Change Detection from Remote Sensing to Guide OpenStreetMap Labeling
The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in combination with state-...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/9/7/427 |
| _version_ | 1797563404803637248 |
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| author | Conrad M. Albrecht Rui Zhang Xiaodong Cui Marcus Freitag Hendrik F. Hamann Levente J. Klein Ulrich Finkler Fernando Marianno Johannes Schmude Norman Bobroff Wei Zhang Carlo Siebenschuh Siyuan Lu |
| author_facet | Conrad M. Albrecht Rui Zhang Xiaodong Cui Marcus Freitag Hendrik F. Hamann Levente J. Klein Ulrich Finkler Fernando Marianno Johannes Schmude Norman Bobroff Wei Zhang Carlo Siebenschuh Siyuan Lu |
| author_sort | Conrad M. Albrecht |
| collection | DOAJ |
| description | The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in combination with state-of-the-art computer vision methods to assist the OSM community in labeling work. This article reports our progress to utilize artificial neural networks (ANN) for change detection of OSM data to update the map. Furthermore, we aim at identifying geospatial regions where mappers need to focus on completing the global OSM dataset. Our approach is technically backed by the big geospatial data platform Physical Analytics Integrated Repository and Services (PAIRS). We employ supervised training of deep ANNs from vertical aerial imagery to segment scenes based on OSM map tiles to evaluate the technique quantitatively and qualitatively. |
| first_indexed | 2024-03-10T18:43:12Z |
| format | Article |
| id | doaj.art-0c0169512d7344fda291e7659d600141 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T18:43:12Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-0c0169512d7344fda291e7659d6001412023-11-20T05:39:50ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-07-019742710.3390/ijgi9070427Change Detection from Remote Sensing to Guide OpenStreetMap LabelingConrad M. Albrecht0Rui Zhang1Xiaodong Cui2Marcus Freitag3Hendrik F. Hamann4Levente J. Klein5Ulrich Finkler6Fernando Marianno7Johannes Schmude8Norman Bobroff9Wei Zhang10Carlo Siebenschuh11Siyuan Lu12IBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAIBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USAThe growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in combination with state-of-the-art computer vision methods to assist the OSM community in labeling work. This article reports our progress to utilize artificial neural networks (ANN) for change detection of OSM data to update the map. Furthermore, we aim at identifying geospatial regions where mappers need to focus on completing the global OSM dataset. Our approach is technically backed by the big geospatial data platform Physical Analytics Integrated Repository and Services (PAIRS). We employ supervised training of deep ANNs from vertical aerial imagery to segment scenes based on OSM map tiles to evaluate the technique quantitatively and qualitatively.https://www.mdpi.com/2220-9964/9/7/427OpenStreetMap data collectionremote sensinggeospatial change detectionimage segmentationartificial neural networksbig geospatial databases |
| spellingShingle | Conrad M. Albrecht Rui Zhang Xiaodong Cui Marcus Freitag Hendrik F. Hamann Levente J. Klein Ulrich Finkler Fernando Marianno Johannes Schmude Norman Bobroff Wei Zhang Carlo Siebenschuh Siyuan Lu Change Detection from Remote Sensing to Guide OpenStreetMap Labeling ISPRS International Journal of Geo-Information OpenStreetMap data collection remote sensing geospatial change detection image segmentation artificial neural networks big geospatial databases |
| title | Change Detection from Remote Sensing to Guide OpenStreetMap Labeling |
| title_full | Change Detection from Remote Sensing to Guide OpenStreetMap Labeling |
| title_fullStr | Change Detection from Remote Sensing to Guide OpenStreetMap Labeling |
| title_full_unstemmed | Change Detection from Remote Sensing to Guide OpenStreetMap Labeling |
| title_short | Change Detection from Remote Sensing to Guide OpenStreetMap Labeling |
| title_sort | change detection from remote sensing to guide openstreetmap labeling |
| topic | OpenStreetMap data collection remote sensing geospatial change detection image segmentation artificial neural networks big geospatial databases |
| url | https://www.mdpi.com/2220-9964/9/7/427 |
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