Remote Sensing
This dataset provides a comprehensive list of articles used for the review and statistical analysis presented in the article titled 'Applications and Advancements of Spaceborne InSAR in Landslide Monitoring and Susceptibility Mapping: A Systematic Review.' The selection of articles was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) workflow.
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The analysis of complex scattering patterns in SAR images of high-voltage power lines is important to comprehend the multi-reflection effects of these cylindrical shape structures. Such insights are invaluable for applications related to inspection management, particularly in safety analysis and ongoing monitoring. This paper introduces a novel point scattering tracing method to dissect the intricate multi-reflection effects of power lines in high-resolution X-band SAR data.
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This dataset comprises radar-acquired signals from 15 subjects walking on a treadmill, aimed at exploring methodologies for non-contact vital sign detection under conditions of significant body movement. Each subject participated in four experimental sessions, where radar data were collected using two Continuous Wave (CW) radars positioned to capture signals from the front and back of the subject. The data includes both raw and demodulated signals synchronized with ground-truth data obtained from a BioPac system.
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This dataset (samplePointsCities_20240811_harmonized.csv) was used for the Rescaled Water Index (RWI) proposal. The GitHub page (https://github.com/edujusti/Rescaled-Water-Index-RWI) contains the Python and JavaScript (Google Earth Engine) scripts used for data production, statistical analyses, and result visualization of the RWI spectral index.
This spectral index is a modification of MNDWI to enhance the mappings of water surfaces.
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In recent years, the success of large-scale visionlanguage models (VLMs) such as CLIP has led to their increased usage in various computer vision tasks. These models enable zero-shot inference through carefully crafted instructional text prompts without task-specific supervision. However, the potential of VLMs for generalization tasks in remote sensing (RS) has not been fully realized. To address this research gap, we propose a novel image-conditioned prompt learning strategy called the Visual Attention Parameterized Prompts Learning Network (APPLeNet).
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The detection of the collapse of landslides trigerred by intense natural hazards, such as earthquakes and rainfall, allows rapid response to hazards which turned into disasters. The use of remote sensing imagery is mostly considered to cover wide areas and assess even more rapidly the threats. Yet, since optical images are sensitive to cloud coverage, their use is limited in case of emergency response. The proposed dataset is thus multimodal and targets the early detection of landslides following the disastrous earthquake which occurred in Haiti in 2021.
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Forest wildfires are one of the most catastrophic natural disasters, which poses a severe threat to both the ecosystem and human life. Therefore, it is imperative to implement technology to prevent and control forest wildfires. The combination of unmanned aerial vehicles (UAVs) and object detection algorithms provides a quick and accurate method to monitor large-scale forest areas.
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