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Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

Bo Peng, Zonglin Meng, Qunying Huang, Caixia Wang
2019 Remote Sensing  
To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after  ...  Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs11212492 fatcat:utrgmeleofgobcukx7ip3mkpby

The need for training and benchmark datasets for convolutional neural networks in flood applications

Abdou Khouakhi, Joanna Zawadzka, Ian Truckell
2022 Hydrology Research  
Flood-related image datasets from social media, smartphones, CCTV cameras, and unmanned aerial vehicles (UAVs) present valuable data for the management of flood risk, and particularly for the application  ...  We note the lack of open and labelled flood image datasets and the growing need for an open, benchmark data library for image classification, object detection, and segmentation relevant to flood management  ...  Multispectral imagery is also used by Peng et al. (2019) to develop a CNNbased data fusion framework for mapping urban flood extent with pre-and post-flooding surface reflectance imagery.  ... 
doi:10.2166/nh.2022.093 fatcat:h3ct5oiyvba6hikumst6toh6gi

Flood Detection Using Multiple Chinese Satellite Datasets during 2020 China Summer Floods

Lianchong Zhang, Junshi Xia
2021 Remote Sensing  
Firstly, the coarse results of the water body were generated by the binary segmentation of GF-3 SAR, the water indexes of GF-1/6 multispectral, and Zhuhai-1 hyperspectral images.  ...  Secondly, the fine results were achieved by the deep neural networks with noisy-label learning. More specifically, the Unet with the T-revision is adopted as the noisy label learning method.  ...  Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery.  ... 
doi:10.3390/rs14010051 fatcat:2jzlgxdpo5cj7b3cw54rkypb2e

Overview of the Special Issue on Applications of Remote Sensing Imagery for Urban Areas

Xinghua Li, Yongtao Yu, Xiaobin Guan, Ruitao Feng
2022 Remote Sensing  
Urban areas are the center of human settlement with intensive anthropic activities and dense built-up infrastructures, suffering significant evolution in population shift, land-use change, industrial production  ...  Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2022, 14, 1204  ...  Almost all of these researches use the satellite RS imageries from optical, thermal, or SAR sensors.  ... 
doi:10.3390/rs14051204 fatcat:ugdfb7g3lzb3bdqo4ybrhsvvbi

Multi^3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery [article]

Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopackova, Piotr Bilinski
2018 arXiv   pre-print
We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network.  ...  We also demonstrate that our model produces highly accurate segmentation maps of flooded buildings using only publicly available medium-resolution data instead of significantly more detailed but sparsely  ...  The authors gratefully acknowledge support from the European Space Agency, NVIDIA Corporation, Satellite Applications Catapult, and Kellogg College, University of Oxford.  ... 
arXiv:1812.01756v1 fatcat:da44sgz3tbhkfjp7zuma6jf6bm

Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery

Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopačková, Piotr Biliński
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network.  ...  We also demonstrate that our model produces highly accurate segmentation maps of flooded buildings using only publicly available medium-resolution data instead of significantly more detailed but sparsely  ...  The authors gratefully acknowledge support from the European Space Agency, NVIDIA Corporation, Satellite Applications Catapult, and Kellogg College, University of Oxford.  ... 
doi:10.1609/aaai.v33i01.3301702 fatcat:ab4xym57arep5c6s6eh5wyx6ey

FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding

Maryam Rahnemoonfar, Tashnim Chowdhury, Argho Sarkar, Debvrat Varshney, Masoud Yari, Robin Murphy
2021 IEEE Access  
On the other hand the primary source of the ground-level imageries is social media [30] , these imageries lack geo-location tags [27] and suffers from data scarcity for deep learning training [11]  ...  His research interest includes Deep Learning, Machine Learning, Semantic Segmentation, Few Shot Learning, Meta Learning, and Bayesian Learning.  ... 
doi:10.1109/access.2021.3090981 fatcat:wacd4jsapzfuhhu5kjvkl3ab2m

AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURES

B. Ghosh, S. Garg, M. Motagh
2022 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Recent advancements in deep learning algorithms for image segmentation has demonstrated excellent potential for improving flood detection.  ...  In this paper, we present two deep learning approaches, first using a UNet and second, using a Feature Pyramid Network (FPN), both based on a backbone of EfficientNet-B7, by leveraging publicly available  ...  of leveraging deep learning algorithm for disaster mapping from satellite imagery.  ... 
doi:10.5194/isprs-annals-v-3-2022-201-2022 fatcat:ywzv2zubljeyhpdaoqp6axui3q

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer
2020 Remote Sensing  
In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs.  ...  To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN).  ...  Conflicts of Interest: The authors declare no conflict of interest. Appendix A.  ... 
doi:10.3390/rs12183053 doaj:56aade2b0b4243b1b45a6839cc85dc15 fatcat:jskmiupd4zaa5egsllib2oyioi

TorchGeo: deep learning with geospatial data [article]

Adam J. Stewart, Caleb Robinson, Isaac A. Corley, Anthony Ortiz, Juan M. Lavista Ferres, Arindam Banerjee
2021 arXiv   pre-print
TorchGeo is also the first library to provide pre-trained models for multispectral satellite imagery (e.g. models that use all bands from the Sentinel 2 satellites), allowing for advances in transfer learning  ...  To help realize the potential of deep learning for remote sensing applications, we introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem.  ...  Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.  ... 
arXiv:2111.08872v2 fatcat:qelmi4igvzehjjy6m3chkxfkca

WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

Mugiraneza, Nascetti, Ban
2019 Remote Sensing  
A multi-stage object-based classification was performed using support vector machines (SVM) and a rule-based approach to derive 12 land cover classes with the input of WorldView-2 spectral bands, spectral  ...  This study aims to evaluate the potential of WorldView-2 high-resolution multispectral and panchromatic imagery for detailed urban land cover classification in Kigali, Rwanda, a complex urban area characterized  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs11182128 fatcat:rtfvzo65ungptle3qe7e32iosu

Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

Yanbing Bai, Wenqi Wu, Zhengxin Yang, Jinze Yu, Bo Zhao, Xing Liu, Hanfang Yang, Erick Mas, Shunichi Koshimura
2021 Remote Sensing  
Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied  ...  Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly  ...  The author gratefully acknowledges the support of K.C. Wong Education Foundation, Hong Kong. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13112220 fatcat:4mrb5eyitvgslir743tynhixcy

Semantics-Driven Remote Sensing Scene Understanding Framework for Grounded Spatio-Contextual Scene Descriptions

Abhishek V. Potnis, Surya S. Durbha, Rajat C. Shinde
2021 ISPRS International Journal of Geo-Information  
Considering the significance of scene understanding for informed decision-making from remote sensing scenes during a flood, we selected it as a test scenario, to demonstrate the utility of this framework  ...  The RSS-KGs are enriched both spatially and contextually, using a deductive reasoner, by mining for implicit spatio-contextual relationships between land-cover classes in the scenes.  ...  Acknowledgments: The authors are grateful to the Google Cloud Research Credits Program for providing the credits that enabled the use of GCP for training and validating the deep-learning models.  ... 
doi:10.3390/ijgi10010032 fatcat:warifoopejaqfglljmkbatjrke

2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12

2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Sun, L., +, JSTARS June 2019 1905-1919 Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification  ...  ., +, JSTARS Jan. 2019 210-222 A Hybrid Markov Random Field Model With Multi-Granularity Information for Semantic Segmentation of Remote Sensing Imagery.  ... 
doi:10.1109/jstars.2020.2973794 fatcat:sncrozq3fjg4bgjf4lnkslbz3u

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Bar-Massada, A., +, JSTARS 2020 3204-3212 Floods Characterizing Flood Impact on Swiss Floodplains Using Interannual Time Series of Satellite Imagery.  ...  ., +, JSTARS 2020 5915-5928 Semi-MCNN: A Semisupervised Multi-CNN Ensemble Learning Method for Urban Land Cover Classification Using Submeter HRRS Images.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
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