Construction of Remote Sensing Early Warning Knowledge Graph Based on Multi-Source Disaster Data
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| Gepubliceerd in: | Remote Sensing vol. 17, no. 21 (2025), p. 3594-3612 |
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| 001 | 3271543987 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17213594 |2 doi | |
| 035 | |a 3271543987 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Chen Miaoying |u State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; 202221051091@mail.bnu.edu.cn | |
| 245 | 1 | |a Construction of Remote Sensing Early Warning Knowledge Graph Based on Multi-Source Disaster Data | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a <sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> Constructed a multi-source disaster knowledge graph integrating remote sensing and statistical data for unified visual query. </list-item> <list-item> Proposed a quantitative remote sensing method to assess disaster intensity, impact, and trends, enabling cross-domain knowledge conversion. </list-item> What are the implications of the main findings? <list list-type="bullet"> <list-item> The “data silo” gap between remote sensing monitoring and sector-specific disaster management is bridged. </list-item> <list-item> A rule-based decision-support tool for scientific and intelligent disaster early warning is provided. </list-item> Natural disasters occur continuously across the globe, posing severe threats to human life and property. Remote sensing technology has provided powerful technical means for large-scale and rapid disaster monitoring. However, the deep integration of remote sensing observations with sector-specific disaster statistical data to construct a knowledge system that supports early warning decision-making remains a significant challenge. This study aims to address the bottleneck in the “data-information-knowledge-service” transformation process by constructing an integrated natural disaster early warning knowledge graph that incorporates multi-source heterogeneous data. We first designed an ontological schema layer comprising six core elements: disaster type, event, anomaly information, impact information, warning information, and decision information. Subsequently, multi-source data were integrated from various sources, including the Emergency Events Database (EM-DAT), sector-specific websites, encyclopedic pages, and remote sensing imagery such as Gaofen-2 (GF-2) and Sentinel-1. A Bidirectional Encoder Representations from Transformers with a Conditional Random Field layer (BERT-CRF) model was employed for entity and relation extraction, and the knowledge was stored and visualized using the Neo4j graph database. The core innovation of this research lies in proposing a quantitative methodology for assessing disaster intensity, impact, and trends based on remote sensing evaluation, establishing a knowledge conversion mechanism with sector-specific warning levels, and designing explicit warning issuance rules. A case study on a specific wildfire event (2017-0417-PRT, Coimbra, Portugal) demonstrates that the knowledge graph not only achieves organic integration and visual querying of multi-source disaster knowledge but also facilitates warning decision-making driven by remote sensing assessment indicators. For this event, quantitative analysis of Gaofen-2 imagery yielded intensity, impact, and trend levels of 4, 3, and 3, respectively, which, when applied to our warning rule (intensity ≥ 1 or impact ≥ 1 or trend ≥ 3), automatically triggered an early warning, thereby validating the rule’s practicality. A preliminary performance evaluation on 50 historical wildfire events demonstrated promising results, with an F1-score of 74.3% and an average query response time of 128 ms, confirming the system’s practical responsiveness and detection capability. In conclusion, this study offers a novel and operational technical pathway for the deep interdisciplinary integration of remote sensing and disaster science, effectively bridging the gap between data silos and actionable warning knowledge. | |
| 610 | 4 | |a United Nations--UN | |
| 653 | |a Statistics | ||
| 653 | |a Databases | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Disaster recovery | ||
| 653 | |a Conditional random fields | ||
| 653 | |a Ontology | ||
| 653 | |a Early warning systems | ||
| 653 | |a Remote sensing | ||
| 653 | |a Disaster management | ||
| 653 | |a Remote monitoring | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Emergency preparedness | ||
| 653 | |a Knowledge representation | ||
| 653 | |a Quantitative analysis | ||
| 653 | |a Risk assessment | ||
| 653 | |a Wildfires | ||
| 653 | |a Decision support systems | ||
| 653 | |a Natural disasters | ||
| 653 | |a Trends | ||
| 653 | |a Decision making | ||
| 653 | |a Knowledge | ||
| 653 | |a Emergency communications systems | ||
| 653 | |a Earthquakes | ||
| 653 | |a Information processing | ||
| 653 | |a Forest & brush fires | ||
| 653 | |a Satellites | ||
| 653 | |a Imagery | ||
| 653 | |a Integration | ||
| 653 | |a Semantics | ||
| 700 | 1 | |a Cao Xin |u State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; 202221051091@mail.bnu.edu.cn | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 21 (2025), p. 3594-3612 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3271543987/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3271543987/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3271543987/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |