Assessing climate risks from satellite imagery with machine learning: A case study of flood risks in Jakarta
Consistent and timely assessment of climate risks is crucial for planning disaster mitigation and adaptation to climate change at the local community level. This article presents an automatized method for monitoring climate risks with machine learning on satellite imagery, specially targeting riveri...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Climate Risk Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2212096324000688 |
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| author | Jeasurk Yang Donghyun Ahn Junbeom Bahk Sungwon Park Nurrokhmah Rizqihandari Meeyoung Cha |
| author_facet | Jeasurk Yang Donghyun Ahn Junbeom Bahk Sungwon Park Nurrokhmah Rizqihandari Meeyoung Cha |
| author_sort | Jeasurk Yang |
| collection | DOAJ |
| description | Consistent and timely assessment of climate risks is crucial for planning disaster mitigation and adaptation to climate change at the local community level. This article presents an automatized method for monitoring climate risks with machine learning on satellite imagery, specially targeting riverine and coastal floods. Our research demonstrates that disaster-related risk measurement becomes more comprehensive and multi-faceted by including the following components: hazards, exposure, and vulnerability. Our model first maps hazard-related risks with geo-spatial data, then extends the model to incorporate exposure and vulnerability. In doing so, we adopt a clustering-based supervised algorithm to sort satellite images to produce the climate risk scores at a grid-level. The developed model was tested over multiple ground-truth datasets on flood risks in the region of Jakarta, Indonesia. Results confirm that our model can assess climate risks in a granular scale and further capture potential risks in the marginalized areas (e.g., slums) that were previously hard to predict. We discuss how computational methods like ours can support humanitarian projects for developing countries. |
| format | Article |
| id | doaj-art-8a41b2c16e0e407ea1f53876ed159669 |
| institution | OA Journals |
| issn | 2212-0963 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Climate Risk Management |
| spelling | doaj-art-8a41b2c16e0e407ea1f53876ed1596692025-08-20T02:34:20ZengElsevierClimate Risk Management2212-09632024-01-014610065110.1016/j.crm.2024.100651Assessing climate risks from satellite imagery with machine learning: A case study of flood risks in JakartaJeasurk Yang0Donghyun Ahn1Junbeom Bahk2Sungwon Park3Nurrokhmah Rizqihandari4Meeyoung Cha5Max Planck Institute for Security and Privacy, Bochum, Germany; Corresponding authors.Max Planck Institute for Security and Privacy, Bochum, GermanyDepartment of Geography, Seoul National University, Seoul, Republic of KoreaSchool of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of KoreaDepartment of Geography, Universitas Indonesia, West Java, IndonesiaMax Planck Institute for Security and Privacy, Bochum, Germany; School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea; Corresponding authors.Consistent and timely assessment of climate risks is crucial for planning disaster mitigation and adaptation to climate change at the local community level. This article presents an automatized method for monitoring climate risks with machine learning on satellite imagery, specially targeting riverine and coastal floods. Our research demonstrates that disaster-related risk measurement becomes more comprehensive and multi-faceted by including the following components: hazards, exposure, and vulnerability. Our model first maps hazard-related risks with geo-spatial data, then extends the model to incorporate exposure and vulnerability. In doing so, we adopt a clustering-based supervised algorithm to sort satellite images to produce the climate risk scores at a grid-level. The developed model was tested over multiple ground-truth datasets on flood risks in the region of Jakarta, Indonesia. Results confirm that our model can assess climate risks in a granular scale and further capture potential risks in the marginalized areas (e.g., slums) that were previously hard to predict. We discuss how computational methods like ours can support humanitarian projects for developing countries.http://www.sciencedirect.com/science/article/pii/S2212096324000688Computer vision algorithm1Climate risks2Exposure3Vulnerability4Hazards5 |
| spellingShingle | Jeasurk Yang Donghyun Ahn Junbeom Bahk Sungwon Park Nurrokhmah Rizqihandari Meeyoung Cha Assessing climate risks from satellite imagery with machine learning: A case study of flood risks in Jakarta Climate Risk Management Computer vision algorithm1 Climate risks2 Exposure3 Vulnerability4 Hazards5 |
| title | Assessing climate risks from satellite imagery with machine learning: A case study of flood risks in Jakarta |
| title_full | Assessing climate risks from satellite imagery with machine learning: A case study of flood risks in Jakarta |
| title_fullStr | Assessing climate risks from satellite imagery with machine learning: A case study of flood risks in Jakarta |
| title_full_unstemmed | Assessing climate risks from satellite imagery with machine learning: A case study of flood risks in Jakarta |
| title_short | Assessing climate risks from satellite imagery with machine learning: A case study of flood risks in Jakarta |
| title_sort | assessing climate risks from satellite imagery with machine learning a case study of flood risks in jakarta |
| topic | Computer vision algorithm1 Climate risks2 Exposure3 Vulnerability4 Hazards5 |
| url | http://www.sciencedirect.com/science/article/pii/S2212096324000688 |
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