Deep learning based landslide density estimation on SAR data for rapid response Article Swipe
V. Boehm
,
Wei Ji Leong
,
Ragini Bal Mahesh
,
Ioannis Prapas
,
Edoardo Nemni
,
Freddie Kalaitzis
,
Siddha Ganju
,
Raúl Ramos-Pollán
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.10338
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.10338
This work aims to produce landslide density estimates using Synthetic Aperture Radar (SAR) satellite imageries to prioritise emergency resources for rapid response. We use the United States Geological Survey (USGS) Landslide Inventory data annotated by experts after Hurricane María in Puerto Rico on Sept 20, 2017, and their subsequent susceptibility study which uses extensive additional information such as precipitation, soil moisture, geological terrain features, closeness to waterways and roads, etc. Since such data might not be available during other events or regions, we aimed to produce a landslide density map using only elevation and SAR data to be useful to decision-makers in rapid response scenarios. The USGS Landslide Inventory contains the coordinates of 71,431 landslide heads (not their full extent) and was obtained by manual inspection of aerial and satellite imagery. It is estimated that around 45\% of the landslides are smaller than a Sentinel-1 typical pixel which is 10m $\times$ 10m, although many are long and thin, probably leaving traces across several pixels. Our method obtains 0.814 AUC in predicting the correct density estimation class at the chip level (128$\times$128 pixels, at Sentinel-1 resolution) using only elevation data and up to three SAR acquisitions pre- and post-hurricane, thus enabling rapid assessment after a disaster. The USGS Susceptibility Study reports a 0.87 AUC, but it is measured at the landslide level and uses additional information sources (such as proximity to fluvial channels, roads, precipitation, etc.) which might not regularly be available in an rapid response emergency scenario.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.10338
- https://arxiv.org/pdf/2211.10338
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309590858
All OpenAlex metadata
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https://openalex.org/W4309590858Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2211.10338Digital Object Identifier
- Title
-
Deep learning based landslide density estimation on SAR data for rapid responseWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-11-18Full publication date if available
- Authors
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V. Boehm, Wei Ji Leong, Ragini Bal Mahesh, Ioannis Prapas, Edoardo Nemni, Freddie Kalaitzis, Siddha Ganju, Raúl Ramos-PollánList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.10338Publisher landing page
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https://arxiv.org/pdf/2211.10338Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2211.10338Direct OA link when available
- Concepts
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Landslide, Geological survey, Geology, Remote sensing, Synthetic aperture radar, Digital elevation model, Pixel, Interferometric synthetic aperture radar, Satellite imagery, Terrain, Cartography, Computer science, Geomorphology, Geography, Artificial intelligence, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.than | 142 |
| abstract_inverted_index.that | 134 |
| abstract_inverted_index.thus | 198 |
| abstract_inverted_index.uses | 52, 222 |
| abstract_inverted_index.work | 1 |
| abstract_inverted_index.(SAR) | 12 |
| abstract_inverted_index.(such | 226 |
| abstract_inverted_index.0.814 | 167 |
| abstract_inverted_index.2017, | 45 |
| abstract_inverted_index.Radar | 11 |
| abstract_inverted_index.Since | 70 |
| abstract_inverted_index.Study | 208 |
| abstract_inverted_index.after | 36, 202 |
| abstract_inverted_index.aimed | 83 |
| abstract_inverted_index.class | 175 |
| abstract_inverted_index.etc.) | 234 |
| abstract_inverted_index.heads | 115 |
| abstract_inverted_index.level | 179, 220 |
| abstract_inverted_index.might | 73, 236 |
| abstract_inverted_index.other | 78 |
| abstract_inverted_index.pixel | 146 |
| abstract_inverted_index.rapid | 20, 102, 200, 243 |
| abstract_inverted_index.study | 50 |
| abstract_inverted_index.their | 47, 117 |
| abstract_inverted_index.thin, | 157 |
| abstract_inverted_index.three | 192 |
| abstract_inverted_index.using | 8, 90, 185 |
| abstract_inverted_index.which | 51, 147, 235 |
| abstract_inverted_index.(USGS) | 29 |
| abstract_inverted_index.71,431 | 113 |
| abstract_inverted_index.María | 38 |
| abstract_inverted_index.Puerto | 40 |
| abstract_inverted_index.States | 26 |
| abstract_inverted_index.Survey | 28 |
| abstract_inverted_index.United | 25 |
| abstract_inverted_index.across | 161 |
| abstract_inverted_index.aerial | 127 |
| abstract_inverted_index.around | 135 |
| abstract_inverted_index.during | 77 |
| abstract_inverted_index.events | 79 |
| abstract_inverted_index.manual | 124 |
| abstract_inverted_index.method | 165 |
| abstract_inverted_index.roads, | 68, 232 |
| abstract_inverted_index.traces | 160 |
| abstract_inverted_index.useful | 98 |
| abstract_inverted_index.correct | 172 |
| abstract_inverted_index.density | 6, 88, 173 |
| abstract_inverted_index.experts | 35 |
| abstract_inverted_index.extent) | 119 |
| abstract_inverted_index.fluvial | 230 |
| abstract_inverted_index.leaving | 159 |
| abstract_inverted_index.obtains | 166 |
| abstract_inverted_index.pixels, | 181 |
| abstract_inverted_index.pixels. | 163 |
| abstract_inverted_index.produce | 4, 85 |
| abstract_inverted_index.reports | 209 |
| abstract_inverted_index.several | 162 |
| abstract_inverted_index.smaller | 141 |
| abstract_inverted_index.sources | 225 |
| abstract_inverted_index.terrain | 62 |
| abstract_inverted_index.typical | 145 |
| abstract_inverted_index.$\times$ | 150 |
| abstract_inverted_index.Aperture | 10 |
| abstract_inverted_index.although | 152 |
| abstract_inverted_index.contains | 109 |
| abstract_inverted_index.enabling | 199 |
| abstract_inverted_index.imagery. | 130 |
| abstract_inverted_index.measured | 216 |
| abstract_inverted_index.obtained | 122 |
| abstract_inverted_index.probably | 158 |
| abstract_inverted_index.regions, | 81 |
| abstract_inverted_index.response | 103, 244 |
| abstract_inverted_index.Hurricane | 37 |
| abstract_inverted_index.Inventory | 31, 108 |
| abstract_inverted_index.Landslide | 30, 107 |
| abstract_inverted_index.Synthetic | 9 |
| abstract_inverted_index.annotated | 33 |
| abstract_inverted_index.available | 76, 240 |
| abstract_inverted_index.channels, | 231 |
| abstract_inverted_index.closeness | 64 |
| abstract_inverted_index.disaster. | 204 |
| abstract_inverted_index.elevation | 92, 187 |
| abstract_inverted_index.emergency | 17, 245 |
| abstract_inverted_index.estimated | 133 |
| abstract_inverted_index.estimates | 7 |
| abstract_inverted_index.extensive | 53 |
| abstract_inverted_index.features, | 63 |
| abstract_inverted_index.imageries | 14 |
| abstract_inverted_index.landslide | 5, 87, 114, 219 |
| abstract_inverted_index.moisture, | 60 |
| abstract_inverted_index.proximity | 228 |
| abstract_inverted_index.regularly | 238 |
| abstract_inverted_index.resources | 18 |
| abstract_inverted_index.response. | 21 |
| abstract_inverted_index.satellite | 13, 129 |
| abstract_inverted_index.scenario. | 246 |
| abstract_inverted_index.waterways | 66 |
| abstract_inverted_index.Geological | 27 |
| abstract_inverted_index.Sentinel-1 | 144, 183 |
| abstract_inverted_index.additional | 54, 223 |
| abstract_inverted_index.assessment | 201 |
| abstract_inverted_index.estimation | 174 |
| abstract_inverted_index.geological | 61 |
| abstract_inverted_index.inspection | 125 |
| abstract_inverted_index.landslides | 139 |
| abstract_inverted_index.predicting | 170 |
| abstract_inverted_index.prioritise | 16 |
| abstract_inverted_index.scenarios. | 104 |
| abstract_inverted_index.subsequent | 48 |
| abstract_inverted_index.coordinates | 111 |
| abstract_inverted_index.information | 55, 224 |
| abstract_inverted_index.resolution) | 184 |
| abstract_inverted_index.acquisitions | 194 |
| abstract_inverted_index.Susceptibility | 207 |
| abstract_inverted_index.precipitation, | 58, 233 |
| abstract_inverted_index.susceptibility | 49 |
| abstract_inverted_index.(128$\times$128 | 180 |
| abstract_inverted_index.decision-makers | 100 |
| abstract_inverted_index.post-hurricane, | 197 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile |