Parcel-Level Flood and Drought Detection for Insurance Using Sentinel-2A, Sentinel-1 SAR GRD and Mobile Images

Thapa, Aakash; Horanont, Teerayut; Neupane, Bipul

doi:10.3390/rs14236095

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…The above two reasons led to not only noticeable visual differences between the PS and S2 images used in this study, but also have quantitative influence. The NDWI index was proved useful in water extent extraction by many previous studies (Cavallo et al, 2021;Goffi et al, 2020;Thapa et al, 2022). The threshold is theoretically set around 0, with values greater than 0 being flooded areas.…”

Section: Resultsmentioning

confidence: 99%

Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation

Li¹,

Demir²

2024

Preprint

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Remote sensing (RS) imagery has become more and more popular in surface water extent extraction applications with the help of increasing availability of RS data and advancements in image processing algorithms, software, and hardware. Many studies have demonstrated that RS imagery has the potential to work independently or along with other well-documented approaches in identifying flood extent. However, due to the insufficiency of images from single-sourced RS and independent references for validation, most existing studies either focused on mapping a single scene or failed to support their results with adequate non-RS validation when multi-temporal/multi-spatiotemporal images were involved. Because of that, hydrosimulations still dominate flood series mapping despite requiring huge data and computational resources. To close these gaps, this study investigated the efficacy of RS-based multi-spatiotemporal flood inundation mapping using multimodal RS imageries to take advantage of improved data availability and complementary image properties. This study also proposed a Quantile-based Filling & Refining (QFR) workflow to resolve the blocking effects of dense vegetation that occurs in study areas. We tested the workflow in four lock and dam sites on the Mississippi River, downstream to the Quad City area, by comparing the RS-based flood maps with HEC-RAS simulations. Compared to the original flood extent that only went through basic post-processing, QFR maps were noticeably more consistent with HEC-RAS maps. Results also showed that all steps in QFR contributed to performance improvements. Despite all being necessary in our case, some should be adjusted in different study regions, such as the levee step. Our findings showcased the efficacy of the multimodal RS flood mapping with QFR post-processing. Due to its simple structure, the proposed workflow has potential to be fully automated and can benefit near-real-time and real-time applications.

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Section: Resultsmentioning

confidence: 99%

Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation

Li¹,

Demir²

2024

Preprint

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“…For scene classification, the available datasets are grouped into diverse scenes. The DL architectures are either trained on these datasets to obtain the predicted scene class [39], or pretrained DL models are used to obtain derived classes from the same scene classes [40,41], depending upon the application. In the context of remote sensing scene classification, the experiments are mainly focused on achieving optimal scene prediction by implementing DL architectures.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis

Thapa,

Horanont,

Neupane

et al. 2023

Remote Sensing

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Remote sensing image scene classification with deep learning (DL) is a rapidly growing field that has gained significant attention in the past few years. While previous review papers in this domain have been confined to 2020, an up-to-date review to show the progression of research extending into the present phase is lacking. In this review, we explore the recent articles, providing a thorough classification of approaches into three main categories: Convolutional Neural Network (CNN)-based, Vision Transformer (ViT)-based, and Generative Adversarial Network (GAN)-based architectures. Notably, within the CNN-based category, we further refine the classification based on specific methodologies and techniques employed. In addition, a novel and rigorous meta-analysis is performed to synthesize and analyze the findings from 50 peer-reviewed journal articles to provide valuable insights in this domain, surpassing the scope of existing review articles. Our meta-analysis shows that the most adopted remote sensing scene datasets are AID (41 articles) and NWPU-RESISC45 (40). A notable paradigm shift is seen towards the use of transformer-based models (6) starting from 2021. Furthermore, we critically discuss the findings from the review and meta-analysis, identifying challenges and future opportunities for improvement in this domain. Our up-to-date study serves as an invaluable resource for researchers seeking to contribute to this growing area of research.

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Separate prediction of soil organic matter in drylands and paddy fields based on optimal image synthesis method in the Sanjiang Plain, Northeast China

Ma,

Wang,

Liu

et al. 2024

Geoderma

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Parcel-Level Flood and Drought Detection for Insurance Using Sentinel-2A, Sentinel-1 SAR GRD and Mobile Images

Cited by 6 publications

References 37 publications

Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation

Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation

Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis

Separate prediction of soil organic matter in drylands and paddy fields based on optimal image synthesis method in the Sanjiang Plain, Northeast China

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