When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review
Earth observations (EOs) have successfully been used to train artificial intelligence (AI)-based models in the field of disaster risk reduction (DRR) contributing to tools such as disaster early warning systems. Given the number of in situ and remote (e.g. radiosonde/satellite) monitoring devices, t...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2023-01-01
|
| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/acf601 |
| _version_ | 1827815227749564416 |
|---|---|
| author | Monique M Kuglitsch Arif Albayrak Jürg Luterbacher Allison Craddock Andrea Toreti Jackie Ma Paula Padrino Vilela Elena Xoplaki Rui Kotani Dominique Berod Jon Cox Ivanka Pelivan |
| author_facet | Monique M Kuglitsch Arif Albayrak Jürg Luterbacher Allison Craddock Andrea Toreti Jackie Ma Paula Padrino Vilela Elena Xoplaki Rui Kotani Dominique Berod Jon Cox Ivanka Pelivan |
| author_sort | Monique M Kuglitsch |
| collection | DOAJ |
| description | Earth observations (EOs) have successfully been used to train artificial intelligence (AI)-based models in the field of disaster risk reduction (DRR) contributing to tools such as disaster early warning systems. Given the number of in situ and remote (e.g. radiosonde/satellite) monitoring devices, there is a common perception that there are no limits to the availability of EO for immediate use in such AI-based models. However, a mere fraction of EO is actually being used in this way. This topical review draws on use cases, workshop presentations, literature, and consultation with experts from key institutes to explore reasons for this discrepancy. Specifically, it evaluates the types of EO needed to train AI-based models for DRR applications and identifies the main characteristics, possible challenges, and innovative solutions for EO. Finally, it suggests ways to make EO more user ready and to facilitate its uptake in AI for DRR and beyond. |
| first_indexed | 2024-03-11T23:57:11Z |
| format | Article |
| id | doaj.art-76c7670465f049ada5163d93315e8139 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-11T23:57:11Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-76c7670465f049ada5163d93315e81392023-09-18T07:51:33ZengIOP PublishingEnvironmental Research Letters1748-93262023-01-0118909300410.1088/1748-9326/acf601When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical reviewMonique M Kuglitsch0Arif Albayrak1Jürg Luterbacher2Allison Craddock3Andrea Toreti4Jackie Ma5Paula Padrino Vilela6Elena Xoplaki7Rui Kotani8Dominique Berod9Jon Cox10Ivanka Pelivan11Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute , Berlin, GermanyNASA Goddard Space Flight Center , Greenbelt, MD, United States of AmericaWorld Meteorological Organization , Geneva, SwitzerlandNASA Jet Propulsion Laboratory, California Institute of Technology , Pasadena, CA, United States of AmericaEuropean Commission Joint Research Centre , Ispra, ItalyFraunhofer Institute for Telecommunications, Heinrich Hertz Institute , Berlin, GermanyUN Environment , Geneva, SwitzerlandDepartment of Geography, Climatology, Climate Dynamics and Climate Change, Justus Liebig University Giessen , Giessen, Germany; Centre of International Development and Environmental Research, Justus Liebig University Giessen , Giessen, GermanyGroup on Earth Observations , Geneva, SwitzerlandWorld Meteorological Organization , Geneva, SwitzerlandWorld Meteorological Organization , Geneva, SwitzerlandFraunhofer Institute for Telecommunications, Heinrich Hertz Institute , Berlin, GermanyEarth observations (EOs) have successfully been used to train artificial intelligence (AI)-based models in the field of disaster risk reduction (DRR) contributing to tools such as disaster early warning systems. Given the number of in situ and remote (e.g. radiosonde/satellite) monitoring devices, there is a common perception that there are no limits to the availability of EO for immediate use in such AI-based models. However, a mere fraction of EO is actually being used in this way. This topical review draws on use cases, workshop presentations, literature, and consultation with experts from key institutes to explore reasons for this discrepancy. Specifically, it evaluates the types of EO needed to train AI-based models for DRR applications and identifies the main characteristics, possible challenges, and innovative solutions for EO. Finally, it suggests ways to make EO more user ready and to facilitate its uptake in AI for DRR and beyond.https://doi.org/10.1088/1748-9326/acf601Earth observationremote sensingdisaster risk reductionartificial intelligencemachine learning |
| spellingShingle | Monique M Kuglitsch Arif Albayrak Jürg Luterbacher Allison Craddock Andrea Toreti Jackie Ma Paula Padrino Vilela Elena Xoplaki Rui Kotani Dominique Berod Jon Cox Ivanka Pelivan When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review Environmental Research Letters Earth observation remote sensing disaster risk reduction artificial intelligence machine learning |
| title | When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review |
| title_full | When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review |
| title_fullStr | When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review |
| title_full_unstemmed | When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review |
| title_short | When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review |
| title_sort | when it comes to earth observations in ai for disaster risk reduction is it feast or famine a topical review |
| topic | Earth observation remote sensing disaster risk reduction artificial intelligence machine learning |
| url | https://doi.org/10.1088/1748-9326/acf601 |
| work_keys_str_mv | AT moniquemkuglitsch whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview AT arifalbayrak whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview AT jurgluterbacher whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview AT allisoncraddock whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview AT andreatoreti whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview AT jackiema whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview AT paulapadrinovilela whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview AT elenaxoplaki whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview AT ruikotani whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview AT dominiqueberod whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview AT joncox whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview AT ivankapelivan whenitcomestoearthobservationsinaifordisasterriskreductionisitfeastorfamineatopicalreview |