Earth Surface Anomaly Detection Using Graph Neural Network-based Representation and Reasoning of Remote Sensing Geographic Object Relationships

LIU Siqi; GAO Zhi; CHEN Boan; LU Yao; ZHU Jun; LI Yanzhang; WANG Qiao

doi:10.11999/JEIT240883

Volume 47 Issue 6

Jun. 2025

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LIU Siqi, GAO Zhi, CHEN Boan, LU Yao, ZHU Jun, LI Yanzhang, WANG Qiao. Earth Surface Anomaly Detection Using Graph Neural Network-based Representation and Reasoning of Remote Sensing Geographic Object Relationships[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1690-1703. doi: 10.11999/JEIT240883

Citation:

LIU Siqi, GAO Zhi, CHEN Boan, LU Yao, ZHU Jun, LI Yanzhang, WANG Qiao. Earth Surface Anomaly Detection Using Graph Neural Network-based Representation and Reasoning of Remote Sensing Geographic Object Relationships[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1690-1703. doi: 10.11999/JEIT240883

Citation:

LIU Siqi, GAO Zhi, CHEN Boan, LU Yao, ZHU Jun, LI Yanzhang, WANG Qiao. Earth Surface Anomaly Detection Using Graph Neural Network-based Representation and Reasoning of Remote Sensing Geographic Object Relationships[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1690-1703. doi: 10.11999/JEIT240883

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Earth Surface Anomaly Detection Using Graph Neural Network-based Representation and Reasoning of Remote Sensing Geographic Object Relationships

doi: 10.11999/JEIT240883 cstr: 32379.14.JEIT240883

LIU Siqi¹,
GAO Zhi^{1
,
,},
CHEN Boan¹,
LU Yao²,
ZHU Jun³,
LI Yanzhang³,
WANG Qiao⁴

1.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
2.
Beijing Institute of Remote Sensing Information, Beijing 100000, China
3.
DFH Satellite Co., Ltd., Beijing 100094, China
4.
Beijing Normal University, Beijing 100091, China

Funds: Civil Aerospace Project (D010206)

Received Date: 2024-10-21
Rev Recd Date: 2025-05-08

Available Online: 2025-05-28

Publish Date: 2025-06-30

Abstract

Abstract

Objective The increasing frequency and severity of surface anomalies induced by natural processes and human activities has raised the demand for real-time, intelligent remote sensing systems for disaster monitoring and emergency response. Existing approaches to extracting geographic object relationships in remote sensing images primarily rely on object detection models. These approaches often lack sufficient localization precision and fail to capture topological dependencies between objects. Moreover, the absence of standardized, high-quality datasets restricts progress in model development. To address these limitations, this study proposes a framework that integrates graph-based representation with a Graph Neural Network (GNN) architecture to reason over geographic object relationships. The main objectives are to: (1) construct a semantically annotated dataset of geographic object relationships in remote sensing imagery; (2) develop a GNN-based model to improve relationship prediction accuracy; and (3) evaluate the model’s effectiveness in detecting and interpreting surface anomalies by analyzing pre- and post-disaster relationship patterns across a range of scenarios. Methods The methodology comprises three primary components: dataset construction, model development, and performance evaluation. To address the scarcity of labeled data, a semantic relationship dataset is constructed. Thirty high-resolution remote sensing images from the OpenEarthMap dataset are manually annotated using EISeg software, resulting in 17 object categories (Table 1) and five semantic relationships—contain, connect, on, along, and beside—defined through analysis of topological interactions (Table 2). Instance-level annotations are generated using connected component labeling, and relationship labels are assigned based on both topological configuration and object category. The resulting dataset includes 7,063 annotated entities and 13,273 relationship triplets. A GNN-based model is developed to predict semantic relationships, incorporating subgraph sampling and hyperparameter optimization. The model employs the Personalized PageRank (PPR) algorithm to extract query-relevant subgraphs, thereby reducing computational complexity while preserving essential topological structure. Message passing mechanisms from RED-GNN are used to propagate node features, and Bayesian optimization is applied to tune hyperparameters. Model performance is assessed using standard metrics: Mean Reciprocal Rank (MRR), HITS@1, and HITS@10. Results and Discussions Extensive experiments demonstrate the high performance of the proposed framework. On the constructed dataset, the model achieves an MRR of 0.9879 on the test set, with HITS@1 and HITS@10 scores of 97.03% and 99.96%, respectively, outperforming baseline methods such as RED-GNN and Grail (Table 5). Ablation studies confirm the effectiveness of the PPR sampling strategy, which outperforms random walk, breadth-first search, and standard PageRank in terms of both accuracy and efficiency (Table 6). Model generalizability is further assessed using pre- and post-disaster images from the xBD dataset. In hurricane-affected regions (Fig. 6, Fig. 7), abnormal relationships—such as “sea lake pond contain residential area”—emerge, reflecting the submergence of buildings and roads due to flooding. Frequency histograms (Fig. 8, Fig. 9) indicate a post-disaster decrease in relationship diversity and a shift toward water-related spatial associations. In wildfire scenarios (Fig. 10–Fig. 13), relationships such as "bareland contain rangeland" replace "tree beside rangeland," suggesting vegetation loss and soil exposure. These findings demonstrate the model’s capacity to detect spatial and semantic shifts in geographic object relationships caused by disasters. Coarse anomaly localization is achieved through centroid-based node mapping, enabling interpretation of surface anomaly dynamics over time. Conclusions This study contributes to remote sensing-based surface anomaly detection through three main innovations. First, a high-quality semantic relationship dataset is constructed with pixel-level annotations and standardized relationship definitions, addressing the lack of labeled data in this area. The dataset includes 17 object categories and five topologically defined relationship types, offering a valuable benchmark for future research. Second, a novel GNN-based model is developed that advances relationship prediction by integrating PPR-based subgraph sampling with optimized message passing mechanisms. Third, the framework is extensively validated using real-world disaster scenarios, demonstrating its practical utility in detecting and interpreting surface anomalies through changes in object relationships. The model’s ability to produce interpretable relationship graphs while maintaining computational efficiency supports its application in time-sensitive emergency response contexts. Future work will focus on expanding image diversity, refining relationship definitions, and incorporating real-world noise to improve robustness.
- Graph Neural Network (GNN),
- Remote sensing image interpretation,
- Semantic relationship,
- Relation prediction,
- Topological relationship

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References(21)

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