Construction of Remote Sensing Early Warning Knowledge Graph Based on Multi-Source Disaster Data

Abstract

What are the main findings?

Constructed a multi-source disaster knowledge graph integrating remote sensing and statistical data for unified visual query.

Proposed a quantitative remote sensing method to assess disaster intensity, impact, and trends, enabling cross-domain knowledge conversion.

What are the implications of the main findings?

The “data silo” gap between remote sensing monitoring and sector-specific disaster management is bridged.

A rule-based decision-support tool for scientific and intelligent disaster early warning is provided.

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.