GIS industry relies heavily on manual efforts to build and maintain digital maps. This approach is timeconsuming and requires a sizable workforce not only for map-making but also for quality-checks that are required to resolve the potential errors resulting from manual digitization. With recent advancements in computer vision, several organizations are using machine-learning algorithms to generate map data from images. In the current machine learning based geometry creation process three limitations prevails. Firstly, the output of the algorithms is never served on-demand to a map editing tool. Secondly, after being further fine-tuned manually by annotators/validators, the results are never fed back to the algorithms to identify the errors incurred and improve accuracy. Finally, a lot of manual effort is required to create training data for new terrains and regions. We propose an end-to-end machine learning system integrated with current map-making tools to address these limitations and reduce the manual effort in creating and updating geometry.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fuzail Palnak, Kshitij Nikhal, Prakhar Verma, Ravi Panchani, and Sagar Rohankar "M.A.G.E.C: machine assisted geometry extraction and creation", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114332Z (31 January 2020); https://doi.org/10.1117/12.2559438
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KEYWORDS
Satellites

Roads

Satellite imaging

Earth observing sensors

Image segmentation

Machine learning

Data modeling

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