Using machine learning and satellite data from multiple sources to analyze mining, water management, and preservation of cultural heritage Article Swipe
Joaquim J. Sousa
,
Jiahui Lin
,
Qun Wang
,
Guang Liu
,
Jinghui Fan
,
Shibiao Bai
,
Hongli Zhao
,
Hongyu Pan
,
Wenjing Wei
,
Vanessa Rittlinger
,
Peter Mayrhofer
,
Ruth Sonnenschein
,
Stefan Steger
,
Luís Paulo Reis
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1080/10095020.2023.2234008
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1080/10095020.2023.2234008
Remote sensing, particularly satellite-based, can play a valuable role in monitoring areas prone to geohazards. The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards. This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data, with a particular emphasis on: (1) detecting mining sub-sidence, where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar (InSAR) technology. The results showed that the Efficient Channel Attention (ECA) U-Net model performed better than the U-Net (baseline) model in terms of Mean Intersection over Union (MIoU) and Intersection over Union (IoU) indicators; (2) monitor-ing water conservancy and hydropower engineering. The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets (SBAS) InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side. The dam body also showed obvious deformation with a velocity exceeding 60 mm/a; (3) the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation. The overall outcome of these methods showed that the use of Artificial Intelligence (AI) techniques in combination with InSAR data leads to more efficient analysis and interpretation, resulting in improved accuracy and prompt identification of potential hazards; and (4) finally, this study also presents a method for detecting landslides in mountainous regions, using optical imagery. The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods, this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.
Related Topics
Concepts
Interferometric synthetic aperture radar
GNSS augmentation
Remote sensing
Baseline (sea)
Synthetic aperture radar
Hydropower
Satellite
Intersection (aeronautics)
Warning system
Computer science
Environmental science
Data mining
Engineering
Geology
Global Positioning System
Cartography
Geography
Telecommunications
GNSS applications
Oceanography
Electrical engineering
Aerospace engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/10095020.2023.2234008
- OA Status
- gold
- Cited By
- 17
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385260057
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385260057Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1080/10095020.2023.2234008Digital Object Identifier
- Title
-
Using machine learning and satellite data from multiple sources to analyze mining, water management, and preservation of cultural heritageWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-25Full publication date if available
- Authors
-
Joaquim J. Sousa, Jiahui Lin, Qun Wang, Guang Liu, Jinghui Fan, Shibiao Bai, Hongli Zhao, Hongyu Pan, Wenjing Wei, Vanessa Rittlinger, Peter Mayrhofer, Ruth Sonnenschein, Stefan Steger, Luís Paulo ReisList of authors in order
- Landing page
-
https://doi.org/10.1080/10095020.2023.2234008Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1080/10095020.2023.2234008Direct OA link when available
- Concepts
-
Interferometric synthetic aperture radar, GNSS augmentation, Remote sensing, Baseline (sea), Synthetic aperture radar, Hydropower, Satellite, Intersection (aeronautics), Warning system, Computer science, Environmental science, Data mining, Engineering, Geology, Global Positioning System, Cartography, Geography, Telecommunications, GNSS applications, Oceanography, Electrical engineering, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 8Per-year citation counts (last 5 years)
- References (count)
-
59Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3095524684, https://openalex.org/W2885995970, https://openalex.org/W4205324960, https://openalex.org/W2152657318, https://openalex.org/W3033097784, https://openalex.org/W2775427771, https://openalex.org/W2928870406, https://openalex.org/W3010863083, https://openalex.org/W2794274366, https://openalex.org/W2172037674, https://openalex.org/W4289942326, https://openalex.org/W3170871988, https://openalex.org/W2904122576, https://openalex.org/W3026855943, https://openalex.org/W2101671134, https://openalex.org/W2047029664, https://openalex.org/W2945667213, https://openalex.org/W3054091620, https://openalex.org/W2058082754, https://openalex.org/W3096889504, https://openalex.org/W2194775991, https://openalex.org/W2121773078, https://openalex.org/W1672745209, https://openalex.org/W2612445135, https://openalex.org/W1983186335, https://openalex.org/W112357801, https://openalex.org/W3132400172, https://openalex.org/W2618530766, https://openalex.org/W2086359768, https://openalex.org/W1991684106, https://openalex.org/W3113785571, https://openalex.org/W2193145675, https://openalex.org/W3018020455, https://openalex.org/W2151111086, https://openalex.org/W2062798124, https://openalex.org/W2045097799, https://openalex.org/W2793831793, https://openalex.org/W2945861368, https://openalex.org/W3002073658, https://openalex.org/W2896857663, https://openalex.org/W2265581300, https://openalex.org/W3194730353, https://openalex.org/W2775127113, https://openalex.org/W1686810756, https://openalex.org/W3208993845, https://openalex.org/W3133392705, https://openalex.org/W2097117768, https://openalex.org/W3004779205, https://openalex.org/W2161336494, https://openalex.org/W2092141993, https://openalex.org/W3034552520, https://openalex.org/W2907663452, https://openalex.org/W3111050907, https://openalex.org/W3132766222, https://openalex.org/W2141414687, https://openalex.org/W3083624172, https://openalex.org/W2774320778, https://openalex.org/W2302096746, https://openalex.org/W3106250896 |
| referenced_works_count | 59 |
| abstract_inverted_index.a | 6, 43, 62, 71, 176, 246, 266 |
| abstract_inverted_index.60 | 179 |
| abstract_inverted_index.an | 78 |
| abstract_inverted_index.as | 296 |
| abstract_inverted_index.be | 26 |
| abstract_inverted_index.by | 22, 76 |
| abstract_inverted_index.in | 9, 107, 217, 230, 251 |
| abstract_inverted_index.is | 74, 263 |
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| abstract_inverted_index.on | 142, 157, 164, 282 |
| abstract_inverted_index.to | 13, 28, 190, 223 |
| abstract_inverted_index.(1) | 66 |
| abstract_inverted_index.(2) | 121 |
| abstract_inverted_index.(3) | 181 |
| abstract_inverted_index.(4) | 240 |
| abstract_inverted_index.The | 15, 89, 128, 168, 202, 257 |
| abstract_inverted_index.and | 18, 32, 47, 55, 82, 115, 125, 147, 193, 199, 227, 233, 239, 270, 285 |
| abstract_inverted_index.any | 283 |
| abstract_inverted_index.can | 4, 25, 286 |
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| abstract_inverted_index.not | 280 |
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| abstract_inverted_index.(AI) | 215 |
| abstract_inverted_index.Mean | 110 |
| abstract_inverted_index.This | 40 |
| abstract_inverted_index.also | 171, 244 |
| abstract_inverted_index.bank | 163 |
| abstract_inverted_index.body | 170 |
| abstract_inverted_index.data | 24, 146, 221 |
| abstract_inverted_index.does | 279 |
| abstract_inverted_index.four | 148 |
| abstract_inverted_index.high | 16, 152 |
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| abstract_inverted_index.used | 27 |
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| abstract_inverted_index.with | 61, 151, 175, 219 |
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| abstract_inverted_index.(IoU) | 119 |
| abstract_inverted_index.Earth | 58 |
| abstract_inverted_index.InSAR | 140, 188, 220 |
| abstract_inverted_index.Radar | 86 |
| abstract_inverted_index.Small | 136 |
| abstract_inverted_index.U-Net | 80, 98, 104 |
| abstract_inverted_index.Union | 113, 118 |
| abstract_inverted_index.areas | 11, 36 |
| abstract_inverted_index.data, | 60 |
| abstract_inverted_index.leads | 222 |
| abstract_inverted_index.mm/a; | 180 |
| abstract_inverted_index.model | 81, 99, 106 |
| abstract_inverted_index.north | 166 |
| abstract_inverted_index.novel | 72 |
| abstract_inverted_index.paper | 41 |
| abstract_inverted_index.prone | 12 |
| abstract_inverted_index.range | 44 |
| abstract_inverted_index.rates | 154 |
| abstract_inverted_index.side. | 167 |
| abstract_inverted_index.slope | 159 |
| abstract_inverted_index.small | 149 |
| abstract_inverted_index.study | 243 |
| abstract_inverted_index.terms | 108 |
| abstract_inverted_index.these | 206 |
| abstract_inverted_index.using | 135, 254 |
| abstract_inverted_index.water | 123 |
| abstract_inverted_index.where | 70 |
| abstract_inverted_index.(MIoU) | 114 |
| abstract_inverted_index.(SBAS) | 139 |
| abstract_inverted_index.7-year | 267 |
| abstract_inverted_index.Remote | 0 |
| abstract_inverted_index.better | 101 |
| abstract_inverted_index.change | 274 |
| abstract_inverted_index.depend | 281 |
| abstract_inverted_index.detect | 288 |
| abstract_inverted_index.events | 31 |
| abstract_inverted_index.method | 141, 247, 262 |
| abstract_inverted_index.mining | 68 |
| abstract_inverted_index.period | 269 |
| abstract_inverted_index.prompt | 234 |
| abstract_inverted_index.series | 145 |
| abstract_inverted_index.showed | 91, 172, 208 |
| abstract_inverted_index.unlike | 271 |
| abstract_inverted_index.(InSAR) | 87 |
| abstract_inverted_index.BAsline | 137 |
| abstract_inverted_index.Channel | 95 |
| abstract_inverted_index.Subsets | 138 |
| abstract_inverted_index.applied | 51 |
| abstract_inverted_index.complex | 132 |
| abstract_inverted_index.methods | 48, 207 |
| abstract_inverted_index.monitor | 34 |
| abstract_inverted_index.obvious | 173 |
| abstract_inverted_index.optical | 255 |
| abstract_inverted_index.outcome | 204 |
| abstract_inverted_index.overall | 203 |
| abstract_inverted_index.periods | 292 |
| abstract_inverted_index.regions | 150 |
| abstract_inverted_index.results | 90, 189 |
| abstract_inverted_index.spatial | 17 |
| abstract_inverted_index.systems | 195 |
| abstract_inverted_index.warning | 194 |
| abstract_inverted_index.Aperture | 85 |
| abstract_inverted_index.accuracy | 232 |
| abstract_inverted_index.analysis | 54, 226, 268 |
| abstract_inverted_index.approach | 73, 278 |
| abstract_inverted_index.coverage | 20 |
| abstract_inverted_index.decades. | 297 |
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| abstract_inverted_index.extended | 291 |
| abstract_inverted_index.finally, | 241 |
| abstract_inverted_index.hazards. | 39 |
| abstract_inverted_index.hazards; | 238 |
| abstract_inverted_index.heritage | 198 |
| abstract_inverted_index.imagery. | 256 |
| abstract_inverted_index.improved | 79, 231 |
| abstract_inverted_index.in-depth | 53 |
| abstract_inverted_index.methods, | 276 |
| abstract_inverted_index.presents | 42, 245 |
| abstract_inverted_index.proposed | 75 |
| abstract_inverted_index.provided | 21 |
| abstract_inverted_index.regions, | 253 |
| abstract_inverted_index.sensing, | 1 |
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| abstract_inverted_index.valuable | 7, 197 |
| abstract_inverted_index.velocity | 177 |
| abstract_inverted_index.Attention | 96 |
| abstract_inverted_index.Efficient | 94 |
| abstract_inverted_index.Synthetic | 84 |
| abstract_inverted_index.combining | 77 |
| abstract_inverted_index.detecting | 67, 249 |
| abstract_inverted_index.detection | 261, 275 |
| abstract_inverted_index.efficient | 225 |
| abstract_inverted_index.evaluated | 264 |
| abstract_inverted_index.exceeding | 178 |
| abstract_inverted_index.integrate | 191 |
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| abstract_inverted_index.resulting | 229 |
| abstract_inverted_index.satellite | 23 |
| abstract_inverted_index.sensitive | 35 |
| abstract_inverted_index.(baseline) | 105 |
| abstract_inverted_index.Artificial | 213 |
| abstract_inverted_index.Sentinel-1 | 143 |
| abstract_inverted_index.Xiaolangdi | 129 |
| abstract_inverted_index.evaluation | 183 |
| abstract_inverted_index.hydropower | 126 |
| abstract_inverted_index.identified | 156 |
| abstract_inverted_index.landslides | 250, 289 |
| abstract_inverted_index.monitoring | 10, 192 |
| abstract_inverted_index.particular | 63 |
| abstract_inverted_index.techniques | 46, 216 |
| abstract_inverted_index.bi-temporal | 273 |
| abstract_inverted_index.combination | 218 |
| abstract_inverted_index.conservancy | 124 |
| abstract_inverted_index.deformation | 153, 174 |
| abstract_inverted_index.geohazards. | 14 |
| abstract_inverted_index.indicators; | 120 |
| abstract_inverted_index.monitor-ing | 122 |
| abstract_inverted_index.mountainous | 252 |
| abstract_inverted_index.observation | 59 |
| abstract_inverted_index.potentially | 287 |
| abstract_inverted_index.reconstruct | 29 |
| abstract_inverted_index.technology. | 88 |
| abstract_inverted_index.utilization | 56 |
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| abstract_inverted_index.engineering. | 127 |
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| abstract_inverted_index.preservation. | 201 |
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| abstract_inverted_index.interpretation, | 228 |
| abstract_inverted_index.prior-knowledge | 284 |
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| corresponding_author_ids | https://openalex.org/A5066556659, https://openalex.org/A5102833310 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 14 |
| corresponding_institution_ids | https://openalex.org/I2799486974, https://openalex.org/I4210166615 |
| citation_normalized_percentile.value | 0.9717053 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |