Generative Street Addresses from Satellite Imagery
We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world’s roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and long...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
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MDPI AG
2018
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| Online Access: | http://hdl.handle.net/1721.1/114662 https://orcid.org/0000-0002-3254-3224 |
| _version_ | 1811082448279175168 |
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| author | Demir, İlke Hughes, Forest Raj, Aman Dhruv, Kaunil Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Muddala, Suryanarayana Raskar, Ramesh |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Demir, İlke Hughes, Forest Raj, Aman Dhruv, Kaunil Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Muddala, Suryanarayana Raskar, Ramesh |
| author_sort | Demir, İlke |
| collection | MIT |
| description | We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world’s roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude information into a memorable form for unknown areas. However, settlements are identified by streets, and such addressing schemes are not coherent with the road topology. Instead, we propose a generative address design that maps the globe in accordance with streets. Our algorithm starts with extracting roads from satellite imagery by utilizing deep learning. Then, it uniquely labels the regions, roads, and structures using some graph- and proximity-based algorithms. We also extend our addressing scheme to (i) cover inaccessible areas following similar design principles; (ii) be inclusive and flexible for changes on the ground; and (iii) lead as a pioneer for a unified street-based global geodatabase. We present our results on an example of a developed city and multiple undeveloped cities. We also compare productivity on the basis of current ad hoc and new complete addresses. We conclude by contrasting our generative addresses to current industrial and open solutions. Keywords: road extraction; remote sensing; satellite imagery; machine learning; supervised learning; generative schemes; automatic geocoding |
| first_indexed | 2024-09-23T12:03:35Z |
| format | Article |
| id | mit-1721.1/114662 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:03:35Z |
| publishDate | 2018 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | mit-1721.1/1146622022-09-27T23:49:04Z Generative Street Addresses from Satellite Imagery Demir, İlke Hughes, Forest Raj, Aman Dhruv, Kaunil Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Muddala, Suryanarayana Raskar, Ramesh Massachusetts Institute of Technology. Media Laboratory Raskar, Ramesh We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world’s roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude information into a memorable form for unknown areas. However, settlements are identified by streets, and such addressing schemes are not coherent with the road topology. Instead, we propose a generative address design that maps the globe in accordance with streets. Our algorithm starts with extracting roads from satellite imagery by utilizing deep learning. Then, it uniquely labels the regions, roads, and structures using some graph- and proximity-based algorithms. We also extend our addressing scheme to (i) cover inaccessible areas following similar design principles; (ii) be inclusive and flexible for changes on the ground; and (iii) lead as a pioneer for a unified street-based global geodatabase. We present our results on an example of a developed city and multiple undeveloped cities. We also compare productivity on the basis of current ad hoc and new complete addresses. We conclude by contrasting our generative addresses to current industrial and open solutions. Keywords: road extraction; remote sensing; satellite imagery; machine learning; supervised learning; generative schemes; automatic geocoding 2018-04-12T14:17:17Z 2018-04-12T14:17:17Z 2018-03 2018-02 2018-03-22T12:47:07Z Article http://purl.org/eprint/type/JournalArticle 2220-9964 http://hdl.handle.net/1721.1/114662 Demir, İlke et al. "Generative Street Addresses from Satellite Imagery." ISPRS International Journal of Geo-Information 7, 3 (March 2018): 84 © 2018 The Authors https://orcid.org/0000-0002-3254-3224 http://dx.doi.org/10.3390/ijgi7030084 ISPRS International Journal of Geo-Information Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf MDPI AG Multidisciplinary Digital Publishing Institute |
| spellingShingle | Demir, İlke Hughes, Forest Raj, Aman Dhruv, Kaunil Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Muddala, Suryanarayana Raskar, Ramesh Generative Street Addresses from Satellite Imagery |
| title | Generative Street Addresses from Satellite Imagery |
| title_full | Generative Street Addresses from Satellite Imagery |
| title_fullStr | Generative Street Addresses from Satellite Imagery |
| title_full_unstemmed | Generative Street Addresses from Satellite Imagery |
| title_short | Generative Street Addresses from Satellite Imagery |
| title_sort | generative street addresses from satellite imagery |
| url | http://hdl.handle.net/1721.1/114662 https://orcid.org/0000-0002-3254-3224 |
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