Application Of Deep Learning On UAV-Based Aerial Images For Flood Detection

smart cities
Article
Application of Deep Learning on UAV-Based Aerial Images for
Flood Detection
Hafiz Suliman Munawar 1 , Fahim Ullah 2,* , Siddra Qayyum 1 and Amirhossein Heravi 2

Citation: Munawar, H.S.; Ullah, F.;
Qayyum, S.; Heravi, A. Application
of Deep Learning on UAV-Based
Aerial Images for Flood Detection.
Smart Cities 2021, 4, 1220–1242.
https://doi.org/10.3390/
smartcities4030065
Academic Editor: Pierluigi Siano
Received: 20 August 2021
Accepted: 16 September 2021
Published: 18 September 2021
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Attribution (CC BY) license (https://
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4.0/).
1 School of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, Australia;
h.munawar@unsw.edu.au (H.S.M.); s.qayyum@unsw.edu.au (S.Q.)
2 School of Civil Engineering and Surveying, University of Southern Queensland,
Springfield, QLD 4300, Australia; amirhossein.heravi@usq.edu.au
* Correspondence: fahim.ullah@usq.edu.au
Abstract: Floods are one of the most fatal and devastating disasters, instigating an immense loss of
human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is
a need to develop and implement real-time flood management systems that could instantly detect
flooded regions to initiate relief activities as early as possible. Current imaging systems, relying
on satellites, have demonstrated low accuracy and delayed response, making them unreliable and
impractical to be used in emergency responses to natural disasters such as flooding. This research
employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can
identify inundated areas from aerial images. The Haar cascade classifier was explored in the case
study to detect landmarks such as roads and buildings from the aerial images captured by UAVs
and identify flooded areas. The extracted landmarks are added to the training dataset that is used to
train a deep learning algorithm. Experimental results show that buildings and roads can be detected
from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded
in classifying flooded and non-flooded regions from the input case study images. The system has
shown promising results on test images belonging to both pre- and post-flood classes. The flood relief
and rescue workers can quickly locate flooded regions and rescue stranded people using this system.
Such real-time flood inundation systems will help transform the disaster management systems in
line with modern smart cities initiatives.
Keywords: flood detection; deep learning; landmarks detection; UAV dataset; disaster management
1. Introduction and Background
On average, 60,000 lives are lost to natural disasters every year, accounting for 0.1%
of the global deaths [1]. These natural disasters include floods, earthquakes, hurricanes,
landslides, and others. Floods are the most frequently occurring natural disasters globally,
representing 40% of global natural disasters [2]. Climate change, hurricanes, heavy precipi-
tation, glacier melting, and winter storms are the underlying factors to be blamed for the
dramatic rise in flood risks [3,4].
Floods have induced damages amounting to hundreds of millions of dollars on
average, along with the loss of thousands of human lives [5–8]. Apart from the loss of lives,
floods cause great damage to the infrastructure and property, agricultural lands, crops,
and livestock, resulting in huge economic losses, which must be minimized in the era of
focus on sustainability and smart cities [9–11]. Extreme rainfall events occurring in the
first decade of the new millennium have caused a substantial increase in flood events,
raising the flood-related losses from USD 6 billion to USD 10 billion. Accordingly, billions
of dollars have been invested in implementing effective flood control measures [2,12].
The associated rescue missions, rehabilitation, and relief services also place an additional
economic burden on the economic development of the affected country. According to an
estimate of flood-related economic loss in 2012, the world lost USD 19 billion to floods in
Smart Cities 2021, 4, 1220–1242. https://doi.org/10.3390/smartcities4030065 https://www.mdpi.com/journal/smartcities

Smart Cities 2021, 4 1221
various global regions. Due to untimely detection of floods and lack of accurate and fast
technologies that could automatically detect the occurrence of flooding in an area, lives
are lost as aids and recovery services cannot be provided on time. This signifies the need
to use advanced digital technologies to detect flood-affected areas quickly and accurately
so that rescue activities can be initiated as soon as possible [2,12–18]. Such timely flood
detection is crucial to efficiently plan relief missions and rescue the stranded people, thus
minimizing its economic impacts and casualties [19–21].
Geographic Information System (GIS) is an important component that provides essen-
tial disaster management decision support and analytical capabilities [21,22]. It enables
the authorities to acquire, save, manage, and analyze spatial or geographic data to provide
appropriate disaster response [23,24]. GIS can automatically determine the flood-affected
regions and integrate the results with the available geographic data, thus assisting in the
better detection of floods [21]. It has been used to determine rescue routes and the available
transport facilities in flood-affected areas [25,26]. However, this technology relies heavily
on the availability of information about the disaster. Such information is only available
after a couple of days, if not weeks, resulting in a slow response in an emergency case such
as a flood. Global Positioning System (GPS) is a global navigation satellite system (GNSS)
that provides geolocation and time information to a GPS receiver anywhere on or near
the Earth where there is an unobstructed line of sight to four or more GPS satellites [23].
This technology has been frequently used in post-flood disaster management and relief
activities. An example is that of the GPS sensor nodes installed on the rooftop of a building,
providing relative position information from both pre- and post-flood disaster periods.
The changes in geolocation points between different building components before and
after the flood are calculated and used to estimate damage, building movement scale, and
factors such as stress and strain for a precise assessment of the damage. Similarly, the
images captured by GPS-enabled devices can be further analyzed using image processing
techniques to detect a flood event. However, GPS-based estimations have a certain degree
of ambiguity, as the exact location of the flooded area cannot be mapped. This is because
there is roughly an error of 15 m for every 3 km in the GPS results [27]. Moreover, another
limitation of the GPS is that it relies on internet services. In times of emergency, network
services such as the internet and Wi-Fi are mostly unavailable. Hence, technologies such as
GIS and GPS become inaccessible. This leads to a lack of precise data about the location of
flooded areas and the affected people requiring aid. Accordingly, the rescue services may
be delayed or interrupted.
Satellite imaging has been used to capture high-quality images of the target area. These
images are analyzed using image processing methods such as edge detection, segmentation,
and pixel-based analysis [28,29]. However, the quality of these satellite images is greatly
affected by noise, illumination conditions, weather, and other barriers between the earth
and the satellite, such as clouds [30]. Furthermore, due to the large number of high-
resolution images stored in the satellite databases, the speed of image processing is affected,
resulting in a slower response. To address the speed concerns, remote sensing techniques
are used to collect data of large areas quickly. Moreover, these techniques also allow the
generation of detailed descriptions of the objects without having any direct connection.
The working mechanism involves using optical and radar imagery to measure water levels
to define the scope of a flooded area [31].
Similarly, object detection has been utilized with aerial images to extract features
that can be analyzed to make flood-related response decisions. Target recognition of
landmarks such as roads and buildings from aerial images has been done using Hough
transform and isotropic surround suppression to find rescue routes [32,33]. Furthermore,
edge detection methods can be used to identify and extract objects from images, such
as detecting a horizontal water line representing the surface level of water on roads
and streets or the height of a dam [28]. Bridge detection has been performed using
aerial images to aid disaster relief missions by mining and analyzing multispectral aerial
image patterns [32,33]. Synthetic Aperture Radar (SAR) has been used to capture remote

images, define a pixel-based threshold and classify flooded or non-flooded regions [34].
Mason et al. [35] used image segmentation and classification techniques on SAR images for
real-time flood detection. However, the quality of images and availability of internet or
satellite connectivity may affect disaster response planning. Therefore, alternate methods
need to be explored and utilized for disaster response planning in flooded regions [36].
Accordingly, Unmanned Aerial Vehicles (UAVs) can be used as efficient tools that can
capture high-resolution spatial images from the target sites [22]. These UAVs are widely
used these days instead of the traditional imaging tools such as satellite imaging and GPS-
based monitoring as smart technologies in the industry 4.0 era [37–41]. UAVs can quickly
collect precise image data and transmit it to their respective off-site servers for sharper,
smarter, and more informed responses [41]. Similarly, Artificial Neural Network (ANN)
models are increasingly used for flood prediction and detection [42,43]. Chang et al. [44]
proposed a hybrid ANN-based model using a self-organizing map (SOM) and the recurrent
nonlinear autoregressive network with exogenous inputs (RNARX) to generate regional
flood inundation maps during storms events. The authors stated that the 4 × 4 SOM
network could cluster inundation depths of the target area, while the RNARX network can
forecast the inundation depths.
Similarly, Chang et al. [45] developed an early flood warning system by integrating a
hydrodynamic model, k-means clustering algorithm, and support vector machines (SVM)
to detect typhoon flood events and accurately predict both the inundation depth and
extent [46]. Fuzzy-logic-based systems are also quite popular and have been used to
forecast river water levels and raise an early alarm in case of floods [47]. Harmonic analysis
and change detection have been used on multi-temporal data for flood detection, with
an accuracy of 80% [48]. Likewise, a new method for change detection and thresholding
(CDAT) was used with SAR images to delineate the extent of flooding for the Chobe
floodplain in the Caprivi region of Namibia [49]. A Bayesian network has also been
proposed to integrate remotely sensed data, such as multi-temporal SAR intensity images
and interferometric-SAR coherence data, with geomorphic and other ground information
such as roads and buildings [50].
Furthermore, a back-propagation-based ANN method called Multilayer Perceptron
(MLP) has been used to predict floods using rainfall time series data and water levels in a
weir that can spread into the cities [51]. Similarly, a Wavelength Neural Network (WNN)
has been used for flood modeling [52]. Thus the pertinent literature shows that image
processing and machine learning techniques have been widely used for flood detection, but
deep learning is rare and not well experimented with or documented for such purposes [46].
This presents a gap targeted on the current study.
Accordingly, a set of key landmarks comprising roads and buildings is detected and
extracted in this study. These landmarks are added to the original dataset used to train
a deep learning model to help the disaster management team plan an effective response.
The study demonstrates the results using original and altered datasets and compares them
with previous flood detection methodologies to highlight their significance. A case study
approach is adopted where the flood-prone area of northern Pakistan, known as Swat, is
investigated, and the technique is applied to extract landmark objects in the flooded region.
The motivation for using UAVs for capturing aerial images of the disaster-hit region is
due to its potential to capture high-resolution images in a short period without requiring
human assistance. This makes UAVs safe to investigate high-risk areas that are unreachable
by humans during disaster events. Thus, UAVs are ideal for acquiring image data in
disasters. The idea of integrating machine learning and image processing for flood detection
and damage assessment facilitates generating results in the least amount of time, which are
accurate and precise without relying on human intervention. The problem addressed in this
paper is the “detection of floods”. For this purpose, UAVs are used that are complemented
through the implementation of deep learning models on aerial images acquired by them.
The detection and monitoring of flooded areas in rural zones are essential to assess the
damages to critical infrastructure, find and locate the population, and find an evacuation

route for the disaster victims. The proposed system ensures the extraction of key landmarks
such as roads, buildings, and bridges that are georeferenced with the stored maps to make
appropriate post-disaster decisions.
Pakistan is a developing country with a growing economy that is faced with sev-
eral challenges. These challenges range from cost and time overruns in projects to brain
drain, lack of competitiveness of local industries, corruption, political instability, lack of
legal frameworks and insurances, and frequent natural disasters such as floods and earth-
quakes [21,24,53–58]. The regularly occurring devastating floods severely dent the local
economy. In Pakistan, flood events in 2010, 2011, and 2013 caused immense destruction
and fatalities [21]. Pakistan has faced a loss of approximately USD 38 billion owing to
floods in the past 70 years [59,60]. In 2010, massive floods caused by the monsoon rains
caused 2000 casualties, affected 20 million people, caused food shortage for 7.8 million
people, and resulted in damages worth USD 16 billion [21]. The healthcare facilities of
the country also suffered as 436 settings providing health-related facilities were lost in the
disaster [61]. The underlying cause of floods in Pakistan is heavy rainfall every year during
the monsoon season (July–August). Last year alone, more than 230 lives were lost to the
floods generated by monsoon rains in Pakistan [62–64]. Therefore, there is a dire need to
propose effective flood mapping techniques in Pakistan.
For pertinent flood detection in this study, Convolution Neural Network (CNN)
is used. It is a multilayer neural network, and one of the most classical and common
deep learning frameworks [65]. Previously, this classification model has demonstrated
excellent performance for image classification, segmentation, and extraction [65,66]. One
advantage is its self-learning ability, as it can automatically learn features from large
datasets by organizing multiple layers of neurons. Traditional machine learning models
such as SVM have been used for flood detection that has shown good results, but the
complexity of this model grows significantly as the training dataset increases. Apart from
that, SVMs need to be tuned to find the optimal kernel function for training. The parameter
optimization related to the kernel function is the key factor affecting the classification
effect [66]. Therefore, to handle datasets such as the one in the current study, researchers
are moving towards deep learning and utilizing deep learning models such as RNN and
CNN for image classification and segmentation problems. Previously, CNN has been
applied for the classification of data captured through remote sensing [67]. However, the
application of CNN for flood mapping is rare and has not been thoroughly investigated.
Therefore, the current study utilizes CNN to detect floods from aerial images captured
through UAVs in Pakistan.
In the current study, a detailed literature review of the recently used flood detection
technologies was performed to obtain insights into the existing methods used for flood
detection. This information was subsequently used to select the most appropriate method-
ologies/parameters well suited to our data set. Moreover, by reviewing the existing work,
current gaps in the research were identified, and proper strategies to overcome these gaps
were devised and implemented. For this purpose, several search queries were formulated
and used in literature search engines (i.e., Scopus and Web of Science) to extract the most
recent studies using literature retrieval methods [2,9–12,17,18,68,69]. The search process
was restricted to the last decade (2010–20) so that the most recent articles were retrieved.
The review process was conducted in two phases: article retrieval and screening, as shown
in Figure 1. The literature review was performed by removing duplicates articles, and
screening research articles, book chapters, and conference papers published in the English
language only.
After the screening process, a total of 98 articles were shortlisted. Among these, 34%
of articles proposed image-processing-based techniques for flood mapping, 26% used
machine learning, 21% were based on deep learning, and 19% of articles used methods
belonging to other domains, as shown in Figure 1.

gence of two rivers, called “Daral” and “Swat”
Figure 1. Detailed process of screening for the most relevant papers related to our research questions.
The paper is organized as follows. The Section 2 explains the research methodology
adopted in this study. One of the most flood-prone areas in the country, i.e., Swat, is dis-
cussed as a case study, and the process of acquiring images for the dataset from this area is
also elaborated. The Section 3 presents an overview of the evaluation process, experimental
results, and comparison with existing techniques. Finally, the overall achievements and
limitations of the proposed study are presented, and the study is concluded.
2. Research Methodology
2.1. Case Study Area
The case study for the current study is the Swat valley, a district of Khyber Pakhtunkhwa,
situated in northern Pakistan, as shown in Figure 2a,b. Located at the convergence of two
rivers, called “Daral” and “Swat”, this region is constantly at high risk of floods and was
severely affected by floods in 2010. Last year alone, 30 lives were lost, and 38 others
were injured in this region during the recent flash flood event. In addition, more than
130 houses, 1 bridge, and a worship place have been damaged or destroyed in these flash
floods. Furthermore, several roads have been blocked or damaged by flash floods, isolating
many communities [64].
(a) (b) (c)
Swat
Figure 2. (a) Swat District on a map of Pakistan; (b) Swat area; (c) aerial image of a flood-hit area in Swat.

Swat is enlisted under the high-risk areas for floods by Pakistan Disaster Management
Authority (PDMA). According to PDMA, this area can become completely inaccessible if
hit by a massive flood again. The recent flash flooding tested this statement and proved
rightful as there occurred a significant delay in rescue services due to the inaccessibility
of the land and damage of connecting bridges leading to more fatalities. Owing to this
significance, this area is selected as a case study for the current research.
To address the flood risks in this region, multispectral aerial images from this area
were captured for developing a comprehensive dataset. A Red-Green-Blue (RGB) image
captured by UAV from this region is shown in Figure 2c that covers a residential area of
Swat. The spatial resolution of the image is 0.23 m, allowing precise detection of inundated
areas. The image has a size of 19,956 × 12,444 and covers an overall area of approximately
11 km2
. In total, 300 images were captured by the UAV, whereas the remaining dataset
was constructed using the pre- and post-flood images collected from the local databases
maintained by PDMA.
2.2. Proposed System Workflow
An abstract-level flowchart of the proposed methodology is shown in Figure 3 that
has six major steps:
1. Image acquisition and data collection using UAV;
2. Preprocessing of the images;
3. Selection of landmarks features for detection;
4. Training the model on the dataset;
5. Flood detection using image classification;
6. Performance evaluation of the proposed system.
DataSet
RGB Images
Noise Reduction Orthorectification
Bridges Roads
RGB Images Bridges Roads
Convolution Neural Network
10-Fold Cross Validation
UAV
Data Collection
Pre-Processing
Feature Selection:
Landmark Detection
Training Dataset
Flood Detection
Evaluation
Figure 3. Proposed methodology.

2.2.1. Image Acquisition
For surveillance of the flooded areas and their inundation detection, a small-sized
UAV called River-map was selected. Go-Pro® digital camera was onboard for capturing
high-resolution RGB aerial images of the case study area. Using this set up, real-time
surveillance of the case study area was performed on 2 September 2020, as shown in
Figure 4a,b, highlighting the destruction caused by the flood in this area. The images show
that most roads are sunk into the water, and buildings are collapsed due to the high intensity
of floodwater, making the process of relief work more difficult and time-consuming, if
not impossible. ‘ ’
(a) (b)
It is a nonlinear filter used to remove “salt and pepper” noise
𝐼′
(𝑢, 𝑣)‹– 𝑀𝑒𝑑𝑖𝑎𝑛{𝐼(𝑢 + 𝑖, 𝑣 + 𝑗)|(𝑖, 𝑗)Ɛ 𝑅
’
Figure 4. Flooded regions of Swat Valley. (a) A village (b) A damaged bridge.
The data acquired by the UAV was in the form of RGB images with some level of
distortion. Such distortion is an inherent characteristic of the images captured through
satellite imagery or an aerial imaging system. Furthermore, the surface of the earth has
some topographical differences, and due to an inclined angle of satellite, the UAV, or the
aerial camera, the distance between the displayed features may not be 100% accurate or
a true representative of the ground realities. The distortion in images increases with an
increase in the topographical variance of the landscape. This distortion must be eliminated
to infer meaningful insights from the captured images. For this purpose, the image
processing technique of “orthorectification” was applied. The orthorectification process
removes the effects of tilts and terrain from the images to create a planimetrically correct
image. The resultant orthorectified image had a more constant scale showing captured
features in their ‘true’ positions.
Furthermore, the captured images may have some noise due to environmental factors
such as air pollution, dust, smoke, and fog. Therefore, a median filter with good de-noising
power and mathematical accuracy is applied to the captured images to reduce this noise.
The median filter is one of the popular order-statistic filters that is effectively used in digital
image processing. It is a nonlinear filter used to remove “salt and pepper” noise from
images while maintaining the edges of features. This filter was applied to the captured
images in the current study so that the feature extraction in the next steps would not be
affected, and high-quality images can be utilized for inferring meaningful results. A fixed
filtering window size is used in a median filter, and the pixel value (target pixel) is replaced
by the median value of the intensity levels of its neighboring pixels. Herein, the filter sorts
all pixels in a window according to their numerical value and replaces the value of the
target pixel with the median of the values of pixels in that window as shown in Figure 5.
The working of the median filter is described using Equation (1) and Figure 5, respectively:
I′
(u, v) ← Median{I(u + i, v + j)|(i, j)ε R} (1)

Here, R is defined as the moving region for all values in the median filter, I′(u, v)
represents the current location, and I(u + i, v + j) denotes the corresponding image element.
’
Figure 5. The working of the median filter for noise removal.
2.2.2. Preprocessing
Image preprocessing is a prerequisite step to enhance the quality of the input images
and prepare them for further processing in the subsequent steps. It involves downloading
the raw images from the UAV’s digital camera, storing them in a database, removing
noise, and applying orthorectification. Data preprocessing is performed to take into
account different variations (i.e., size, shape, and brightness) in images. Therefore, after
data collection, preprocessing was performed in the current study to remove unwanted
objects and noise from the captured images. Additionally, the brightness and sizes of the
collected images were also adjusted, followed by the removal of unwanted background
regions/surfaces using image cropping. For the proposed CNNs, data augmentation was
performed based on random image cropping and patching that was further used for label
generation and flood detection in the entire training procedure [70]. Furthermore, the
feature selection was conducted by extracting landmark features from the preprocessed
images using a supervised learning approach.
In the current study, landmark objects are restricted to bridges, buildings, and roads.
The extracted landmarks are then combined with the raw Red-Green-Blue (RGB) images to
build the feature space for training a CNN classifier. Furthermore, the classifier is evaluated
to test its flood detection capability on new test images. Finally, the performance is assessed
using the confusion matrix derived from the validation process.
2.2.3. Selection of Landmarks Features for Detection
In the context of image classification, feature selection plays a vital role in achieving
high accuracy. Good features can enhance the inter-class separation and decrease the
in-class variance [71]. Thus, it is imperative to capture and select high-quality images with
more pronounced attributes for getting accurate results. The images captured by the UAV
in the current study had three color bands: red, green, and blue, which are not sufficient

to achieve high classification performance since many other objects on the ground may
have the same color leading to false detections by the classifier. Thus, it is necessary to
extract relevant features from the input images, increase the inter-class separability, and
remove irrelevant and redundant parts of the images. By analyzing the aerial images, it
was noticed that most of the key landmarks were roads, buildings, and bridges that were
subsequently chosen as target landmarks to be extracted using an automated tool. From
the literature review, common image processing techniques used for road detection from
images are edge detection and line-based extraction used in this study.
Supervised learning was utilized in this study to detect the objects of interest using
the Haar cascade classifier. This classifier uses Haar-like features to identify objects from
images. The main advantage is its high computational speed, making it suitable for
real-time landmark detection when planning emergency responses. Thus, this method is
adopted to detect roads, bridges, and buildings such as houses from the input images in
the current study. Instead of looking at the pixels in an image, a Haar-like feature detector
analyzes rectangular blocks and computes the total pixel intensity in the region. It then
determines the differences between the calculated sums of each region.
For example, consider the shaded area shown in Figure 6. If ‘I’ denotes the integral
image and ‘P’, ‘Q’, ‘R’, and ‘S’ are points of a rectangular area in this image. The points P, Q,
R, and S represent the four reference points used for the computation of images values on
the black region. These points are described as P(x0, y0), Q(x1, y0), R(x0, y1), and S(x1, y1),
respectively, as shown in Figure 6. The sum of the shaded region/area with points P, Q, R,
and S is calculated using the sum(PQRS), as described in Equation (2). Thus, the sum(PQRS)
can be computed in a constant time using only four references, i.e., I(P(x0, y0)), I(Q(x1, y0)),
I(R(x0, y1)) and I(S(x1, y1)) to the integral image (Equation (2) [72–74], as follows:
sum(PQRS) = I(S) + I(P) − I(Q) − I(R) (2)
‘ ’
‘ ’ ‘ ’ ‘ ’ ‘ ’
–
𝑠𝑢𝑚(𝑃𝑄𝑅𝑆) = 𝐼(𝑆) + 𝐼(𝑃) − 𝐼(𝑄) − 𝐼(𝑅)
Figure 6. Integral image and Haar-like features.
This use of integral images helps in achieving computational efficiency, which is
lacking in traditional methods. Haar-like features consist of predefined edge, line, and
center-surround features, as shown in Figure 6. A strong classifier such as the Haar classifier
can detect a feature under varying illumination, scale, and color. This makes the Haar
classifier an ideal method for landmarks detection in aerial images, as these images can
have varying lighting conditions and color properties during day and night or under
different climatic conditions. Accordingly, it has been used in the current study.
2.2.4. Training Datasets
The RGB format is one of the most prominent encoding formats used for the repre-
sentation of most natural images. As discussed earlier, for training the CNN, we used

the original images in the RGB format that were used to extract landmark features from
the preprocessed images using a supervised learning approach. The collected dataset
contained both original RGB images and the landmarks extracted from these images in the
feature selection stage. Generally, during the training procedure, the high performance
CNNs are likely to display chances of over-fitting, which might be due to the memorization
of the non-generalized image features by the CNNs that are present in the training set.
Therefore, using a sufficient set of training samples is extremely important to avoid the
over-fitting of the model [75]. Collecting a sufficient set of training samples is costly; there-
fore, data augmentation methods such as flipping, resizing, and random cropping are used
to cater to it [76,77]. Applying the aforementioned augmentation techniques is essential to
increase the level of variations in the collected images to prevent model over-fitting [76,77].
Accordingly, these have been used in the current study.
Additionally, for the current study, both training and test sets were visually interpreted.
The visual interpretation of both sets highlighted that the test set images contain five pixel
classes (i.e., buildings, bridges, roads, soil, vegetation, and water). However, all the classes
were not present in all training images, thus leading to an imbalance problem. This
imbalance problem was resolved using a balancing function based on median frequency
in which a weight is assigned to each of the five-pixel classes that are absent in an image
using the following Equation (3):
w =
Median(c f )
class f requency
(3)
where ‘cf’ represents the class frequencies calculated over the whole dataset that are
calculated using Equation (4):
class f requency =
Number o f pixels in each class
Total pixels in the image
(4)
The frequency of each of the classes in the training dataset of the current study is
shown in Table 1.
Table 1. Frequency of each class in the dataset.
Class Frequency (%)
Buildings 30.1
Roads 42.8
Soil 11.9
Grass 10
Water 5.2
Bridges 1.1
By analyzing the map of Swat, training samples containing the images of river “Swat”
and river “Daral” were eliminated to avoid the ambiguity arising from their classification
under the flooded category. Overall, the training dataset contained 3000 images that were
utilized for the current study. These images are used to extract the landmark features and
subsequently aid in rescue operations.
To train the classifier, a set of labeled positive sample images containing the object to be
detected and a set of labeled negative samples that do not contain the object are needed [78].
These datasets were constructed from scratch for the case study area as there is no previous
research that used supervised learning for object detection in aerial images in the case
study area. Images were gathered from available online databases of Microsoft Bing Maps,
Google Maps, and Google Earth. Furthermore, images were extracted at varying altitudes,
brightness, and scales to form a dataset containing diverse images. A total of 3000 aerial
images of buildings (1000), bridges (1000), and images of roads (1000) were extracted for
the case study area.

Figure 7 shows part of the road and building images datasets used for training the
classifier. The next step was to label objects in each of the downloaded images. This
involves highlighting, cropping, and naming the target object in each image. Furthermore,
a negative training set was developed by cropping the regions not containing the target
object from the images. In total, 1000 negative samples were collected for buildings,
bridges, and road datasets, respectively. Finally, the OpenCV computer vision library,
which provides the utility to train a Haar cascade classifier, was used for training. This
involves creating a feature vector of the training dataset and providing it as an input to
the classifier. The detected images of buildings and roads were cropped and added to the
original dataset containing the RGB images, as shown in Figure 7.
Figure 7. (a) Subset of aerial road images used for training (b) Subset of aerial building images used for training (c) Haar
Cascade Classifier Results.
2.2.5. Flood Detection Using Image Classification
CNN has been used in this study for detecting floods. The architecture for CNN
is shown in Figure 8, in which three layers are used: convolution, pooling, and fully
connected layers.

‘ ’ ‘ ’
‘ ’ ‘ ’
‘ ’
𝑐(𝑡) = 𝑥(𝑡) ∗ 𝑦(𝑡) = ∫ 𝑥(𝜏)𝑦(𝑡 − 𝜏)𝑑𝜏
+∞
−∞
‘
’ ‘ ’
‘ ’
𝑐(𝑎, 𝑏) = 𝑥(𝑎, 𝑏) ∗ 𝑦(𝑎, 𝑏)
‘ ’ ‘ ’ ‘ ’
𝑦𝑗 𝑤𝑗𝑘
𝑣𝑘
𝑂 = ∑ 𝑤𝑗𝑘 × 𝑦𝑗 + 𝑣𝑘
𝑛
𝑗=1
nique in which a filter of size “ ” is selected, and a maximum operation is applied over
the “ ”
Convolution + ReLu Pooling Fully Connected
Fully Connected + ReLu
Input
Figure 8. Proposed architecture diagram of CNN.
Convolution is a common analytical operation that is applied to signal and image
processing problems. Different features from an image, such as texture and edges, can
be obtained using a convolution function. The overlapping area of two functions, ‘x’ and
‘y’, can be computed using the convolution operator. If ‘x’ is the original function and
‘y’ represents its flipped form, Equation (5) can determine the third function ‘c’ [43,52]
as follows:
c(t) = x(t) ∗ y(t) =
Z +∞
−∞
x(τ)y(t − τ)dτ (5)
Furthermore, a digital image is viewed as a two-dimensional function such as ‘x(a, b)’
in image processing. Therefore, using a two-dimensional convolution function, ‘y(a, b)’, the
output image ‘c(a, b)’ can be determined through Equation (6) as follows:
c(a, b) = x(a, b) ∗ y(a, b) (6)
Similarly, in the case of a colored image that contains three channels, red, green, and
blue, the input image of width ‘w’ and length ‘l’ is an array of size ‘S’, which is calculated
using Equation (7) as follows:
S = 3 × w × l (7)
A feature map is obtained as an output of the convolutional layer. This is obtained by
taking a sum of the inputs (yj) of all neurons multiplied by their weights (wjk), plus a bias
value (vk) and an activation function as shown in Equation (8):
O =
n
∑
j=1
wjk × yj + vk (8)
The Rectified Linear Unit (ReLU) is a standard activation function for neural networks.
It makes the model easier to train and brings better performance outcomes. This function
activates a node by outputting the input directly if it is positive and otherwise returning
zero. This has been applied in the current study.
A pooling layer is added immediately after the convolutional layer and applied to
decrease the width and height of the test image in the current study. This simplifies
the computation as the number of parameters is reduced by decreasing the spatial size.
This also tackles the over-fitting problem. Max pooling is the most widely used pooling
technique in which a filter of size “s × s” is selected, and a maximum operation is applied
over the “s × s”-sized subset of the image. After the pooling layer is added, a fully
connected layer is introduced in which each neuron receives input from every neuron
present in the previous layer. Computation, based on the multiplication of matrices and a

bias offset, is used to determine the output. The aim is to compute class scores to classify
the image in the current study.
2.2.6. Results Extraction and Performance Evaluation of the Proposed System
Images captured by the UAV in the current study contained rich spatial information
and hence consumed more memory space. Due to limited memory capacity, these images
were divided into smaller patches of 525 × 525 in size. To test the model, an evaluation
method based on 10-fold cross-validation was applied, in which the dataset was divided
into 10 equal parts or folds. This method was used to overcome the problem of overfitting
of data and enhance the generalization performance of the classifier. One fold was used as
the test set in each iteration, and the remaining parts were combined and used for training
the system. This step was repeated 10 times, taking a new set for testing in each iteration,
thus using unique testing set in each step. The classification accuracy and error were
calculated using accuracy and error percentages from all 10 folds.
To train the CNN, images from both flooded and non-flooded categories were fed to
the first convolutional layer, followed by two convolutional layers, a pooling layer, and
finally, two fully connected layers. Initially, data is collected when the concept of CNNs is
introduced, and models are trained using machine learning. Accordingly, in this study, data
collection was performed using images from the flooded and non-flooded categories. This
was followed by data preprocessing and labeling. For data labeling, different techniques,
including bonding box and semantic segmentation, can be used. Accordingly, in this study,
we have used semantic segmentation, which is a pixel-by-pixel labeling method where
the water pixels, and the background pixels were extracted separately. Thus, an improved
version of CNN was used in this study. Herein, the final fully connected layers produce
only two outputs that classify the image into either a flooded or non-flooded category.
During the learning process, weights of the input variables were tuned in the convolu-
tional layer in this study by taking random values for the parameters and updating them
using back-propagation. The learning rate was 0.0001, and the maximum epoch value
was set as 5 for all classes. A total of 167,400 iterations were conducted in the training
process of the 10-fold validation. As a result of the training, the model learned to link
images with class labels and make predictions about test images. It took a 24-hour period
for cross-validation using an Intel Quad Core i7-8550U Processor at 1.8 GHz to extract the
results. For assessing the performance of the system, a confusion matrix was used to assess
and highlight the accuracy of the classification method. It provided a complete measure of
the performance of a classifier by separating correct predictions from the incorrect ones for
each class in the dataset. The confusion matrix was divided into four cells representing
true positives (T.P.), true negatives (T.N.), false positives (F.P.), and false negatives (F.N.), as
shown in Table 2.
Table 2. The confusion matrix.
Predicted Values
Actual Values
Positive Negative
Positive TP FP
Negative FN TN
Other measures such as precision, recall, and F-score were taken along with accuracy
for performance evaluation of the classifier, as the accuracy alone does not give sufficient
information about the class-wise results. For example, consider a dataset having 100 images,
of which 95 belong to the non-flooded class and 5 belong to the flooded class. If all the
images are classified as non-flooded, the accuracy will still be 95%, even though the flood
was not successfully detected in any of the images. Hence, relying only upon accuracy
may not be the right approach, and other measures are needed need to be put in place.
Accordingly, in this study, other measures such as recall, precision, and others calculate the

TP. The formula for these performance measures, including the accuracy, recall, precision,
f-score, true positive rate, and false-positive rate, are given in Equations (9)–(14):
Accuracy =
TP + TN
TP + FN + TN + FP
× 100% (9)
Recall =
TP
TP + FN
(10)
Precision =
TP
TP + FP
(11)
F − Score = 2 ×
Precision × Recall
Precision + Recall
(12)
True Positive Rate = TPR =
TP
TP + FN
(13)
False Positive Rate (Speci f icity) = FPR =
FP
TN + FP
(14)
Overall, in terms of the time taken to conduct the study, it took almost nine hours
to preprocess the input raw images. This included noise removal and orthorectification
operations. Landmarks extraction by training a Haar cascade classifier and cropping
the landmarks took 5 h. Finally, training the CNN-based deep neural network using
the updated dataset took 24 h. Using the trained model to extract flooded regions took
almost eight hours. This is a reasonable time to preprocess, analyze, extract features, and
train a flood detection model from scratch, beginning with remotely sensed raw data to
instigate an immediate response plan. Compared to other techniques that take weeks or
sometimes months to plan a proper response, the current method is speedy and accurate.
Upon completing the training process, testing was carried out using 300 aerial images
of buildings and roads that included images captured at varying altitudes, scales, and
illumination conditions. The classifier correctly identified roads and buildings from the
dataset with 91% and 94% accuracy, respectively.
3. Results and Discussions
As explained in the method section, the model was first trained using the original
dataset and the generated test results. Table 3 shows the confusion matrix generated by
applying a dataset of test images to the trained model. The test dataset consists of 400
flooded and 400 non-flooded images. The results show that out of a total of 800 images,
675 were correctly classified, showing an 84.4% accuracy.
Table 3. Confusion matrix generated for a model trained using an original dataset.
Predicted Class
Flooded Non-Flooded Total
Actual Class
Flooded 352 48 400
Non-Flooded 77 323 400
Table 4 shows the confusion matrix generated after applying the test images dataset to
a model trained using the altered (improved) dataset. This resulted in 728 out of 800 images
being correctly classified, making the trained model 91% accurate.
By applying Equations (9)–(12) to determine accuracy, recall, precision, and f-Score,
respectively, we get the values as reported in Table 5. The results with and without
landmarks addition have been compared in Table 5. The overall accuracy of 91% has been
recorded after adding landmarks to the dataset, with a 6% improvement in the accuracy
achieved using the original dataset. Furthermore, as shown in Table 5, the precision, recall,
and f-scores of the model have been improved by 8%, 5%, and 6%, respectively, when the
altered dataset is used to train the CNN model.

Table 4. Confusion matrix generated for a model trained using an altered dataset.
Predicted Class
Flooded Non-Flooded Total
Actual Class
Flooded 371 19 400
Non-Flooded 33 357 400
Table 5. Experimental results.
No. Metrics
Altered Dataset
(Landmarks + Original Images)
Original Dataset
(Without Landmarks)
1 Accuracy 91% 84.4%
2 Precision 0.92 0.84
3 Recall 0.95 0.90
4 F-Score 0.93 0.87
Compared to other studies, Fuentes et al. [64] used semantic metadata and visual cues
to train a CNN model for flood detection and achieved an average accuracy of 83.96%.
Feng et al. [71] obtained an accuracy of 87.5% using texture features and random forests
along with RGB images for flood mapping. Elkhrachy [79] obtained an accuracy of 84.4%
using an Analytical Hierarchical Process (AHP) to determine the relative impact weight of
flood causative factors. Tehrany et al. [80] used different kernel types with an SVM classifier
to develop a flood susceptibility mapping system integrated with GIS and achieved an
accuracy of 84.97%. Thus, the current study system shows superior results compared to
similar studies.
Table 6 compares the performance of the proposed system with recent methodologies
for flood detection. These results show that the proposed flood-mapping model outper-
forms the recently proposed techniques for flood detection. The only method that shows
results superior to the current CNN method is the deep learning neural network. How-
ever, the difference is minor (1%); hence the two methods can yield nearly similar results.
Hence based on its high performance, the proposed model is very promising for real-time
flood mapping.
Table 6. Comparison of flood mapping results of the current study with previous research.
No. Method
Accuracy
Result
Images in
Dataset
Location
1 Deep Learning Neural Network [81] 92% 1464 Lao Cai, Vietnam
2
Semantic metadata and visual data with
Convolutional Neural Network [82]
83.96% 6600 Misc (Flickr images)
3 Random Forest Classifier [71] 87.5% 5000 Yuyao, China
4 Analytical Hierarchical Process [79] 84.4% 519
Najran City, Kingdom
of Saudi Arabia
5 Support Vector Machines (SVM) [80] 84.97% 1000 Terengganu, Malaysia
6 Proposed Model (CNN with landmarks extraction) 91% 3000 Swat, Pakistan
Figure 9 depicts the dependence of the classification accuracy on the number of images
in the training set used in the current study. The x-axis and y-axis represent the number of
training samples in the dataset and the corresponding classification accuracies, respectively.
This graph indicates that the accuracy increases considerably with an increase in the
number of input samples in the training set. Hence, expanding the size of the training
dataset is one way to improve the performance of this model. If more training data, say
a million or even billions of samples, are used, a better performance will be achieved as
predicted by the graph. Adding the key distinguishing features to the training set helps
the network learn more about classifying the images. The idea is to increase the size of

the dataset by applying processes that imitate real-world variations. In this research, the
background or irrelevant features present in the images were cropped.
flooded class, the accuracy of a model predicting each image as “negative or non flooded”
senting a “no skill” prediction model.
0.1
42
57
61
75
83
92
0
10
20
30
40
50
60
70
80
90
100
0 500 1000 1500 2000 2500 3000 3500
Classification
Accuracy
(%)
Number of Training Samples
Figure 9. Graph showing classification performance with the number of samples in the training dataset.
Furthermore, the classification model was trained on a set of key features to be
analyzed while distinguishing the classes. This is synonymous with the tactics used by
a human while differentiating between a set of images manually. The performance of
a learning model depends deeply on the training dataset, so expanding the dataset in a
meaningful way is one way to yield a high classification accuracy. However, this approach
can be expensive and slows down the training, so the tradeoff should be carefully analyzed.
Figure 10 illustrates a Receiver Operator Characteristic (ROC) curve plotted using
true positive rate (TPR) (y-axis) against False Positive Rate (FPR) (x-axis) with a cut-point
of 0.5 probability. The area under the curve (AUC) represents one value that summarizes
the result of the ROC curve. A value of AUC close to one represents its good classification
performance, while AUC = 0.5 represents a bogus or no-skill model. For the CNN-based
model in the current study, the values of AUC, TPR, and FPR are 95.7, 88.0, and 86.7%,
respectively. Accuracy is the most simple and intuitive measure for highlighting classifi-
cation performance. However, there are certain conditions where accuracy may not be a
satisfactory measure. For example, if only one of a thousand test images belongs to the
flooded class, the accuracy of a model predicting each image as “negative or non-flooded”
will still be 99.99%. Unlike accuracy, ROC curves are not sensitive to the imbalance of
classes in the dataset. They depict the ability of a model to distinguish between classes. The
classification model in the example provided will have an ROC curve of 0.5, representing a
“no skill” prediction model.
The output of the flood detection model is tested with 50 new test images captured
from the case study area using the UAVs. Figure 11 shows sample test images used with
the current model. Firstly, orthorectification and noise filtering were applied to the input
image. The image at this stage was smoother, with certain noise arising from the shadows,
reflections, or other factors completely removed or reduced to some extent. The color
attributes of the image were preserved as the prediction model was trained for classifying
multispectral images. This image was then fed to the trained CNN model for predicting its
class. The classifier presented its output as shown in Figure 11, where the red highlighted
regions show flooding. Accordingly, the image was classified as “flooded”, and hence

responses can be instigated. The results clearly demonstrate the efficiency of the model for
detecting and mapping flooded regions. All major areas in the image showing floodwater
were identified. Some little patches of flooded regions may have been misclassified due to
shadows or reflections in the image. However, the image will still be classified as flooded,
which is the main objective of the CNN model in the current study. Furthermore, it must
be noted that whenever we utilize real time imagery for capturing the shore or collateral
regions, there are chances of achieving true negatives. Therefore, the edges of water were
not classified in Figure 11. Overall, all the significant flood-affected areas were detected by
the model.
. Accordingly, the image was classified as “flooded”
Figure 10. ROC Curve.
In summary, UAV-based image acquisition is a promising method to capture high-
resolution spatial images of the disaster site during emergencies such as floods. Such
images containing rich information about the ground objects boost the performance of
machine learning models. The associated image processing techniques result in increased
precision and accuracy of the landmark identification and helps instigate a proper emer-
gency response. UAVs are not restricted by their takeoff and landing conditions, making
them more flexible and safer to be used than man-driven aerial vehicles. They also fly at a
low altitude, enabling them to overcome the limitation of satellite imaging, such as block-
ing target view due to cloud cover or other barriers and accessing otherwise inaccessible
locations. Thus, such imagery has the edge over GIS-based satellite imagery.
Furthermore, the type of sensor used with the UAV determines the effectiveness of
the real-time data. By using a digital camera such as the Go-Pro® used in the current
study, high-quality images can be captured and quickly sent to the server based on the
ground station. These images can be seen on screen in real-time, allowing for relief workers
to make quick decisions in an emergency. This addresses the limitations of commercial
cameras where RGB images will only be accessible after the landing of the UAV and cannot
be viewed or analyzed in real-time hence delaying the response and hindering the rescue
operation. Thus, to provide the feature of real-time surveillance, a versatile camera such as
a Go-Pro ® should be used with the UAV.

Figure 11. Flood mapping results on input test images.

A deep learning approach based on CNN was used in the current study to classify
the images that showed highly promising results. By including landmark features with
the original dataset, a 5% improvement in overall accuracy was recorded. This is because
expanding the dataset results in more data to the model, leading to increased learning and
forming a better and improved prediction model. However, the results are highly depen-
dent on the content of images and features for training. Key landmarks must be extracted
and added to the training dataset to provide the model with the most relevant features. A
comparison with recent flood mapping techniques that used SVM, random forests, deep
learning, and AHP shows that the CNN model of the current study had superior perfor-
mance. Furthermore, it had comparable results to deep learning neural-network-based
image processing. Hence, deep learning approaches are highly recommended for flood
detection in aerial images captured through UAVs. This will help pave the way for smart
disaster management in the Industry 4.0 era and move towards the goals of smart cities
and regions.
The current study does not detect or assess the population to provide aid and potential
calculating damages to the population. Furthermore, it is limited to a maximum coverage
area through a limited number of UAVs. Due to limited battery timings of UAVs, i.e.,
30–45 min, the regions should be prioritized for capturing images and detecting floods.
To deal with this, swarm intelligence should be considered where UAVs can be made
smart/intelligent by a heuristic-based approach.
4. Conclusions
This study presented a hybrid model for landmarks-based feature selection and
CNN-based flood detection. The key landmarks (i.e., roads, bridges, and buildings) were
detected using supervised learning and added to the training dataset through swift re-
sponse instigation, which was further used for training the CNN model. The inclusion
of landmark features with the original RGB images significantly improved the model’s
performance. Moreover, using the CNN model on a large dataset based on aerial images
has shown superior results, which surpassed traditional machine learning classifiers. Thus,
successful implementation of UAV-based imaging for flood inundation mapping has been
demonstrated in the study, proving that UAVs are ideal for the facilitation of real-time
surveillance of inundated regions. Additionally, an accuracy of 92% was observed for
extraction of the inundated areas from images. An overall 5% improvement in accuracy
was observable when landmark features were included in the dataset. Overall, our model
demonstrated an improvement over previous techniques that used classifiers such as SVM
and random forests.
The outcomes of this research are directly aligned with the United Nations Interna-
tional Strategy for Disaster Reduction and Sendai Framework for Disaster Risk Reduction
2015–2030 that is aimed at providing practical solutions to rescue people in flood-affected
areas. Our study can help the disaster management authorities (i.e., PDMA) in Pakistan
to carry out post-disaster rescue services efficiently and quickly. This will help them to
supply aid and relief to the stranded people, thus saving lives and reducing the impacts of
disasters such as floods. This will pave the way for the adoption of smart technologies in
the Industry 4.0 era.
A limitation of the proposed model is that it highlights the flood-affected and sub-
merged areas using the two-dimensional images captured by UAV and cannot specify the
depth of floodwater in the region, which may be needed to analyze the extent of flood in-
tensity in a region. This issue can be overcome using technologies such as Digital Elevation
Model (DEM) and Light Detection and Ranging Equipment (LiDAR). In the future, the
accuracy of the system can be enhanced by expanding the dataset through the addition of
more landmarks and features. Additionally, the feature selection based on rivers, people,
and vehicles can also improve the quality of the dataset. Furthermore, other deep learning
approaches, including RNN and LSTM, can be explored to perform flood inundation map-
ping in addition to its detection. Similarly, the study can be merged with vehicle routing

techniques to plan rescue and emergency first aid responses in disaster-struck areas. This
will help the flood management, fire, and search and rescue authorities in any country.
Accordingly, the goals of modern smart cities and smart regions could be achieved.
Author Contributions: Conceptualization, H.S.M. and F.U.; methodology, H.S.M., F.U., S.Q. and
A.H.; software, H.S.M. and F.U.; validation, H.S.M., F.U., S.Q. and A.H.; formal analysis, H.S.M. and
F.U.; investigation, H.S.M. and F.U.; resources, H.S.M., F.U. and A.H.; data curation, H.S.M., F.U.,
S.Q. and A.H.; writing—original draft preparation, H.S.M. and F.U.; writing—review and editing,
H.S.M., F.U., S.Q. and A.H.; visualization, H.S.M. and F.U.; supervision, F.U. and A.H.; project
ad-ministration, H.S.M. and F.U.; funding acquisition, F.U. and A.H. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data is available with the first author and can be shared with
anyone upon reasonable request.
Conflicts of Interest: The authors declare no conflict of interest.
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