Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes 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.2211.02869
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.02869
With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing technique that can provide measurements of affected areas independent of weather or lighting conditions. Usage of SAR, however, is hindered by domain knowledge that is necessary for the pre-processing steps and its interpretation requires expert knowledge. We provide simplified, pre-processed, machine-learning ready SAR datacubes for four globally located landslide events obtained from several Sentinel-1 satellite passes before and after a landslide triggering event together with segmentation maps of the landslides. From this dataset, using the Hokkaido, Japan datacube, we study the feasibility of SAR-based landslide detection with supervised deep learning (DL). Our results demonstrate that DL models can be used to detect landslides from SAR data, achieving an Area under the Precision-Recall curve exceeding 0.7. We find that additional satellite visits enhance detection performance, but that early detection is possible when SAR data is combined with terrain information from a digital elevation model. This can be especially useful for time-critical emergency interventions. Code is made publicly available at https://github.com/iprapas/landslide-sar-unet.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.02869
- https://arxiv.org/pdf/2211.02869
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308611022
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4308611022Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2211.02869Digital Object Identifier
- Title
-
Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) DatacubesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-05Full publication date if available
- Authors
-
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/2211.02869Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2211.02869Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2211.02869Direct OA link when available
- Concepts
-
Landslide, Synthetic aperture radar, Computer science, Remote sensing, Artificial intelligence, Satellite, Terrain, Interferometric synthetic aperture radar, Segmentation, Geology, Cartography, Geography, Seismology, Engineering, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2023: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4308611022 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2211.02869 |
| ids.doi | https://doi.org/10.48550/arxiv.2211.02869 |
| ids.openalex | https://openalex.org/W4308611022 |
| fwci | |
| type | preprint |
| title | Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10535 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9993000030517578 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2308 |
| topics[0].subfield.display_name | Management, Monitoring, Policy and Law |
| topics[0].display_name | Landslides and related hazards |
| topics[1].id | https://openalex.org/T10644 |
| topics[1].field.id | https://openalex.org/fields/19 |
| topics[1].field.display_name | Earth and Planetary Sciences |
| topics[1].score | 0.9939000010490417 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1902 |
| topics[1].subfield.display_name | Atmospheric Science |
| topics[1].display_name | Cryospheric studies and observations |
| topics[2].id | https://openalex.org/T10801 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9728999733924866 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2202 |
| topics[2].subfield.display_name | Aerospace Engineering |
| topics[2].display_name | Synthetic Aperture Radar (SAR) Applications and Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C186295008 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8797101378440857 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q167903 |
| concepts[0].display_name | Landslide |
| concepts[1].id | https://openalex.org/C87360688 |
| concepts[1].level | 2 |
| concepts[1].score | 0.732812225818634 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q740686 |
| concepts[1].display_name | Synthetic aperture radar |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6064538359642029 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C62649853 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5574378371238708 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[3].display_name | Remote sensing |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5293484330177307 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C19269812 |
| concepts[5].level | 2 |
| concepts[5].score | 0.47035789489746094 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q26540 |
| concepts[5].display_name | Satellite |
| concepts[6].id | https://openalex.org/C161840515 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4654081165790558 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q186131 |
| concepts[6].display_name | Terrain |
| concepts[7].id | https://openalex.org/C22286887 |
| concepts[7].level | 3 |
| concepts[7].score | 0.42962077260017395 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1666056 |
| concepts[7].display_name | Interferometric synthetic aperture radar |
| concepts[8].id | https://openalex.org/C89600930 |
| concepts[8].level | 2 |
| concepts[8].score | 0.41448312997817993 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[8].display_name | Segmentation |
| concepts[9].id | https://openalex.org/C127313418 |
| concepts[9].level | 0 |
| concepts[9].score | 0.3068732023239136 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[9].display_name | Geology |
| concepts[10].id | https://openalex.org/C58640448 |
| concepts[10].level | 1 |
| concepts[10].score | 0.20882639288902283 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q42515 |
| concepts[10].display_name | Cartography |
| concepts[11].id | https://openalex.org/C205649164 |
| concepts[11].level | 0 |
| concepts[11].score | 0.14146265387535095 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[11].display_name | Geography |
| concepts[12].id | https://openalex.org/C165205528 |
| concepts[12].level | 1 |
| concepts[12].score | 0.1212429404258728 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q83371 |
| concepts[12].display_name | Seismology |
| concepts[13].id | https://openalex.org/C127413603 |
| concepts[13].level | 0 |
| concepts[13].score | 0.07319551706314087 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[13].display_name | Engineering |
| concepts[14].id | https://openalex.org/C146978453 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q3798668 |
| concepts[14].display_name | Aerospace engineering |
| keywords[0].id | https://openalex.org/keywords/landslide |
| keywords[0].score | 0.8797101378440857 |
| keywords[0].display_name | Landslide |
| keywords[1].id | https://openalex.org/keywords/synthetic-aperture-radar |
| keywords[1].score | 0.732812225818634 |
| keywords[1].display_name | Synthetic aperture radar |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6064538359642029 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/remote-sensing |
| keywords[3].score | 0.5574378371238708 |
| keywords[3].display_name | Remote sensing |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5293484330177307 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/satellite |
| keywords[5].score | 0.47035789489746094 |
| keywords[5].display_name | Satellite |
| keywords[6].id | https://openalex.org/keywords/terrain |
| keywords[6].score | 0.4654081165790558 |
| keywords[6].display_name | Terrain |
| keywords[7].id | https://openalex.org/keywords/interferometric-synthetic-aperture-radar |
| keywords[7].score | 0.42962077260017395 |
| keywords[7].display_name | Interferometric synthetic aperture radar |
| keywords[8].id | https://openalex.org/keywords/segmentation |
| keywords[8].score | 0.41448312997817993 |
| keywords[8].display_name | Segmentation |
| keywords[9].id | https://openalex.org/keywords/geology |
| keywords[9].score | 0.3068732023239136 |
| keywords[9].display_name | Geology |
| keywords[10].id | https://openalex.org/keywords/cartography |
| keywords[10].score | 0.20882639288902283 |
| keywords[10].display_name | Cartography |
| keywords[11].id | https://openalex.org/keywords/geography |
| keywords[11].score | 0.14146265387535095 |
| keywords[11].display_name | Geography |
| keywords[12].id | https://openalex.org/keywords/seismology |
| keywords[12].score | 0.1212429404258728 |
| keywords[12].display_name | Seismology |
| keywords[13].id | https://openalex.org/keywords/engineering |
| keywords[13].score | 0.07319551706314087 |
| keywords[13].display_name | Engineering |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2211.02869 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2211.02869 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2211.02869 |
| locations[1].id | doi:10.48550/arxiv.2211.02869 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2211.02869 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5001343292 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-5409-4821 |
| authorships[0].author.display_name | V. Boehm |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Boehm, Vanessa |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5091877085 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-2354-1988 |
| authorships[1].author.display_name | Wei Ji Leong |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Leong, Wei Ji |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5045980273 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2747-9811 |
| authorships[2].author.display_name | Ragini Bal Mahesh |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Mahesh, Ragini Bal |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5028404103 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Ioannis Prapas |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Prapas, Ioannis |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5036023440 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-0166-4613 |
| authorships[4].author.display_name | Edoardo Nemni |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Nemni, Edoardo |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5029587041 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-1471-646X |
| authorships[5].author.display_name | Freddie Kalaitzis |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Kalaitzis, Freddie |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5048162043 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-9462-4898 |
| authorships[6].author.display_name | Siddha Ganju |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Ganju, Siddha |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5011088033 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-6195-3612 |
| authorships[7].author.display_name | Raúl Ramos-Pollán |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Ramos-Pollan, Raul |
| authorships[7].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2211.02869 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2022-11-13T00:00:00 |
| display_name | Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10535 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9993000030517578 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2308 |
| primary_topic.subfield.display_name | Management, Monitoring, Policy and Law |
| primary_topic.display_name | Landslides and related hazards |
| related_works | https://openalex.org/W2389676928, https://openalex.org/W3169474304, https://openalex.org/W2369104181, https://openalex.org/W3201652628, https://openalex.org/W4212972401, https://openalex.org/W2389287188, https://openalex.org/W3081499580, https://openalex.org/W3206958763, https://openalex.org/W2159160885, https://openalex.org/W2148958581 |
| cited_by_count | 5 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 3 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2211.02869 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2211.02869 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2211.02869 |
| primary_location.id | pmh:oai:arXiv.org:2211.02869 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2211.02869 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2211.02869 |
| publication_date | 2022-11-05 |
| publication_year | 2022 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 13, 31, 93, 173 |
| abstract_inverted_index.DL | 129 |
| abstract_inverted_index.We | 70, 149 |
| abstract_inverted_index.an | 141 |
| abstract_inverted_index.at | 191 |
| abstract_inverted_index.be | 132, 179 |
| abstract_inverted_index.by | 54 |
| abstract_inverted_index.is | 12, 30, 52, 58, 162, 167, 187 |
| abstract_inverted_index.of | 8, 39, 43, 49, 101, 116 |
| abstract_inverted_index.or | 45 |
| abstract_inverted_index.to | 4, 134 |
| abstract_inverted_index.we | 112 |
| abstract_inverted_index.Our | 125 |
| abstract_inverted_index.SAR | 76, 138, 165 |
| abstract_inverted_index.and | 64, 91 |
| abstract_inverted_index.but | 158 |
| abstract_inverted_index.can | 36, 131, 178 |
| abstract_inverted_index.for | 16, 60, 78, 182 |
| abstract_inverted_index.its | 65 |
| abstract_inverted_index.the | 6, 61, 102, 108, 114, 144 |
| abstract_inverted_index.0.7. | 148 |
| abstract_inverted_index.Area | 142 |
| abstract_inverted_index.Code | 186 |
| abstract_inverted_index.From | 104 |
| abstract_inverted_index.SAR, | 50 |
| abstract_inverted_index.This | 177 |
| abstract_inverted_index.With | 0 |
| abstract_inverted_index.data | 166 |
| abstract_inverted_index.deep | 122 |
| abstract_inverted_index.find | 150 |
| abstract_inverted_index.four | 79 |
| abstract_inverted_index.from | 85, 137, 172 |
| abstract_inverted_index.help | 22 |
| abstract_inverted_index.made | 188 |
| abstract_inverted_index.maps | 100 |
| abstract_inverted_index.need | 15 |
| abstract_inverted_index.that | 21, 35, 57, 128, 151, 159 |
| abstract_inverted_index.this | 105 |
| abstract_inverted_index.used | 133 |
| abstract_inverted_index.when | 164 |
| abstract_inverted_index.with | 98, 120, 169 |
| abstract_inverted_index.(DL). | 124 |
| abstract_inverted_index.(SAR) | 29 |
| abstract_inverted_index.Japan | 110 |
| abstract_inverted_index.Radar | 28 |
| abstract_inverted_index.Usage | 48 |
| abstract_inverted_index.after | 92 |
| abstract_inverted_index.areas | 41 |
| abstract_inverted_index.curve | 146 |
| abstract_inverted_index.data, | 139 |
| abstract_inverted_index.early | 160 |
| abstract_inverted_index.event | 96 |
| abstract_inverted_index.rapid | 17 |
| abstract_inverted_index.ready | 75 |
| abstract_inverted_index.steps | 63 |
| abstract_inverted_index.study | 113 |
| abstract_inverted_index.there | 11 |
| abstract_inverted_index.under | 143 |
| abstract_inverted_index.using | 107 |
| abstract_inverted_index.before | 90 |
| abstract_inverted_index.change | 2 |
| abstract_inverted_index.detect | 135 |
| abstract_inverted_index.domain | 55 |
| abstract_inverted_index.events | 83 |
| abstract_inverted_index.expert | 68 |
| abstract_inverted_index.inform | 23 |
| abstract_inverted_index.model. | 176 |
| abstract_inverted_index.models | 130 |
| abstract_inverted_index.passes | 89 |
| abstract_inverted_index.remote | 32 |
| abstract_inverted_index.useful | 181 |
| abstract_inverted_index.visits | 154 |
| abstract_inverted_index.climate | 1 |
| abstract_inverted_index.digital | 174 |
| abstract_inverted_index.enhance | 155 |
| abstract_inverted_index.events, | 10 |
| abstract_inverted_index.growing | 14 |
| abstract_inverted_index.located | 81 |
| abstract_inverted_index.provide | 37, 71 |
| abstract_inverted_index.results | 126 |
| abstract_inverted_index.sensing | 33 |
| abstract_inverted_index.several | 86 |
| abstract_inverted_index.terrain | 170 |
| abstract_inverted_index.weather | 44 |
| abstract_inverted_index.Aperture | 27 |
| abstract_inverted_index.affected | 40 |
| abstract_inverted_index.combined | 168 |
| abstract_inverted_index.dataset, | 106 |
| abstract_inverted_index.globally | 80 |
| abstract_inverted_index.hindered | 53 |
| abstract_inverted_index.however, | 51 |
| abstract_inverted_index.increase | 5 |
| abstract_inverted_index.learning | 123 |
| abstract_inverted_index.lighting | 46 |
| abstract_inverted_index.obtained | 84 |
| abstract_inverted_index.possible | 163 |
| abstract_inverted_index.publicly | 189 |
| abstract_inverted_index.requires | 67 |
| abstract_inverted_index.together | 97 |
| abstract_inverted_index.Hokkaido, | 109 |
| abstract_inverted_index.SAR-based | 117 |
| abstract_inverted_index.Synthetic | 26 |
| abstract_inverted_index.achieving | 140 |
| abstract_inverted_index.available | 190 |
| abstract_inverted_index.datacube, | 111 |
| abstract_inverted_index.datacubes | 77 |
| abstract_inverted_index.detection | 19, 119, 156, 161 |
| abstract_inverted_index.elevation | 175 |
| abstract_inverted_index.emergency | 24, 184 |
| abstract_inverted_index.exceeding | 147 |
| abstract_inverted_index.knowledge | 56 |
| abstract_inverted_index.landslide | 9, 18, 82, 94, 118 |
| abstract_inverted_index.necessary | 59 |
| abstract_inverted_index.predicted | 3 |
| abstract_inverted_index.satellite | 88, 153 |
| abstract_inverted_index.technique | 34 |
| abstract_inverted_index.Sentinel-1 | 87 |
| abstract_inverted_index.additional | 152 |
| abstract_inverted_index.especially | 180 |
| abstract_inverted_index.knowledge. | 69 |
| abstract_inverted_index.landslides | 136 |
| abstract_inverted_index.likelihood | 7 |
| abstract_inverted_index.responses. | 25 |
| abstract_inverted_index.supervised | 121 |
| abstract_inverted_index.triggering | 95 |
| abstract_inverted_index.conditions. | 47 |
| abstract_inverted_index.demonstrate | 127 |
| abstract_inverted_index.feasibility | 115 |
| abstract_inverted_index.independent | 42 |
| abstract_inverted_index.information | 171 |
| abstract_inverted_index.landslides. | 103 |
| abstract_inverted_index.simplified, | 72 |
| abstract_inverted_index.measurements | 38 |
| abstract_inverted_index.performance, | 157 |
| abstract_inverted_index.segmentation | 99 |
| abstract_inverted_index.technologies | 20 |
| abstract_inverted_index.time-critical | 183 |
| abstract_inverted_index.interpretation | 66 |
| abstract_inverted_index.interventions. | 185 |
| abstract_inverted_index.pre-processed, | 73 |
| abstract_inverted_index.pre-processing | 62 |
| abstract_inverted_index.Precision-Recall | 145 |
| abstract_inverted_index.machine-learning | 74 |
| abstract_inverted_index.https://github.com/iprapas/landslide-sar-unet. | 192 |
| 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 | 0.800000011920929 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile |