☆ 4.7 Article

Detection of Flood Damage in Urban Residential Areas Using Object-Oriented UAV Image Analysis Coupled with Tree-Based Classifiers

REMOTE SENSING (2021)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Interdisciplinary Applications

Detection and Semantic Segmentation of Disaster Damage in UAV Footage

Yalong Pi et al.

Summary: Following large-scale disasters like hurricanes, floods, or earthquakes, preliminary damage assessment is crucial to determine the impact of the damage and meet the needs of affected individuals and communities. Traditionally, site evaluation and consensus-based assessment techniques are used, but unmanned aerial vehicles (UAVs) have become increasingly popular due to their cost-effectiveness and ease of deployment. This paper introduces a fully annotated dataset and convolutional neural network (CNN) models to detect and segment critical objects in aerial footage of disaster sites, achieving high accuracy and precision levels for countable and bulk objects.

JOURNAL OF COMPUTING IN CIVIL ENGINEERING (2021)

添加到收藏夹

Article Environmental Sciences

Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

Shobitha Shetty et al.

Summary: Machine learning classifiers, particularly Random Forest, were evaluated for their performance in Land Use and Land Cover (LULC) mapping using multi-temporal satellite remote sensing data. Different sampling methods were found to have varying impacts on the classification results, with Stratified Proportional Random Sampling (SRS(Prop)) favoring major classes, Stratified Equal Random Sampling (SRS(Eq)) providing good accuracies for minority classes, and Stratified Systematic Sampling (SSS) performing well for areas with large intra-class variability. Random Forest outperformed other machine learning classifiers with a confidence level of over 95%, while Support Vector Machine and Classification and Regression Trees showed similar performance. Relevance Vector Machine achieved good results with limited training samples.

REMOTE SENSING (2021)

添加到收藏夹

Article Computer Science, Information Systems

The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen's Kappa and Brier Score in Binary Classification Assessment

Davide Chicco et al.

Summary: Measuring the outcome of binary classifications is crucial in machine learning and statistics, but there is no consensus on which statistical rate to use. The Matthews correlation coefficient (MCC) has advantages over other scores in terms of reliability on imbalanced datasets. Comparing MCC with Cohen's Kappa and Brier score, MCC provides more truthful and informative results in certain use cases. It is therefore recommended to use MCC for evaluating binary classifications.

IEEE ACCESS (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Flood Extent Mapping: An Integrated Method Using Deep Learning and Region Growing Using UAV Optical Data

Leila Hashemi-Beni et al.

Summary: The article introduces an integrated CNN and region growing method for mapping both visible and underneath vegetation flooded areas. Data augmentation is used to enhance the accuracy of image classification, and the proposed integrated method efficiently detects floods in both visible and vegetation-covered areas to support effective flood emergency response and recovery activities.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

添加到收藏夹

Article Engineering, Civil

Classifying earthquake damage to buildings using machine learning

Sujith Mangalathu et al.

EARTHQUAKE SPECTRA (2020)

添加到收藏夹

Article Environmental Sciences

Can we still predict the future from the past? Implementing non-stationary flood frequency analysis in the UK

Duncan Faulkner et al.

JOURNAL OF FLOOD RISK MANAGEMENT (2020)

添加到收藏夹

Article Environmental Sciences

Guidelines for the Use of Unmanned Aerial Systems in Flood Emergency Response

Gloria Salmoral et al.

WATER (2020)

添加到收藏夹

Article Geosciences, Multidisciplinary

Rapid urban flood damage assessment using high resolution remote sensing data and an object-based approach

Sergio Ivan Jimenez-Jimenez et al.

GEOMATICS NATURAL HAZARDS & RISK (2020)

添加到收藏夹

Article Environmental Sciences

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

Shahab Eddin Jozdani et al.

REMOTE SENSING (2019)

添加到收藏夹

Article Environmental Sciences

Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions

Francesco Nex et al.

REMOTE SENSING (2019)

添加到收藏夹

Review Engineering, Civil

Emerging artificial intelligence methods in structural engineering

Hadi Salehi et al.

ENGINEERING STRUCTURES (2018)

添加到收藏夹

Article Remote Sensing

Synergy of sampling techniques and ensemble classifiers for classification of urban environments using full-waveform LiDAR data

Mohsen Azadbakht et al.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2018)

添加到收藏夹

Article Remote Sensing

Use of active learning for earthquake damage mapping from UAV photogrammetric point clouds

Zhihua Xu et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2018)

添加到收藏夹

Review Remote Sensing

Implementation of machine-learning classification in remote sensing: an applied review

Aaron E. Maxwell et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2018)

添加到收藏夹

Review Geography, Physical

A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches

Su Ye et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2018)

添加到收藏夹

Article Environmental Sciences

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

Moslem Ouled Sghaier et al.

REMOTE SENSING (2018)

添加到收藏夹

Article Environmental Sciences

The Use of Unmanned Aerial Vehicles to Estimate Direct Tangible Losses to Residential Properties from Flood Events: A Case Study of Cockermouth Following the Desmond Storm

Monica Rivas Casado et al.

REMOTE SENSING (2018)

添加到收藏夹

Article Chemistry, Analytical

Challenges and Opportunities for UAV-Based Digital Elevation Model Generation for Flood-Risk Management: A Case of Princeville, North Carolina

Leila Hashemi-Beni et al.

SENSORS (2018)

添加到收藏夹

Review Geography, Physical

A review of supervised object-based land-cover image classification

Lei Ma et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2017)

添加到收藏夹

Article Chemistry, Analytical

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

Dan Popescu et al.

SENSORS (2017)

添加到收藏夹

Review Computer Science, Artificial Intelligence

Learning from imbalanced data: open challenges and future directions

Bartosz Krawczyk

PROGRESS IN ARTIFICIAL INTELLIGENCE (2016)

添加到收藏夹

Article Environmental Sciences

Classification of Ultra-High Resolution Orthophotos Combined with DSM Using a Dual Morphological Top Hat Profile

Qian Zhang et al.

REMOTE SENSING (2015)

添加到收藏夹

Article Environmental Sciences

UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis

Quanlong Feng et al.

REMOTE SENSING (2015)

添加到收藏夹

Article Environmental Sciences

Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier-A Case of Yuyao, China

Quanlong Feng et al.

WATER (2015)

添加到收藏夹

Article Water Resources

Flood risk and climate change: global and regional perspectives

Zbigniew W. Kundzewicz et al.

HYDROLOGICAL SCIENCES JOURNAL (2014)

添加到收藏夹

Review Computer Science, Artificial Intelligence

Classification and regression trees

Wei-Yin Loh

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2011)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.