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

2019 ◽  
Vol 11 (21) ◽  
pp. 2492 ◽  
Author(s):  
Bo Peng ◽  
Zonglin Meng ◽  
Qunying Huang ◽  
Caixia Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Real Time ◽  
Spectral Reflectance ◽  
Urban Areas ◽  
Flood Mapping ◽  
Urban Flooding ◽  
Urban Flood ◽  
Before And After ◽  
Flood Maps

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. 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 over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. 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 flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).

scholarly journals Testing Urban Flood Mapping Approaches from Satellite and In-Situ Data Collected during 2017 and 2019 Events in Eastern Canada

2020 ◽  
Vol 12 (19) ◽  
pp. 3141
Author(s):  
Ian Olthof ◽  
Nicolas Svacina
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Urban Areas ◽  
Cloud Cover ◽  
Data Availability ◽  
Flood Mapping ◽  
Eastern Canada ◽  
Urban Flood ◽  
Water Height

The increasing frequency of flooding worldwide has driven research to improve near real-time flood mapping from remote-sensing data. Improved automation and processing speed to map both open water and vegetated area flooding have resulted from these research efforts. Despite these achievements, flood mapping in urban areas where a significant number of overall impacts are felt remains a challenge. Near real-time data availability, shadowing caused by manmade infrastructure, spatial resolution, and cloud cover inhibiting optical transmission, are all factors that complicate detailed urban flood mapping needed to inform response efforts. This paper uses numerous data sources collected during two major flood events that impacted the same region of Eastern Canada in 2017 and 2019 to test different urban flood mapping approaches presented as case studies in three separate urban boroughs. Cloud-free high-resolution 3 m PlanetLab optical data acquired near peak-flood in 2019 were used to generate a maximum flood extent product for that year. Approaches using new Lidar Digital Elevation Models (DEM)s and water height estimated from nineteen RADARSAT-2 flood maps, point-based flood perimeter observations from citizen geographic information, and simulated traffic camera or other urban sensor network data were tested and verified using independent data. Coherent change detection (CCD) using multi-temporal Interferometric Wide (IW) Sentinel-1 data was also tested. Results indicate that while clear-sky high-resolution optical imagery represents the current gold standard, its availability is not guaranteed due to timely coverage and cloud cover. Water height estimated from 8 to 12.5 m resolution RADARSAT-2 flood perimeters were not sufficiently accurate to flood adjacent urban areas using a Lidar DEM in near real-time, but all nineteen scenes combined captured boroughs that flooded at least once in both flood years. CCD identified flooded boroughs and roughly captured their flood extents, but lacked timeliness and sufficient detail to inform street-level decision-making in near real-time. Point-based flood perimeter observation, whether from in-situ sensors or high-resolution optical satellites combined with Lidar DEMs, can generate accurate full flood extents under certain conditions. Observed point-based flood perimeters on manmade features with low topographic variation produced the most accurate flood extents due to reliable water height estimation from these points.

A two-stage analogue model for real-time urban flood forecasting

2021 ◽  
Author(s):  
Chris Onof ◽  
Yuting Chen ◽  
Li-Pen Wang ◽  
Amy Jones ◽  
Susana Ochoa Rodriguez
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Image Feature ◽  
Two Stage ◽  
Analogue Model ◽  
Flood Prediction ◽  
Urban Flood ◽  
Radar Images ◽  
Proposed Model ◽  
Flood Maps

<p>In this work a two-stage (rainfall nowcasting + flood prediction) analogue model for real-time urban flood forecasting is presented. The proposed approach accounts for the complexities of urban rainfall nowcasting while avoiding the expensive computational requirements of real-time urban flood forecasting.</p><p>The model has two consecutive stages:</p><ul><li><strong>(1) Rainfall nowcasting: </strong>0-6h lead time ensemble rainfall nowcasting is achieved by means of an analogue method, based on the assumption that similar climate condition will define similar patterns of temporal evolution of the rainfall. The framework uses the NORA analogue-based forecasting tool (Panziera et al., 2011), consisting of two layers. In the <strong>first layer, </strong>the 120 historical atmospheric (forcing) conditions most similar to the current atmospheric conditions are extracted, with the historical database consisting of ERA5 reanalysis data from the ECMWF and the current conditions derived from the US Global Forecasting System (GFS). In the <strong>second layer</strong>, twelve historical radar images most similar to the current one are extracted from amongst the historical radar images linked to the aforementioned 120 forcing analogues. Lastly, for each of the twelve analogues, the rainfall fields (at resolution of 1km/5min) observed after the present time are taken as one ensemble member. Note that principal component analysis (PCA) and uncorrelated multilinear PCA methods were tested for image feature extraction prior to applying the nearest neighbour technique for analogue selection.</li> <li><strong>(2) Flood prediction: </strong>we predict flood extent using the high-resolution rainfall forecast from Stage 1, along with a database of pre-run flood maps at 1x1 km<sup>2</sup> solution from 157 catalogued historical flood events. A deterministic flood prediction is obtained by using the averaged response from the twelve flood maps associated to the twelve ensemble rainfall nowcasts, where for each gridded area the median value is adopted (assuming flood maps are equiprobabilistic). A probabilistic flood prediction is obtained by generating a quantile-based flood map. Note that the flood maps were generated through rolling ball-based mapping of the flood volumes predicted at each node of the InfoWorks ICM sewer model of the pilot area.</li> </ul><p>The Minworth catchment in the UK (~400 km<sup>2</sup>) was used to demonstrate the proposed model. Cross‑assessment was undertaken for each of 157 flooding events by leaving one event out from training in each iteration and using it for evaluation. With a focus on the spatial replication of flood/non-flood patterns, the predicted flood maps were converted to binary (flood/non-flood) maps. Quantitative assessment was undertaken by means of a contingency table. An average accuracy rate (i.e. proportion of correct predictions, out of all test events) of 71.4% was achieved, with individual accuracy rates ranging from 57.1% to 78.6%). Further testing is needed to confirm initial findings and flood mapping refinement will be pursued.</p><p>The proposed model is fast, easy and relatively inexpensive to operate, making it suitable for direct use by local authorities who often lack the expertise on and/or capabilities for flood modelling and forecasting.</p><p><strong>References: </strong>Panziera et al. 2011. NORA–Nowcasting of Orographic Rainfall by means of Analogues. Quarterly Journal of the Royal Meteorological Society. 137, 2106-2123.</p>

scholarly journals Potential and Limitations of Open Satellite Data for Flood Mapping

2018 ◽  
Vol 10 (11) ◽  
pp. 1673 ◽  
Author(s):  
Davide Notti ◽  
Daniele Giordan ◽  
Fabiana Caló ◽  
Antonio Pepe ◽  
Francesco Zucca ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Flood Hazard ◽  
Flood Mapping ◽  
Ancillary Data ◽  
Po River ◽  
Ground Truth Data ◽  
Hazard Management ◽  
Flooded Areas ◽  
Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In recent years, the availability of free satellite data significantly increased in terms of type and frequency, allowing the production of flood maps at low cost around the world. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. The proposed methods are suitable to be applied by the community involved in flood hazard management, not necessarily experts in remote sensing processing. As case studies, we selected three flood events that recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, and Sentinel-2 and synthetic aperture radar (SAR) data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., Modified Normalized Difference Water Index, SAR backscattering variation, and supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data such as the digital elevation model-based water depth model and available ground truth data. We calculated flood detection performance (flood ratio) for the different datasets by comparing with flood maps made by official river authorities. The results show that it is necessary to consider different factors when selecting the best satellite data. Among these factors, the time of the satellite pass with respect to the flood peak is the most important. With co-flood multispectral images, more than 90% of the flooded area was detected in the 2015 Ebro flood (Spain) case study. With post-flood multispectral data, the flood ratio showed values under 50% a few weeks after the 2016 flood in Po and Tanaro plains (Italy), but it remained useful to map the inundated pattern. The SAR could detect flooding only at the co-flood stage, and the flood ratio showed values below 5% only a few days after the 2016 Po River inundation. Another result of the research was the creation of geomorphology-based inundation maps that matched up to 95% with official flood maps.

scholarly journals Integrate Weather Radar and Monitoring Devices for Urban Flooding Surveillance

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 825 ◽  
Author(s):  
Shih-Yen Hsu ◽  
Tai-Been Chen ◽  
Wei-Chang Du ◽  
Jyh-Horng Wu ◽  
Shih-Chieh Chen
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Weather Radar ◽  
Water Levels ◽  
Mitigation Measures ◽  
Extreme Weather Events ◽  
Television System ◽  
Urban Flood ◽  
Flood Monitoring ◽  
Image Capturing

With the increase of extreme weather events, the frequency and severity of urban flood events in the world are increasing drastically. Therefore, this study develops ARMT (automatic combined ground weather radar and CCTV (Closed Circuit Television System) images for real-time flood monitoring), which integrates real-time ground radar echo images and automatically estimates a rainfall hotspot according to the cloud intensity. Furthermore, ARMT combines CCTV image capturing, analysis, and Fourier processing, identification, water level estimation, and data transmission to provide real-time warning information. Furthermore, the hydrograph data can serve as references for relevant disaster prevention, and response personnel may take advantage of them and make judgements based on them. The ARMT was tested through historical data input, which showed its reliability to be between 83% to 92%. In addition, when applied to real-time monitoring and analysis (e.g., typhoon), it had a reliability of 79% to 93%. With the technology providing information about both images and quantified water levels in flood monitoring, decision makers can quickly better understand the on-site situation so as to make an evacuation decision before the flood disaster occurs as well as discuss appropriate mitigation measures after the disaster to reduce the adverse effects that flooding poses on urban areas.

Automated Multi-Sensor Near-Real Time Flood Monitoring in the Lower Mekong

2020 ◽  
Author(s):  
Amanda Markert ◽  
Kel Markert ◽  
Timothy Mayer ◽  
Farrukh Chisthie ◽  
Biplov Bhandari Bhandari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Southeast Asia ◽  
Real Time ◽  
Disaster Response ◽  
Monsoon Season ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Cost Effective ◽  
Google Earth ◽  
Flood Maps

<p>Floods and water-related disasters impact local populations across many regions in Southeast Asia during the annual monsoon season.  Satellite remote sensing serves as a critical resource for generating flood maps used in disaster efforts to evaluate flood extent and monitor recovery in remote and isolated regions where information is limited.  However, these data are retrieved by multiple sensors, have varying latencies, spatial, temporal, and radiometric resolutions, are distributed in different formats, and require different processing methods making it difficult for end-users to use the data.  SERVIR-Mekong has developed a near real-time flood service, HYDRAFloods, in partnership with Myanmar’s Department of Disaster Management that leverages Google Earth Engine and cloud computing to generate automated multi-sensor flood maps using the most recent imagery available of affected areas. The HYDRAFloods application increases the spatiotemporal monitoring of hydrologic events across large areas by leveraging optical, SAR, and microwave remote sensing data to generate flood water extent maps.  Beta testing of HYDRFloods conducted during the 2019 Southeast Asia monsoon season emphasized the importance of multi-sensor observations as frequent cloud cover limited useable imagery for flood event monitoring. Given HYDRAFloods’ multi-sensor approach, cloud-based resources offer a means to consolidate and streamline the process of accessing, processing, and visualizing flood maps in a more cost effective and computationally efficient way. The HYDRAFlood’s cloud-based approach enables a consistent, automated methodology for generating flood extent maps that are made available through a single, tailored, mapviewer that has been customized based on end-user feedback, allowing users to switch their focus to using data for disaster response.</p>

scholarly journals Development of a Spatial Hydrologic Soil Map Using Spectral Reflectance Band Recognition and a Multiple-Output Artificial Neural Network Model

2017 ◽  
Author(s):  
Khamis Naba Sayl ◽  
Haitham Abdulmohsin Afan ◽  
Nur Shazwani Muhammad ◽  
Ahmed ElShafie
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Spectral Reflectance ◽  
Artificial Neural Network Model ◽  
Performance Criteria ◽  
Artificial Neural ◽  
Soil Groups

Abstract. Soil type is important in any civil engineering project. Thorough and comprehensive information on soils in both the spatial and temporal domains can assist in sustainable hydrological, environmental and agricultural development. Conventional soil sampling and laboratory analysis are generally time-consuming, costly and limited in their ability to retrieve the temporal and spatial variability, especially in large areas. Remote sensing is able to provide meaningful data, including soil properties, on several spatial scales using spectral reflectance. In this study, a multiple-output artificial neural network model was integrated with geographic information system, remote sensing and survey data to determine the distributions of hydrologic soil groups in the Horan Valley in the Western Desert of Iraq. The model performance was evaluated using seven performance criteria along with the hydrologic soil groups developed by the United States Geological Survey (USGS). On the basis of the performance criteria, the model performed best for predicting the spatial distribution of clay soil, and the predicted soil types agreed well with the soil classifications of the USGS. Most of the samples were categorized as sandy loam, whereas one sample was categorized as loamy sand. The proposed method is reliable for predicting the hydrological soil groups in a study area.

scholarly journals Vulnerability Assessment of Urban Floods in Lahore, Pakistan using GIS based integrated Analytical Hierarchy Approach

2021 ◽  
Vol 58 (1) ◽  
pp. 85-96
Author(s):  
Sahar Zia ◽  
Safdar A. Shirazi ◽  
Muhammad Nasar-u-Minallah
Keyword(s):  
Land Use ◽  
Flood Risk ◽  
Urban Areas ◽  
Physical Factors ◽  
Urban Flooding ◽  
Residential Areas ◽  
Urban Flood ◽  
Risk Areas ◽  
Practical Tool ◽  
Flood Risk Mapping

Urban flooding is getting attention due to its adverse impact on urban lives in mega cities of the developing world particularly Pakistan. This study aims at finding a suitable methodology for mapping urban flooded areas to estimate urban flooding vulnerability risks in the cities of developing countries particularly Lahore, Pakistan. To detect the urban flooded vulnerability and risk areas due to natural disaster, GIS-based integrated Analytical Hierarchy Process (AHP) is applied for the case of Lahore, which is the second most populous city and capital of the Punjab, Pakistan. For the present research, the flood risk mapping is prepared by considering these significant physical factors like elevation, slope, and distribution of rainfall, land use, density of the drainage network, and soil type. Results show that the land use factor is the most significant to detect vulnerable areas near roads and commercial areas. For instance, this method of detection is 88%, 80% and 70% accurate for roads, commercial and residential areas. The methodology implemented in the present research can provide a practical tool and techniques to relevant policy and decision-makers authorities to prioritize and actions to mitigate flood risk and vulnerabilities and identify certain vulnerable urban areas, while formulating a methodology for future urban flood risk and vulnerability mitigation through an objectively simple and organizationally secure approach. 

scholarly journals Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion

2019 ◽  
Vol 11 (19) ◽  
pp. 2231 ◽  
Author(s):  
Yu Li ◽  
Sandro Martinis ◽  
Marc Wieland ◽  
Stefan Schlaffer ◽  
Ryo Natsuaki
Keyword(s):  
Bayesian Network ◽  
Rural Areas ◽  
Urban Areas ◽  
Kappa Coefficient ◽  
Urban Environments ◽  
Aerial Photographs ◽  
Flood Event ◽  
Flood Mapping ◽  
Urban Flood ◽  
Other Information

Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.

scholarly journals Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas

2020 ◽  
Vol 10 (21) ◽  
pp. 7448
Author(s):  
Jorge Felipe Gaviria ◽  
Alejandra Escalante-Perez ◽  
Juan Camilo Castiblanco ◽  
Nicolas Vergara ◽  
Valentina Parra-Garces ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Urban Areas ◽  
Low Cost ◽  
Raspberry Pi ◽  
Automatic Identification ◽  
Signal Recognition ◽  
Main Challenge

Real-time automatic identification of audio distress signals in urban areas is a task that in a smart city can improve response times in emergency alert systems. The main challenge in this problem lies in finding a model that is able to accurately recognize these type of signals in the presence of background noise and allows for real-time processing. In this paper, we present the design of a portable and low-cost device for accurate audio distress signal recognition in real urban scenarios based on deep learning models. As real audio distress recordings in urban areas have not been collected and made publicly available so far, we first constructed a database where audios were recorded in urban areas using a low-cost microphone. Using this database, we trained a deep multi-headed 2D convolutional neural network that processed temporal and frequency features to accurately recognize audio distress signals in noisy environments with a significant performance improvement to other methods from the literature. Then, we deployed and assessed the trained convolutional neural network model on a Raspberry Pi that, along with the low-cost microphone, constituted a device for accurate real-time audio recognition. Source code and database are publicly available.

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