A Holistic Framework for Addressing the World using Machine Learning
© 2018 IEEE. Millions of people are disconnected from basic services due to lack of adequate addressing. We propose an automatic generative algorithm to create street addresses from satellite imagery. Our addressing scheme is coherent with the street topology, linear and hierarchical to follow human...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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IEEE
2021
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| Online Access: | https://hdl.handle.net/1721.1/137983 |
| _version_ | 1826195062618324992 |
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| author | Demir, Ilke Hughes, Forest Raj, Aman Dhruv, Kaunil Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Raskar, Ramesh |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Demir, Ilke Hughes, Forest Raj, Aman Dhruv, Kaunil Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Raskar, Ramesh |
| author_sort | Demir, Ilke |
| collection | MIT |
| description | © 2018 IEEE. Millions of people are disconnected from basic services due to lack of adequate addressing. We propose an automatic generative algorithm to create street addresses from satellite imagery. Our addressing scheme is coherent with the street topology, linear and hierarchical to follow human perception, and universal to be used as a unified geocoding system. Our algorithm starts with extracting road segments using deep learning and partitions the road network into regions. Then regions, streets, and address cells are named using proximity computations. We also extend our addressing scheme to cover inaccessible areas, to be flexible for changes, and to lead as a pioneer for a unified geodatabase. |
| first_indexed | 2024-09-23T10:06:19Z |
| format | Article |
| id | mit-1721.1/137983 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:06:19Z |
| publishDate | 2021 |
| publisher | IEEE |
| record_format | dspace |
| spelling | mit-1721.1/1379832021-11-10T03:37:46Z A Holistic Framework for Addressing the World using Machine Learning Demir, Ilke Hughes, Forest Raj, Aman Dhruv, Kaunil Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Raskar, Ramesh Massachusetts Institute of Technology. Media Laboratory © 2018 IEEE. Millions of people are disconnected from basic services due to lack of adequate addressing. We propose an automatic generative algorithm to create street addresses from satellite imagery. Our addressing scheme is coherent with the street topology, linear and hierarchical to follow human perception, and universal to be used as a unified geocoding system. Our algorithm starts with extracting road segments using deep learning and partitions the road network into regions. Then regions, streets, and address cells are named using proximity computations. We also extend our addressing scheme to cover inaccessible areas, to be flexible for changes, and to lead as a pioneer for a unified geodatabase. 2021-11-09T17:01:52Z 2021-11-09T17:01:52Z 2018-06 2019-08-02T13:47:42Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137983 Demir, Ilke, Hughes, Forest, Raj, Aman, Dhruv, Kaunil, Muddala, Suryanarayana Murthy et al. 2018. "A Holistic Framework for Addressing the World using Machine Learning." en 10.1109/cvprw.2018.00245 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IEEE Other repository |
| spellingShingle | Demir, Ilke Hughes, Forest Raj, Aman Dhruv, Kaunil Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Raskar, Ramesh A Holistic Framework for Addressing the World using Machine Learning |
| title | A Holistic Framework for Addressing the World using Machine Learning |
| title_full | A Holistic Framework for Addressing the World using Machine Learning |
| title_fullStr | A Holistic Framework for Addressing the World using Machine Learning |
| title_full_unstemmed | A Holistic Framework for Addressing the World using Machine Learning |
| title_short | A Holistic Framework for Addressing the World using Machine Learning |
| title_sort | holistic framework for addressing the world using machine learning |
| url | https://hdl.handle.net/1721.1/137983 |
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