pydynpd: A Python package for dynamic panel
model

Wu, Dazhong; Ma, Jianhua; Xing, Feng

doi:10.21105/joss.04416

Cited by 3 publications

(2 citation statements)

References 9 publications

(9 reference statements)

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“…As such, it was capable of segmenting a broad range of land covers, although not without its mistakes. The recent literature, however, suggest that models based on ViT can be capable of performing zero-shot segmentation on different domains, or at least be adapted with few-shot learning [25][26][27][28].…”

Section: Discussionmentioning

confidence: 99%

The Potential of Visual ChatGPT for Remote Sensing

et al. 2023

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Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. These are known as Visual LLMs and one notable model is Visual ChatGPT, which combines ChatGPT’s LLM capabilities with visual computation to enable effective image analysis. These models’ abilities to process images based on textual inputs can revolutionize diverse fields, and while their application in the remote sensing domain remains unexplored, it is important to acknowledge that novel implementations are to be expected. Thus, this is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model’s limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field.

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Section: Discussionmentioning

confidence: 99%

The Potential of Visual ChatGPT for Remote Sensing

et al. 2023

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“…First, post-flood disaster datasets were obtained from Maxar satellite imagery, capturing building footprints through automated segmentation datasets [37]. A comparison of post-event footprints with pre-event 'Google Buildings' footprints was conducted using Arc-GIS Pro spatial analysis tools to identify impacts on building structures in the floodplain.…”

Section: S4 Disaster Impact Analysismentioning

confidence: 99%

Rising Waters, Falling Dams: Deciphering the Derna Flood Disaster

Imran,

Nemnem,

Tanim

et al. 2024

Preprint

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On September 11, 2023, the city of Derna along the Libyan coast experienced an unprecedented disaster as Storm Daniel unleashed heavy rainfall, leading to the failure of dams and catastrophic flooding. The study investigates the dynamics of this event, exploring the factors leading to dam failure and their role in the devastating flooding of Derna. Through hydrologic, hydraulic, and geotechnical modeling, the research investigates scenarios with and without dams to understand the specific impact of dam failure on flood inundation. The storm's origin, the rainfall distribution in the Wadi Derna watershed, and the subsequent runoff and reservoir filling are analyzed. The study reveals that the dams, if survived would have been minimally effective in controlling the flash flood in the city due to the extreme nature of Storm Daniel's runoff. However, the surge wave generated due to their failure had a devastating impact on human lives and infrastructures. The findings emphasize the critical need for thorough hydro-meteorological assessments and flood management practices in regions vulnerable to climate change impacts and recommend a shift from over-reliance on structural flood control measures. The analysis also highlights the 'dam paradox' and the importance of effective communication, evacuation plans, and comprehensive flood control evaluations in mitigating the impact of extreme storms.

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The impact of regulation on risk and return

Koziol

Kuhn

2023

Economics Letters

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pydynpd: A Python package for dynamic panel model

Cited by 3 publications

References 9 publications

The Potential of Visual ChatGPT for Remote Sensing

The Potential of Visual ChatGPT for Remote Sensing

Rising Waters, Falling Dams: Deciphering the Derna Flood Disaster

The impact of regulation on risk and return

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