M.A.G.E.C: machine assisted geometry extraction and creation

Fuzail Palnak; Kshitij Nikhal; Prakhar Verma; Ravi Panchani; Sagar Rohankar

doi:10.1117/12.2559438

31 January 2020 M.A.G.E.C: machine assisted geometry extraction and creation

Fuzail Palnak, Kshitij Nikhal, Prakhar Verma, Ravi Panchani, Sagar Rohankar

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.

Citation Download Citation

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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