Bo Yu

1. Land cover classification is vital for applications like environmental monitoring (Gong, Wang, and Huang 2024; Li et al. 2023) and urban planning (Mansour, Al-Belushi, and Al-Awadhi 2020; Yu et ...

1 Landslides are a major natural hazard that cause widespread damage to human settlements, infrastructure, and ecosystems, particularly in mountainous and tectonically active regions (Froude and Pe...

Landslides endanger human safety and damage infrastructure, underscoring the importance of accurate extraction. However, landslide extraction is often hindered by the omission of sparsely distributed landslides and the difficulty of delineating their blurred boundaries. Large-scale landslide extraction faces two key challenges. The first is a severe sample imbalance between landslides and background objects, which biases the model toward background and omits landslides. The second is the confusion between landslides and background features, which leads to inaccurate boundary delineation and fragmented extraction results. To address these issues, this paper proposes a two-phase landslide extraction framework. First, we propose a PCA-based landslide candidate extraction module to remove salient background objects and reduce data imbalance. Second, we propose a Spike-inspired Landslide Extraction Model to further discriminate actual landslides from the candidates by incorporating a spike-inspired sparse attention module (SISA). It can enhance weak landslide features such as blurred boundaries while mitigating background noise through its adaptive spatial suppression mechanism. To integrate spatial details across scales, a mix-scale feature aggregation module (MSFA) is proposed, which aggregates hierarchical features to extract landslides of various scales. Experiments on the landslide datasets from the Hengduan Mountains and Hokkaido, Japan, show IoU improvements of 4.26% and 1.22% compared to the recently proposed methods, validating its effectiveness under both imbalanced and dense landslide conditions.

Landslides, a frequent and devastating natural disaster, pose significant risks to human populations and the environment, often leading to substantial loss of life, property damage, and ecological disruption. Creating a comprehensive landslide inventory is essential for disaster response planning and understanding landslide mechanisms. Current methods for landslide extraction, often designed for specific events and primarily focused on vegetative backgrounds, face challenges in practical applications. Landslide extraction from large-scale areas encounters two primary challenges: data imbalance between landslides and background objects, and the confusing features of small-scale landslides with complex background objects. This paper introduces a two-phase framework for extracting multi-scale landslides across large areas with intricate backgrounds. Initially, a dual-temporal image-based method is employed to identify candidate landslides, effectively reducing background interference and addressing data imbalance. Subsequently, a Swin Transformer-based Multi-scale Attention Model (Swin-MA) is proposed to capture and learn multi-scale landslide features comprehensively. We conducted our study in two regions: the Hengduan Mountains in China, a hotspot for frequent landslides, and Hokkaido, Japan, where significant landslides occurred following an earthquake on September 6, 2018. Our approach outperforms seven recently proposed methods, demonstrating at least a 5.36% improvement in Intersection over Union (IoU) and affirming its effectiveness and significance in large-scale landslide extraction.

Methane (CH₄) is a significant greenhouse gas in exacerbating climate change. Approximately 25 % of CH₄ is emitted from storage tanks. It is crucial to spatially explore the CH₄ emission patterns from storage tanks for efficient strategy proposals to mitigate climate change. However, due to the lack of publicly accessible storage tank locations and distributions, it is difficult to ascertain the CH₄ emission spatial pattern over a large-scale area. To address this problem, we generated a storage tank dataset (STD) by implementing a deep learning model with manual refinement based on 4403 high-spatial-resolution images (1–2 m) from the Gaofen-1, Gaofen-2, Gaofen-6, and Ziyuan-3 satellites over city regions in China with officially reported numerous storage tanks in 2021. STD is the first storage tank dataset for over 92 typical city regions in China. The dataset can be accessed at https://doi.org/10.5281/zenodo.10514151 (Chen et al., 2024). It provides a detailed georeferenced inventory of 14 461 storage tanks wherein each storage tank is validated and assigned the construction year (2000–2021) by visual interpretation of the collected high-spatial-resolution images, historical high-spatial-resolution images of Google Earth, and field survey. The inventory comprises storage tanks with various distribution patterns in different city regions. Spatial consistency analysis with the CH₄ emission product shows good agreement with storage tank distributions. The intensive construction of storage tanks significantly induces CH₄ emissions from 2005 to 2020, underscoring the need for more robust measures to curb CH₄ release and aid in climate change mitigation efforts. Our proposed dataset, STD, will foster the accurate estimation of CH₄ released from storage tanks for CH₄ control and reduction and ensure more efficient treatment strategies are proposed to better understand the impact of storage tanks on the environment, ecology, and human settlements.

Intermittent rivers and ephemeral streams are crucial for the water cycle and ecosystem services, yet they are often neglected by managers and researchers, especially in headwater areas. This oversight has caused a lack of comprehensive basemaps for these vital river systems. In headwater regions, water bodies are typically sparse and disconnected, with narrow and less distinct channels. Therefore, we propose a novel hybrid method that integrates topographic data and remote sensing imagery to delineate river networks. Our method reestablishes connectivity among sparsely distributed water bodies through topographic pairs, enhances less distinct channel features using the gamma function, and converts topographic and water indices data into a weighted graph to determine optimal channels with the A* algorithm. The topographic and water indices data are derived from the Multi-Error-Removed Improved-Terrain DEM (MERIT DEM) and an average composite of the Modified Normalized Difference Water Index (MNDWI), respectively. In the upper Lancang-Mekong River basin, our method outperformed five publicly available DEM datasets, achieving over 91% positional accuracy within a 30 m buffer. This hybrid method enhances positional accuracy and effectively connects sparse water bodies in headwater areas, offering promising applications for delineating intermittent rivers and ephemeral streams and providing baseline information for these river systems.

Methane (CH₄) is a significant greenhouse gas in exacerbating climate change. Approximately 25 % of CH₄ is emitted from storage tanks. It is crucial to spatially explore the CH₄ emission patterns from storage tanks for efficient strategy proposals to mitigate climate change. However, due to the lack of publicly accessible storage tank locations and distributions, it is difficult to ascertain the CH₄ emission spatial pattern over a large-scale area. To address this problem, we generated a storage tank dataset (STD) by implementing a deep learning model with manual refinement based on high spatial resolution images (1–2m) from the GaoFen-1, GaoFen-2, GaoFen-6, and Ziyuan-3 satellites over cities in China with officially reported numerous storage tanks in 2021. STD is the first storage tank dataset over 92 typical cities in China. The dataset can be accessed at https://zenodo.org/records/10514151 (Chen et al., 2024). It provides a detailed georeferenced inventory of 14,461 storage tanks, wherein each storage tank is validated and assigned the construction year (2000–2021) by visual interpretation referring to the collected high spatial resolution images, historical high spatial resolution images of Google Earth, and field survey. The inventory comprises storage tanks having various distribution patterns in different cities. Spatial consistency analysis with CH₄ emission product shows good agreement with storage tank distributions. The intensive construction of storage tanks significantly induces CH₄ emissions from 2005 to 2020, underscoring the need for more robust measures to curb CH₄ release and aid in climate change mitigation efforts. Our proposed dataset STD will foster the accurate estimation of CH₄ released from storage tanks for CH₄ control and reduction and ensure more efficient treatment strategies are proposed to better understand the impact of storage tanks on the environment, ecology, and human settlements.