SpaceNet 6: Multi-Sensor All Weather Mapping Dataset Article Swipe
Jacob Shermeyer
,
David B. Hogan
,
Jason Brown
,
Adam Van Etten
,
N. Weir
,
Fabio Pacifici
,
Ronny Haensch
,
Alexei Bastidas
,
Scott Soenen
,
Todd M. Bacastow
,
Ryan Lewis
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.06500
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.06500
Within the remote sensing domain, a diverse set of acquisition modalities exist, each with their own unique strengths and weaknesses. Yet, most of the current literature and open datasets only deal with electro-optical (optical) data for different detection and segmentation tasks at high spatial resolutions. optical data is often the preferred choice for geospatial applications, but requires clear skies and little cloud cover to work well. Conversely, Synthetic Aperture Radar (SAR) sensors have the unique capability to penetrate clouds and collect during all weather, day and night conditions. Consequently, SAR data are particularly valuable in the quest to aid disaster response, when weather and cloud cover can obstruct traditional optical sensors. Despite all of these advantages, there is little open data available to researchers to explore the effectiveness of SAR for such applications, particularly at very-high spatial resolutions, i.e. <1m Ground Sample Distance (GSD). To address this problem, we present an open Multi-Sensor All Weather Mapping (MSAW) dataset and challenge, which features two collection modalities (both SAR and optical). The dataset and challenge focus on mapping and building footprint extraction using a combination of these data sources. MSAW covers 120 km^2 over multiple overlapping collects and is annotated with over 48,000 unique building footprints labels, enabling the creation and evaluation of mapping algorithms for multi-modal data. We present a baseline and benchmark for building footprint extraction with SAR data and find that state-of-the-art segmentation models pre-trained on optical data, and then trained on SAR (F1 score of 0.21) outperform those trained on SAR data alone (F1 score of 0.135).
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2004.06500
- https://arxiv.org/pdf/2004.06500
- OA Status
- green
- Cited By
- 13
- References
- 34
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3040984638
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3040984638Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2004.06500Digital Object Identifier
- Title
-
SpaceNet 6: Multi-Sensor All Weather Mapping DatasetWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-14Full publication date if available
- Authors
-
Jacob Shermeyer, David B. Hogan, Jason Brown, Adam Van Etten, N. Weir, Fabio Pacifici, Ronny Haensch, Alexei Bastidas, Scott Soenen, Todd M. Bacastow, Ryan LewisList of authors in order
- Landing page
-
https://arxiv.org/abs/2004.06500Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2004.06500Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2004.06500Direct OA link when available
- Concepts
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Computer science, Geospatial analysis, Footprint, Synthetic aperture radar, Remote sensing, Cloud computing, Benchmark (surveying), Segmentation, Cloud cover, Lidar, Data mining, Artificial intelligence, Geography, Cartography, Archaeology, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
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2021: 10, 2020: 3Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| publication_date | 2020-04-14 |
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| referenced_works_count | 34 |
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| abstract_inverted_index.cover | 62, 105 |
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| abstract_inverted_index.which | 160 |
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| abstract_inverted_index.(MSAW) | 156 |
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| abstract_inverted_index.obstruct | 107 |
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| abstract_inverted_index.Synthetic | 67 |
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| abstract_inverted_index.benchmark | 221 |
| abstract_inverted_index.challenge | 172 |
| abstract_inverted_index.detection | 37 |
| abstract_inverted_index.different | 36 |
| abstract_inverted_index.footprint | 178, 224 |
| abstract_inverted_index.optical). | 168 |
| abstract_inverted_index.penetrate | 77 |
| abstract_inverted_index.preferred | 50 |
| abstract_inverted_index.response, | 100 |
| abstract_inverted_index.strengths | 17 |
| abstract_inverted_index.very-high | 135 |
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| abstract_inverted_index.capability | 75 |
| abstract_inverted_index.challenge, | 159 |
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| abstract_inverted_index.evaluation | 209 |
| abstract_inverted_index.extraction | 179, 225 |
| abstract_inverted_index.footprints | 203 |
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| abstract_inverted_index.outperform | 248 |
| abstract_inverted_index.Conversely, | 66 |
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| abstract_inverted_index.advantages, | 115 |
| abstract_inverted_index.combination | 182 |
| abstract_inverted_index.conditions. | 87 |
| abstract_inverted_index.multi-modal | 214 |
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| abstract_inverted_index.traditional | 108 |
| abstract_inverted_index.weaknesses. | 19 |
| abstract_inverted_index.Multi-Sensor | 152 |
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| abstract_inverted_index.segmentation | 39, 233 |
| abstract_inverted_index.Consequently, | 88 |
| abstract_inverted_index.applications, | 54, 132 |
| abstract_inverted_index.effectiveness | 127 |
| abstract_inverted_index.electro-optical | 32 |
| abstract_inverted_index.state-of-the-art | 232 |
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