Compressive Sensing Based Reconstruction and Pixel-Level Classification of Very High-Resolution Disaster Satellite Imagery Using Deep Learning

Shinde, Rajat C.; Potnis, Abhishek V.; Durbha, Surya S.; Andugula, Prakash

doi:10.1109/igarss.2019.8899871

Cited by 6 publications

(1 citation statement)

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“…With the robust development of machine learning, researchers have been able to explore different landslide classification or zoning methods using algorithms such as logistic regression, support vector machines, decision trees, and random forests (Mohan et al, 2021). In recent years, numerous studies have utilized high-resolution imagery and deep learning models based on Convolutional Neural Networks (CNNs) to extract landslides, achieving promising results (Ghorbanzadeh et al, 2019;Sameen and Pradhan, 2019;Shinde et al, 2019;Morales et al, 2022). Although the aforementioned optical remote sensing image analyses for landslide extraction are typically limited to smaller spatial scales, they have provided valuable insights into the distribution and characteristics of landslides.…”

Section: Introductionmentioning

confidence: 99%

A research on the relationship between landslide area changes and environmental factors in the southern Tibetan plateau

Xu,

Wang,

Yang

et al. 2024

Front. Earth Sci.

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Introduction: Landslides are known to be one of the most frequent types of geological disasters. However, there is not an established method for large-scale, rapid, and high-precision landslide extraction. The quantitative impact of environmental changes on landslide development is also not well understood, which hinders accurate assessments and decision-making in environmental and disaster response. The polar regions, including the Antarctic, the Arctic, and the Tibetan plateau (TP), sensitive to global environmental changes, are significantly affected by global warming. This leads to extensive landslide development, particularly in the southern TP. This research focuses on new landslides in the southern TP, exploring extraction methods and the relationship between landslides and environmental factors.Methods: Utilizing the Google Earth Engine (GEE) and an improved Otsu threshold segmentation algorithm, we processed remote sensing images with 10 m resolution to identify landslide areas. The proposed Normalized Landslide Bare-soil Separation Index (NDLBSI) achieved an 87% pre-extraction accuracy in extracting landslides from Sentinel-2 images from 2019 to 2023. For the pre-extraction results, manual interpretation and correction were carried out, and a model correlating annual landslide changes with environmental factors was established based on least squares multivariate statistical methods.Results: Results show that a significant increase in landslide areas in the southern TP over the past 5 years, correlating with the watershed-wide increase in annual average temperature and vegetation cover, along with a decrease in snow cover area.Discussion: These changes could affect soil and rock moisture, influencing soil stability and landslide occurrence. The study provides valuable insights for large-scale landslide detection and understanding the environmental factors influencing landslides, which is of some significance for landslide hazards early warning.

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