# Unmanned Aerial Vehicles for Wildland Fires

Moulay A. Akhloufi
Perception, Robotics, and Intelligent Machines Research Group (PRIME)
Department of Computer Science
Université de Moncton
Moncton, Canada, E1A 3E9
<EMAIL_ADDRESS>
Andy Couturier
Perception, Robotics, and Intelligent Machines Research Group (PRIME)
Department of Computer Science
Université de Moncton
Moncton, Canada, E1A 3E9
<EMAIL_ADDRESS>
Nicolás A. Castro
Department of Electronic Engineering
Universidad Técnica Federico Santa María
Valparaíso, Chile
<EMAIL_ADDRESS>

###### Abstract

Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing
the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.

_K_ eywords Unmanned Aerial Systems $\cdot$ UAS $\cdot$ Autonomous Systems
$\cdot$ Wildfire $\cdot$ Forest fires $\cdot$ Fire Detection

Cite as: Akhloufi, M.A.; Couturier, A.; Castro, N.A. "Unmanned Aerial Vehicles
for Wildland Fires: Sensing, Perception, Cooperation and Assistance". Drones.
2021; 5(1):15; pp 1-25. DOI:10.3390/drones5010015

A recent published version of this paper is available at:
https://doi.org/10.3390/drones5010015

##  1 Introduction

Wildland and forest fires are an important threat in rural and protected
areas. Their control and mitigation are difficult as they can quickly spread
to their surroundings, potentially burning large areas of land and getting
close to urban areas and cities. The occurrence of wildfires results into
substantial costs to economy, ecosystems and climate [1]. In western U.S.
alone, wildfires increased by 400% in the last decades [2, 3]. Kelly et al.[4]
reported an increase in intensity and frequency of wildfires compared to the
past 10,000 years. In 2018, 8.8 million acres were burned by more than
58,083,000 wildfires [5]. In Northern California, for example, the Camp Fire
caused 85 deaths. This fire was the most destructive in California history
burning 153,336 acres and destroying 18,733 structures. Losses were estimated
to $16.5 billion [2]. Experts estimate that wildfires will increase in the
coming years mainly because of climate change [6].

Two key elements are relevant in the effort to reduce the impact of a
wildfire. First, the time span between the start of a fire and the arrival of
firefighters which needs to be reduced to a minimum. Thus decreasing the
chances of fire to spread to unmanageable levels. Second, the severity
evaluation of the event and the monitoring of the emergency response which is
necessary to elaborate better fighting strategies. For these reasons, it is
important to have reliable and efficient systems for early stage fire
detection and monitoring.

Research efforts have been devoted to the development of systems that can cope
with this type of scenario. Remote sensing is essential to this field because
it prevents humans to be unnecessarily exposed to dangerous activities.
Satellite images have been used for the detection of active fires [7, 8] and
the reporting of fire risks [9]. Wireless sensor networks (WSNs) have also
been proposed for wildfires detection [10], monitoring [11] and risk
assessment [12]. Both types of systems have practical limitations. In the
first approach, depending on the image resolution, the data is averaged over
pixels making it difficult to detect small fires [13]. Furthermore, satellites
have limited ground coverage and it can take a significant amount of time
between two passes over the same point, making them unsuitable for continuous
monitoring. In the case of WSNs, the maintenance difficulties, the lack of
power independence and the fact that they are not scalable (due to their
static nature) severely reduce their coverage capacities [14]. Moreover, in
case of fire the sensors are destroyed leading to additional replacement
costs. Unmanned Aerial Systems (UAS) are more convenient for this task due to
their maneuverability, autonomy and easy deployment.

UAS have known impressive progress and are widely used today in various
fields. They become smaller, more affordable and also gained higher processing
capabilities turning them into reliable tools for remote sensing missions in
hostile environments. Research has shown their benefit in technical and
strategical assistance for surveillance, monitoring, and tasks related to
post-fire damage evaluation [15, 16, 17, 18, 19]. Additionally, UAS have
exhibited a positive economic balance in favor of their use in wildfire
emergencies [20, 21] making them both practical and economically reliable. UAS
can fly or hover over specific zones to retrieve relevant data with cameras or
other airborne sensors. This has motivated authors to propose frameworks and
techniques using UAS with the goal of delivering optimal fire detection,
coverage and firefighting. This paper presents a survey of the main literature
related to the use of UAS in the context of wildland and forest fires. We
review the fire assistance architectures proposed, the sensing modalities
used, the fire perception approaches ad datasets and the UAS coordination
strategies.

##  2 Fire assistance systems architecture

Remote sensing with aerial systems present multiple advantages in the context
of emergency assistance. Their high maneuverability allows them to dynamically
survey a region, follow a defined path or navigate autonomously. The wide
range of sensors that can be loaded onboard allows the capture of important
data which can be used to monitor a situation of interest and plan the
emergency response. The ability to remotely control an UAS helps reduce the
risk for humans and remove them from life-threatening tasks. The automation of
maneuvers, planning and other mission-related tasks through a computer
interface improves distant surveillance and monitoring. Advances in these
aspects have a direct impact on the firefighting resource management.

General purpose fire assistance systems can be divided into three main
components: sensing modalities and instruments, fire perception algorithms,
and coordination strategies between multiple UAS or with the Ground Control
Station (GCS). These components are designed to perform one or more tasks
related to fire emergencies. Within the reviewed works, the most developed
aspects are fire detection and monitoring. Fire prognosis and firefighting,
although they are present in some work, are less prominent. Fire detection and
monitoring is centered around recognition techniques, a field of research that
has seen significant advances in the last decades. Fire prognosis and fighting
has practical limitations that hinder research in these areas. Prognosis
requires complex mathematical models that must be fed with data that can be
difficult to acquire in real-time and in unknown environments. Fire fighting,
on the other hand, requires expensive combat equipment especially for large
wildland fires. Furthermore, close proximity with fires can pose a significant
risk for the vehicle integrity and lead to its loss. Research has been done to
design a UAS capable of fighting fires [22, 23] and more recently some drones
manufacturers have step in to tackle this problem [24], but more work remains
to be done for these vehicles to be affordable and technically viable.

UAS have different sizes, maneuverability, and endurance capacities. There is
a wide selection of aerial systems ranging from large UAS with long endurance
and high-processing capabilities to small UAS with short flight times and
limited processing capabilities [25, 26]. Large vehicles are expensive but
have higher payload and can carry more sensors and other instruments. In the
other hand, smaller vehicles are more affordable but with limited payload. The
instruments onboard the vehicles vary between the reviewed systems, but some
are essential to navigation and localization and therefore found in almost all
UAS. Global Navigation Satellite Systems (GNSS) and Inertial Navigation
Systems (INS) falls into this last category. Furthermore, almost all of the
vehicles have at least one kind of imagery sensor used for different purposes
including fire perception. Temperature sensors are also present in some of the
proposed fire assistance systems, but as shown in section 3, they are less
popular.

Sensor measurements are the input of the fire perception algorithms that
process the data to detect the presence of the fire. The processing can be
either onboard the UAS or in a computer located at a GCS. The fire perception
can also be performed by a human operator inspecting the data from a GCS. Most
of the developments are devoted to the automation of fire perception and the
optimization of the processing while preserving the accuracy. Computer vision
and machine learning techniques are widely used for this purpose.

The last component is the coordination strategy, it provides the framework for
the deployment of the flight mission. Surveillance missions are usually
planned beforehand and aim to search wide areas, prioritizing areas with
higher fire risks. The coordination strategy becomes essential during the
monitoring of a fire propagation, as it is necessary to adapt the flight plan
to the fire spread. This is even more relevant if there are multiple UAS
collaborating to the mission during a fire emergency. Multiple coordination
strategies were proposed. For example, one UAS could hover near a fire spot
and alert the rest of the fleet to proceed with fire confirmation [27]. More
complex planning is also possible, by requiring a consensus on the task to be
performed by each unit [28] or by flying in a specific formation around the
fire perimeter [29]. In both cases, a concrete description of the task and the
autonomous decision scheme must be defined. Section 7 gives more details about
the coordination strategies using single or multiple UAS.

Table 1 presents an overview of the main fire assistance systems. The first
column contains the reference of the work. The second column presents the
sensing modalities used to perform fire perception. Some of the systems do not
specify sensing instruments and the authors assume that the necessary
instruments are available. The third column describes the tasks that are
addressed in the corresponding research. The possible tasks are detection,
monitoring, diagnosis, prognosis and fighting. The fourth column gives the
used coordination strategy, specifying if it is a man-controlled vehicle, a
single UAS, or a fleet of vehicles. The last column describes the validation
methodology used in each work. It can be either by system overview,
simulation, laboratory tests (near practical), or real flight tests
(practical).

Table 1: Characteristics of fire assistance systems. Authors |  Sensing mode |  Tasks |  Coordination |  Validation
---|---|---|---|---
Casbeer et al.[29] |  IR |  Monitoring |  Centralized team |  Simulation
Bradley et al.[30] |  IR |  Detection and monitoring |  Single UAS |  Near practical
Hinkley et al.[17] |  IR |  Monitoring |  Single man-controlled UAS |  Practical
Kumar et al.[31] |  IR |  Monitoring and fighting |  Collaborative team |  Simulation
Pham et al.[32] |  IR |  Monitoring |  Collaborative team |  Simulation
Yuan et al.[33] |  IR |  Detection |  Centralized team |  Simulation
Belbachir et al.[34] |  Temperature |  Detection |  Multiple UASs. No collaboration |  Simulation
Lin et al.[35] |  Temperature |  Monitoring |  Collaborative team |  Simulation
Yuan et al.[36, 37, 38, 39] |  Visual |  Detection |  Single UAS |  Near practical
Sun et al.[40] |  Visual |  Detection and Monitoring |  Single man-controlled UAS |  Validation
Merino et al.[41, 42], Martinez-de-Dios et al.[27] |  IR and Visual |  Detection and Monitoring |  Centralized team |  Practical
Martinez-de-Dios et al.[43] |  IR and Visual |  Monitoring and diagnosis |  Single UAS |  Practical
Pastor et al.[44] |  IR and Visual |  Detection and Monitoring |  Single UAS |  System overview
Merino et al.[45, 46] |  IR and/or Visual |  Detection and Monitoring |  Collaborative |  Practical
Martins et al.[47] |  NIR and Visual |  Detection |  Single UAS |  Simulation
Ambrosia et al.[16] |  Multispectral camera |  Detection and diagnosis |  Single man-controlled UAS |  Practical
Sujit et al.[48] |  Not specified |  Monitoring |  Collaborative team |  Simulation
Alexis et al.[49] |  Not specified |  Monitoring |  Collaborative team |  Simulation
Karma et al.[50] |  Not specified |  Monitoring |  Single man-controlled UAS |  Practical
Ghamry et al.[51, 52] |  Not specified |  Detection and monitoring |  Collaborative team |  Simulation
Ghamry et al.[28] |  Not specified |  Fighting |  Centralized team |  Simulation

##  3 Sensing Instruments

Sensors provide the necessary data for navigation and for firefighting
monitoring and assistance. In outdoor scenarios, GNSS and INS provide real-
time UAS localization. They are also used to georeference the captured images
thus allowing geographical mapping of fires. Fires have specific signatures
that can be composed of different elements such as heat, flickering, motion,
brightness, smoke and bio-product [53]. These elements can be measured using
suitable sensing instruments. Cameras are the sensing instruments that offer
the most versatility. Visual and infrared (IR) sensors onboard UAS can be used
to capture a rich amount of information. Table 2, gives the main camera
spectral bands used in fire studies.

Table 2: Visual and IR electromagnetic spectrum. Spectral band |  Wavelength $(\mu m)$
---|---
Visible |  0.4 - 0.75
Near Infrared (NIR) |  0.75 - 1.4
Short Wave IR (SWIR) |  1.4 - 3
Mid Wave IR (MWIR) |  3 - 8
Long Wave IR (LWIR) |  8 - 15

Visual, IR and other sensors proposed in past work are presented in the
following.

###  3.1 Infrared spectrum

The infrared electromagnetic spectrum wavelengths ranges from $0.7\mu m$ to
$1000\mu m$ (Table 2). Specialized sensors are available to capture images in
different sub-bands of the IR spectrum.

All matter emits an electromagnetic radiation in some wavelength band which is
proportional to the fourth power of the object absolute temperature. At room
temperature, the radiation peak is located within the thermal infrared band.
For a wildfire, temperatures can top the $1000$$\mathrm{\SIUnitSymbolDegree}$C
( $1800$$\mathrm{\SIUnitSymbolDegree}$F), leading to a peak radiation in the
mid-wave infrared (MWIR) sub-band [54, 53]. Therefore, a sensor operating in
the MWIR is best suited for fire perception. However, until recently [55] the
form factor of MWIR sensors and their cost limited their use in low-cost small
and medium UAS.

To overcome these restrictions in smaller aerial vehicles, fire detection
systems are using mostly NIR, SWIR or LWIR sensors. The use of these sub-bands
is possible due to the fact that the higher temperature shifts the
distribution of the object radiation to shorter wavelengths. This property can
be seen in figure 1 illustrating how the radiance changes as a function of the
temperature. It is not absolutely necessary to detect fire at the peak as the
effect is also clearly visible in other close spectral bands. However, a
disadvantage of using NIR and SWIR is that objects under sunlight are often
reflecting radiation in these sub-bands creating false positives. Still hot
fire spots remain detectable but the fire contrast is reduced during day time
flights [53].

Regardless of their characteristics, IR sensors are the most commonly used
sensors in fire assistance systems due to their ability to detect heat. In
[30], [29], [17], [31] and [33] the authors proposed methods based solely on
IR spectrum (Table 1).

Figure 1: Black body radiance at different wavelengths. Each curve represents
a specific temperature in Kelvin, starting at 200[K] at the bottom and up to
2000[K] at the top. Visible light and IR wavelengths are highlighted at the
left and right, respectively.

###  3.2 Visible spectrum

Visible spectrum cameras are widely available and commonly used in various
applications. They come in a wide variety of resolutions, form-factors and
cost. Their versatility offers a valuable alternative in wildfires research
from both technical and commercial perspectives. Moreover, the ever continuing
reduction in visible cameras size and weight makes them perfect candidates for
UAS.

Data provided by these sensors are images in grayscale or RGB format. This
allows the development of computer vision techniques using color, shape,
temporal changes and motion in images or a sequence of images. Some of the
vision-based techniques are presented in section 4. Although, they are
versatile and widely available, visible light sensors must be carefully
selected for night time operations as some sensors perform poorly in low light
conditions. Despite some of their limitations, they equip almost all UAS and
make them good candidates for wildland fires study. Authors in [36], [37],
[38], [39], and [40] proposed systems that rely only on the use of visible
spectrum cameras (Table 1).

###  3.3 Multispectral cameras

Using each spectral band alone comes with its limitations. To tackle these
limitations authors propose the use of multiple cameras and combine multiple
spectrum. This allows the use of data fusion techniques to increase the
accuracy of fire detection in complex situations and under different lighting
conditions. Esposito et al.[56] developed a multispectral camera operating in
the LWIR, NIR and visible spectrum mounted on a UAS. In a NASA Dryden’s
project, Ikhana UAS [16, 57], a Predator B unmanned aircraft system adapted
for civilian missions, was built to carry a multispectral sensor that operates
in 16 different bands from visible to LWIR spectrum. Despite their interesting
characteristics, in both cases, the weight of these combined sensors limited
their implementation to large airborne platforms only. To address this
problem, other alternatives combine smaller sensors such as visible spectrum
sensors and IR sensors. In [27], the authors used this approach to capture and
project the IR data onto visible images. This generated a superposition of the
data leading to pixels being represented with four intensity values red,
green, blue and IR. The authors reports improvements in fire detection with
mixed segmentation techniques that make use of the four-channel values.

###  3.4 Other sensors

Various sensors other than cameras have been proposed to detect and confirm
the presence of fires. Some authors proposed the use of chemical sensors which
can detect concentrations of hazardous compounds [50]. Spectrometry measures
can also be used to detect the characteristics of burned vegetation and
confirm a fire [53]. Again, in both cases, the size of the sensors limited
their use. Temperature sensors have also been used by Belbachir et al.[34] and
Lin et al.[35] to generate heat maps and detect/locate fires.

The remaining of the reviewed literature will be based on the used of cameras
to detect, monitor and measure fires.

##  4 Fire perception

Research has shown the effectiveness of UAS as a remote sensing tool in
firefighting scenarios [16, 50, 17]. They are very useful even in simple tasks
such as observing the fire from a static position and streaming the video
sequence to human operators. This simple use case already allows firefighters
to have an aerial view of the spreading fire and plan containment measures.
However, single man-controlled UAS, even if they are useful for small
emergencies, do not scale up in large scenarios. Automation of detection and
monitoring of fires can help deliver an optimal coverage of the fire area with
the help of multiple UAS. Furthermore, the gathered data can be processed to
analyze the fire, estimate its Rate of Spread (RoS) [58], volume [43] or
perform post fire damage evaluation [16].

To perform fire-related tasks autonomously, systems must address different
tasks such as fire location estimation and path planning. They also need to
dynamically adapt to the evolving emergency. For that purpose, sensor data are
processed to detect fire and extract fire-related measures. For fire
detection, authors usually extract fire-like pixels based on color cues or IR
intensities. Monitoring tasks usually require further analysis to estimate the
fire perimeter or burned areas. Computed measures can be used as input to fire
models to estimate the fire propagation over time. This section reviews some
fire detection and segmentation techniques found in the literature.

###  4.1 Fire segmentation

Fire segmentation is the process of extracting pixels corresponding to fire in
an image. The criteria by which a pixel is selected vary from one method to
another. The selection criteria are also the main factor affecting the
accuracy of the detection. In general, fire segmentation uses the pixel values
of a visual spectrum image (e.g. color space segmentation) or the intensities
of an IR image. Motion segmentation can also be used to extract the fire using
its mouvement over a sequence of images.

####  4.1.1 Color segmentation

Images are built of pixel units that can have different encoding (e.g.
grayscale, color). In color images, pixels are composed of three values in the
red, green and blue channels (RGB). Other color spaces are also possible such
as YCbCr, HSI, CIELAB, YUV, etc. [59]. In IR images the pixels have a one
channel value representing temperature (MWIR and LWIR) or reflectance (NIR,
SWIR).

In the COMETS project [27, 41, 42], the authors use a lookup table with fire-
like colors (RGB values) that were extracted from a learned fire color
histogram. The image pixels are compared to the table and the values that are
not found are considered as non-fire. A non-calibrated LWIR camera is used to
capture qualitative images with radiation values relative to the overall
temperature of the objects in the scene. The heat peak observed in the
resulting image depends on the current scenario. A training process was
carried out to learn the thresholds to be applied to the IR images for
binarization. Images with and without fires were considered as well as
different lighting conditions and backgrounds. This permitted the selection of
the appropriate threshold to apply during deployment in known conditions.
Ambrosia et al.[16] selected fixed thresholds for each IR spectral band. They
also varied the bands used for day and night missions. During night time, the
MWIR and LWIR bands were used and during the day the NIR band was added. The
results shows that fixed threshold adapt poorly to unexpected conditions but
can be tuned to perform better in known environments.

In [33, 36, 37, 38, 39], the authors use color space segmentation. The images
are converted from RGB to the CIELAB color space prior to further processing.
Sun et al.[40] propose the use of YCbCr color space. In both cases, a set of
rules were developed based on empirical calculations performed on captured
fire images. For example, the authors in [40] considered pixels as fires if
their values followed the following rules: $Y>Cb$, $Cr>Cb$, $Y>Y_{mean}$,
$Cb<Cb_{mean}$ and $Cr>Cr_{mean}$. The mean sub-index indicates the channel
mean value of the corresponding image. Otsu thresholding technique [60] was
used in [33] to segment IR images.

Color value rule-based segmentation approaches are computationally efficient,
but lack robustness during detection. Results show that objects with a color
similar to fire are often mislabeled as fire and trigger false alarms. A
combination of rules in different color spaces and the addition of IR can
increase the detection accuracy. More complex algorithms that are time and
space aware have also been shown to increase the accuracy of the fire
detection [61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]. But the majority of
them have not been integrated with UAS.

In recent years, deep learning algorithms have shown impressive results in
different areas. In UAS, past work using deep convolutional neural networks
(CNN) dealt mainly with fire detection [72, 73, 74]. Deep fire segmentation
techniques proposed recently has shown the potential of developing an
efficient wildfire segmentation system [75]. The used dataset in this last
work included some aerial forest fires images [76]. Deep segmentation of
wildland fires is still lacking in UAS applications.

####  4.1.2 Motion segmentation

Fire segmentation using static images help reduce the search space, but often
objects with a similar color to fire can be detected and lead to false
positives. In [33, 36, 37, 38, 39] and [40], the authors proposed the use of
Lukas-Kanade optical flow algorithm [77] to consider fire movements. With the
detection of corresponding feature points in consecutive image frames, a
relative motion vector can be computed. The mean motion vector matches the UAS
motion except for moving objects in the ground. Fire flames are among those
objects because of their random motion. By detecting feature points within
regions with both random movements and fire-like colors, the fire can be
confirmed and the false alarm rate reduced.

###  4.2 Fire detection and features extraction

The data fed to a detection system are analyzed in order to find patterns that
confirm the occurrence of an event. Patterns are recognized by computing
different features which can be strong or weak signatures for a specific
application. In the case of fire detection with UAS, the most popular features
are color, brightness and motion. Research focusing on fire detection
considers the fusion of more features to obtain better results in the
classification stage. These features can be categorized by the level of
abstraction at which they are extracted: pixel, spatial and temporal.

Color cues are widely used at the first step to extract fire-like pixels. This
reduces the search space for further processing with more computationally
expensive detection algorithms. For example, the RGB mean values of a Region
of Interest (RoI) and the absolute color differences ($|R-B|$, $|R-G|$,
$|B-G|$) can be thresholded [78] or used to train a classification algorithm
[79]. In the work of [80], the authors further added the intensity mean, the
variance and the entropy values of the ROI to the feature vector. Other
features used in the literature include color histograms of ROI [64] and color
spatial dispersion measures [62].

After the detection of the ROI, other features can be extracted. Some authors
consider spatial characteristics to determine the fire perimeter complexity by
relating the convex hull to the perimeter ratio and the bounding rectangle to
perimeter ratio [81]. The distance between the blob centroid position within
to bounding box has also been considered in this work.

Texture is another spatial characteristic often used for fire detection. The
main texture descriptors proposed for this task are Local Binary Patterns
(LBP) [82, 83, 84, 85] and Speeded Up Robust Features (SURF) [67, 64]. These
operators characterize local spatial changes in intensity or color in an image
and return a feature vector that can be used as input for classification. SURF
[86] is computationally expensive but allows for scale and rotation invariant
matching. LBP [87] needs less processing power and extract the mean relation
between pixels in a small area using the 8 neighbors of a pixel. Some authors
[88, 89] also use the Harris corner detector [90], which is a computationally
efficient feature point extractor.

The features reviewed above are extracted from single images. When a video
sequence is available, the temporal variation in color, shape and position of
some blobs can be extracted. In [91], the fire blob shape variation is
computed by a skewness measure of the distance from the perimeter points to
the blob’s centroid. Foggia et al.[92] measured shape changes by computing the
perimeter to area ratio variation over multiple frames. The authors also
detected the blob movements by matching them in contiguous frames and to
compute the centroid displacement. Fire tend to moves slowly upwards, thus
blobs that do not comply with this rule can be discarded [91, 61, 93]. The
centroid displacement can also be an input for further classification [81, 61,
94]. A similarity evaluation is employed in [81]. They measure the rate of
change of overlapping area of blobs in contiguous frames. This gives a
practical representation of the speed at which the region of interest is
moving and if it is growing or decreasing in size. Fire flickering can also be
identified by considering specific measures such as intensity variation [95],
the number of high-pass zero crossing in the wavelet transform [96] or the
number of changes from fire to non-fire pixels inside a region [97]. Wang et
al.[98] implemented a long-term movement gradient histogram, which accumulates
the motion changes. The histogram is fitted to a curve which is used to
evaluate if the area corresponds to a fire or not. Kim et al.[99] proposed a
Brownian motion estimator that measures the correlation of two random vectors
[100]. The vectors are composed of channel values, the first intensity
derivative and the second intensity derivative. Therefore, the Brownian motion
estimator describes the dynamic dependence between a series of regions across
multiple frames. Temporal features consider a time window for the fire
evaluation. Then, some empirical criteria are established to determine the
optimal thresholds and duration of the events in order to trigger a fire
alarm.

Among the features described so far, there are some features that are more
oriented towards fire detection. Features such as color, blob centroid
displacement and flickering are some of the most popular. Some novel
approaches such as the Brownian correlation or the histogram of gradients have
been less explored but are nevertheless interesting. A comparison of these
different features and an evaluation of which one has a greater impact on the
fire detection accuracy and false positive rate would be very useful.
Unfortunately, to our knowledge, such a comprehensive comparison has not been
yet published. However, as most of these features are not computationally
expensive, ensembling the features can improve the performance and reduce the
false detection rate. Table 3 gives an overview of the features used depending
on the input.

Table 3: Image input and extracted features. Input |  Statistical measures |  Spatial features |  Temporal features
---|---|---|---
Color, IR and radiance images |  Mean value, mean difference, color histogram, variance and entropy. |  LBP, SURF, shape, convex hull to the perimeter rate, bounding box to the perimeter rate. |  Shape and intensity variations, centroid displacement, ROI overlapping, fire to non-fire transitions, movement gradient histogram, and Brownian correlation.
Wavelet transform |  Mean energy content. |  Mean blob energy content. |  Diagonal filter difference. High-pass filter zero-crossing of wavelet transform on area variation.

###  4.3 Considerations in UAS applications

Additional features can improve the fire detection. Features that are obtained
by temporal analysis evaluate the difference between contiguous frames. In
simple scenarios, where the camera is static and the background is not
complex, frame subtractions can help detect moving pixels. In the presence of
complex and dynamic backgrounds, Gaussian mixture models and other
sophisticated background modeling techniques can be considered. However, the
video streams from UAS have fast motions and no classical background
subtraction method would give satisfying results because of the assumption of
a static camera. Even in a situation where the UAS is hovering over a fixed
position, the images are still affected by wind turbulence and vibrations.
Therefore, in order to be able to apply these motion analysis techniques, it
is necessary to consider image alignment and video stabilization. The usual
approach is to find strong feature points that can be tracked over a sequence
of frames. Merino et al.[45], in their fire assistance system, used a motion
estimation approach based on feature points matching known as sparse motion
field. From the matched points, they estimate a homography matrix that maps
the pixels in an image with the pixels in the previous frame. This allows
mapping every image to a common coordinated frame for alignement. SURF [86]
and ORB [101] are two feature point methods that were used for extracting
salient features prior to image alignment. To our knowledge, the impacts and
the benefits of the image alignment has not been yet addressed in the
literature relating to fire and smoke detection but some researchers such as
[45] consider it important for their fire assistance system to work properly.

##  5 Wildland fire datasets

A large number of fire detection approaches use a classification method that
relies on learning algorithms. The main challenges of machine learning is to
build or to find a large enough dataset with low bias. Such a dataset should
contain positive examples with high feature variance and negative examples
consisting of standard and challenging samples. Deep learning techniques need
even larger datasets for training. Data augmentation techniques can help in
this regard but it requires a sufficiently large dataset to start.

Well-developed research fields such as face or object recognition have already
large datasets that have been built and vetted by the community. These
datasets are considered suitable for the development and benchmarking of the
new algorithms in their respective fields. In the case of fire detection, no
such widely employed dataset is available yet. But some effort has been made
toward this direction. Steffens et al.[102] captured a set of 24 videos from
hand-held cameras and robots mounted cameras. The ground truth was defined by
bounding boxes around the fire. Foggia et al.[92] compiled a collection of 29
videos of fire and smoke but did not provide ground truth data. In [83], the
authors gathered around 180 fire images to test their BowFire algorithm and
made the dataset available with manually segmented binary images representing
the ground truth for the fire area. However, the main problem with these
datasets is the lack of wildfire samples. This could be problematic for the
development of a fire detection module for wildfire assistance systems. Aerial
fire samples in the form of videos are also necessary for the development of
UAS based systems. In [76], the authors collected images and videos to build
the Corsican fire database. This dataset is specifically built for wildfires
and forest fires. It also contains multimodal images (visual and NIR images)
of fires. The images have their corresponding binary masks representing the
ground truth (segmented fire area). Other information are also available such
as smoke presence, location of capture, type of vegetation, dominant color,
fire texture level, etc. The dataset contains some aerial forest fire views,
but their number is limited. The wildfire UAS research is still lacking a
dataset that can help improve the development of the algorithms needed in a
wildland fire assistance system. Table 4 contains a brief description of the
main fire research datasets.

Table 4: Fire datasets. Dataset |  Description |  Wildland fires |  Aerial footage |  Annotations
---|---|---|---|---
FURG [102] |  14,397 fire frames in 24 videos from static and moving cameras. |  No |  No |  Fire bounding boxes
BowFire [83] |  186 fire and non-fire images. |  No |  No |  Fire masks
Corsican Fire DB [76] |  500 RGB and 100 multimodal images. |  All |  Few |  Fire masks
VisiFire [92] |  14 fire videos, 15 smoke videos, 2 videos containing fire-like objects. |  17 videos |  7 videos |  No

##  6 Fire geolocation and fire modeling

When a wildland fire is detected, the vehicle must alert the GCS and send the
fire location to deploy the firefighting resources. With a camera located on
the bottom of a UAS looking downward, the position of the fire is easy to
compute using the UAS GPS position. For a camera located on the front side of
the UAS, we need to project the camera plane onto a global coordinate system
using homography. This transformation maps pixel coordinates to the ground
plane. This approach performs well when the UAS pose estimation is reliable
and when the ground is mostly planar. Some difficulties arise in the presence
of uneven surfaces. Some authors [27, 16, 44, 45, 46] have circumvented this
limitation by exploiting a previously known Digital Elevation Map (DEM) of the
surveyed area. DEM allows for the estimation of the location from where a ray
corresponding to a fire pixel originated and thus improves the fire location
estimation. DEMs can induce some errors. To reduce these errors, a UAS fleet
looking at the same hotspot can first detect the fire and then use different
views of the UAS to refine the estimations [27].

Fire models are used to adjust the measurements, to find fire boundaries and
to describe the fire behavior. Simple models use an elliptic shape fitted to
fire, where each ellipse axis increases at some given rate. Other models can
be very complex depending on the considered variables. Such models try to
estimate the rate of spread (ROS) of the fire based on wind speed and
direction, terrain slopes, vegetation density, weather and other variables.

Ghamry et al.[58, 51, 52] applied an elliptical model to estimate the fire
perimeter. Here, the rate at which the axis grows depends on the direction
towards which the wind blows and its speed. The authors assumed that the fire
spread more quickly along that direction. The estimated perimeter is then used
to define a UAS team formation.

More complex dynamic models have been used for simulation purposes. In [31,
32, 35], the authors used the FARSITE (Fire Area Simulation) tool to test
their coordination strategies under various scenarios. The proposed models are
not suitable for real-time fire estimation because their complexity
significantly increases the computation time.

Some authors use different approaches to model and characterize the fire. For
example, Martinez-de-Dios et al.[43] use multiple images to extract geometric
features from the fire such as the base perimeter, the height and the
inclination. The extraction is performed using computer vision techniques
(e.g. image segmentation).

Bradley et al.[30] divide the environment into cells and assigns a fire
probability is assigned to each cell using IR images. This method takes into
account the uncertainty in the position of the UAS and therefore applies a
Gaussian weighting scheme to the probabilities. The authors then apply a
Sequential Monte Carlo (SMC) method to compose a Georeferenced Uncertainty
Mosaic (GUM) which is then used to locate the fire. Belbachir et al.[34] model
the fire as a static cone of heat sourcing from the fire center and
dissipating with an altitude and a horizontal distance. Based on this
assumption, they construct a grid of fire probabilities with the temperature
measures. The fire is detected when the probabilities are above a defined
threshold. Lin et al.[35] also generates an occupancy grid with associated
temperatures. They also compute the gradient of the grid and estimate the fire
center, ROS, and perimeter.

##  7 Coordination strategy

Coordination strategy is important when deploying autonomous UAS. component.
It deals with communication, task allocation and planning procedures. Based on
the communication links established during the mission, three main schemes can
be distinguished. First, for a single vehicle, the only possible connection is
with the GCS. For multiple UAS, the path planning and task allocation need to
be optimally resolved. The usual approach is to centralize the heavy
processing in the GCS and only allow the UAS to communicate with it but not
between each other. The last scheme tackles the problem of coordinating
multiple entities in a distributed manner. Each vehicle can connect with the
GCS and other UAS, allowing for distributed decision-making at a global scale
(GCS) and locally (each UAS).

###  7.1 Single UAS

Single UAS, either large airships or small aerial systems, have been evaluated
for the viability of using UAS for wildfire surveillance and monitoring. The
Ikhana UAS [16] was deployed in western US between 2006 and 2010. It was a
single large and high endurance vehicle with powerful sensory systems for
autonomous fire detection. The decision strategy and the path planning were
performed by human operators. Pastor et al.[44] proposed a semi-autonomous
system in which a single UAS would sweep a rectangular area, locate hotspots
and then return to a nearby ground station. A human could control the UAS and
order it to stay over the hotspot location to confirm visually if it
corresponds to a real fire or not. Martins et al.[47] used an entirely
autonomous navigation system where the UAS only received waypoints from where
to start surveillance. When a hotspot is detected, the UAS approaches the
source, hover over the target and confirm the fire. The experimental tests
showed very interesting results for fire detection and monitoring tasks.

However, the use of a single UAS is limited when dealing with large-scale
wildfires. For this reason, the majority of recent developments deal with
team-based systems that help increase the area coverage.

###  7.2 Centralized team

The addition of more UAS to the mission increases the area covered by the
systems. In a centralized team strategy, all UAS are coordinated by a single
GCS. This scheme can lead to a more accurate fire georeferencing and reduce
false alarms. In [27], the authors follow this approach. They merge data from
multiple UAS to correct and reduce the uncertainty of fire georeferencing.
After a fire is detected by a unit, nearby vehicles are sent to the same
region to perform a fire confirmation. Belbachir et al.[34] propose a greedy
algorithm for fire detection using a probability grid. For this purpose, each
UAS select in a greedy way, the path that provides more information. The UAS
visit cells that have not yet been checked and which are within the direction
where the temperature increases. Kumar et al.[31] propose a coordination
protocol for monitoring and firefighting. In the first step, the planned path
of each UAS is optimized to minimize the distance to a detected fire
perimeter. The second step optimizes the path of UAS carrying fire
suppressants by minimizing the distance to the fire center. The last two
methods have not been tested in a practical situation, but offer an insight on
how a centralized team could work. The other advantage of centralized
processing is that smaller and more affordable UAS can be used as they do not
require high-processing power.

###  7.3 Collaborative team

In a distributed fleet, UAS communicate together and with the GCS, allowing
the integration of more complex strategies. The system is able to perform more
tasks in an autonomous manner and even to cover larger areas by using some UAS
as communication relays. However, the added complexity imposes new challenges.
Distributed coordination algorithms need to be developed and implemented. In
the literature, these systems were used for optimal fire perimeter
surveillance and task allocation.

In [49], the authors describe a UAS rendezvous based consensus algorithm which
aims to equally distribute the path length of the UAS around the fire
perimeter. UAS depart in pairs and in opposite directions around the fire
perimeter. They set rendezvous locations where they share knowledge about the
traveled paths, the current state of the fire perimeter and other units
encountered. If the update shows that the fire perimeter has evolved, then
each UAS will select new rendezvous locations in such a way that the distance
traveled by each of the UAS is almost the same. The authors have shown through
simulation that the algorithm converge and the recomputing of rendezvous
points allows efficient adaptation of the UAS formation to an evolving fire
perimeter.

The optimal distribution of UAS around a fire perimeter has also been studied
by in [29]. They demonstrated that in order to reduce the latency in
communication with the GCS, the UAS must depart in pairs, travel in opposite
directions and be evenly spaced around the perimeter. To achieve optimal
perimeter tracking, they designed a control loop to keep half of the bottom-
facing IR camera over hotspot pixels and the other half over non-fire area.
Ghamry et al.[51] also distribute the UAS uniformly around the fire perimeter
modeled with an elliptical form. This allows the UAS to keep their paths at
even angles around the estimated fire center. Sujit et al.[48] proposed the
same approach to perform fire perimeter monitoring, but in [52], the authors
added the ability to restructure the formation if a UAS is damaged or has to
leave for refueling. To achieve this fault-tolerant capacity when a UAS needs
to leave the formation, all its communications are stoped. Other vehicles will
notice the missing UAS and will start performing the reformation process. In
this system, prior to the monitoring task, the fleet flies in a leader-
follower formation where the leader gets a predetermined path plan and the
rest follow it at specific distances and angles. In the work of Lin et
al.[35], UAS are directed to fly uniformly around an estimated fire center. In
this approach Kalman filter is used to estimate the fire contour and the fire
center movements, allowing the UAS to fly and adapt their formation
accordingly.

For monitoring, Pham et al.[32] propose a collaborative system in which UAS
are sent to monitor a fire and optimally cover the fire area. This formation
is achieved by detecting neighboring UAS and reducing camera view overlaps
while considering the location of the fire front. A simple way to model this
behavior is with the application of a force field-based algorithm that
simulates the attraction of a UAS by the fire front and its repeal from the
other UAS. The attraction and repulsion forces are adapted by considering the
fire front confidence and the estimated field of view of each UAS. One problem
with this approach is that the visibility reduction induced by smoke is not
taken into account which can put the vehicle in a dangerous situation.

Another coordination strategy was proposed to perform optimal task allocation
within a team of UAS. The tasks can be surveillance, monitoring or
firefighting. In [28], the authors propose an auction-based firefighting
coordination algorithm. In this algorithm, a fire is first detected and then
the UAS must coordinate themselves to act upon each known fire spot. To
achieve this task, each vehicle generates a bid valued by a cost function of
its distance to the fire spot. In this manner, the UAS with the best offer for
the task will be assigned to it. Sujit et al.[48] also proposed a similar
auction-based collaboration algorithm but with the ability to consider a
minimal number of UAS to watch each hotspot.

##  8 Cooperative autonomous systems for wildfires

UAS can play an important role in the detection and monitoring of large
wildfires. Multiple UAS can collaborate in the extraction of important data
and improve firefighting strategies. Moreover, aerial vehicles can cooperate
with unmanned ground vehicles (UGV) in operational firefighting scenarios.

One type of cooperation can consist in the use of UGV to carry small short
endurance UAS to detected fire areas and be used as refueling stations. Ghamry
et al.[58] propose such a system, where a coordinated leader-follower strategy
is used. UAS are carried by UGV to a desired location and deployed to explore
preassigned areas. If a UAS detects a fire, an alert is sent to the leading
UGV and to the rest of the fleet. The leader computes new optimal trajectories
for the UAS in order to monitor the fire perimeter. In [103], the authors
present another firefighting collaborative UAS-UGV strategy. A hierarchical
UAS-UGV system composed of a large leading airship and cooperative UAS and UGV
is proposed. When a fire is detected, the vehicles are deployed for fire
monitoring. In this scenario, UAS and UGV are supposed to have the capacity to
carry water and combat fire. The UAS are deployed in an optimal flying
formation over the fire front area. UGV are sent to prevent the fire
propagation and limit its spread using water and fire retardants. Auction
based algorithms are implemented to allocate the tasks to each vehicle.
Viguria et al.[104] also propose the use of task allocation by an auction-
based algorithm. In their framework, the vehicles can perform various tasks
such as surveillance, monitoring, fire extinguishing, transportation and acts
as a communication relay. A human or the GCS can generate a list of tasks that
need to be fulfilled. Each robot sends a bid for each task and the one with
the best offer wins and can proceed to execute the task. The offers are based
on specific cost functions for each task that consider the vehicle distance,
fuel level and capabilities.

In [105], the authors propose a multimodal UAS-UGV cooperative framework for
large-scale wildfire detection and segmentation, 3D modeling and strategical
firefighting. The framework is composed of multiple UAS and UGV operating in a
team-based cooperative mode. Figure 2 illustrates the proposed framework. The
vehicles are equipped with a multimodal stereo-vision system such as the ones
developed for ground-based fire detection and 3D modeling [106, 107, 108,
109]. The stereo system includes multispectral cameras operating in the
Visible and NIR spectrum for efficient fire detection and segmentation. Each
stereo system provides an approximate 3D model of the fire. The models
captured using multiple views are registered using inertial measurements,
geospatial data and the extracted features using computer vision to build the
propagating fire front 3D model [106, 107, 108] . Based on the 3D model of the
fire, the UAS and UGV can be positioned strategically to capture complementary
views of the fire front. This 3D model is tracked over time to compute
different three-dimensional fire characteristics such as height, width,
inclination, perimeter, area, volume, ROS and their evolution over time. The
extracted three-dimensional fire characteristics can be fed to a mathematical
fire propagation model to predict the fire behavior and spread over time. The
obtained data makes it possible to alert and inform about the risk levels in
the surrounding areas. The predicted fire propagation can be mapped and used
in an operational firefighting strategy. Furthermore, this information can be
used for the optimal deployment of UAS and UGV in the field. This type of
framework can be combined with other firefighting resources such as
firefighters, aerial firefighting aircraft and future fire extinguisher
drones.

(a) The acquisition of 3D fire front data; UAS and UGV equipped with
multimodal stereo cameras, IMU, and GPS.

(b) The modeling and prediction; Registered 3D fire front, weather,
topographic and vegetation data are used to predict the fire propagation and
map it.

Figure 2: UAS-UGV multimodal framework for wildfires assistance [105].

##  9 Conclusion

This paper presents a survey of different approaches for the development of
UAS fire assistance systems. Sensing instruments, fire perception algorithms
and different coordination strategies have been described.

UAS can play an important role in the fight against wildfires in large areas.
With the decrease in their prices and their wider commercial availability, new
applications in this field will emerge. However, some limitations remain such
as autonomy, reliability and fault tolerance. Security is also a concern, with
the risk associated to having UAS flying over firefighters or close to
aircraft carrying water and fire retardants. Nevertheless, the benefits of
using UAS are significant and this could lead to innovations to solve these
problems.

In the perception side, most of the developed techniques rely on classical
computer vision algorithms. Still, we are witnessing the emergence of some
work in the field of deep learning in recent years, especially for fire
detection. We presented some of the available datasets containing wildfire
images that can be used for developing the computer vision algorithms.
However, a small number contain aerial views. In addition, the lack of a large
dataset limits the development of advanced deep learning algorithms. Such a
dataset can be an important contribution to the field and can serve as a
baseline for comparing multiple approaches.

We also introduced the frameworks proposing cooperative autonomous systems
where both aerial and ground vehicles contribute to wildland firefighting.
While these frameworks are mostly theoretical and limited to simulation, they
provide interesting ideas about a more complete wildfire fighting system.
Future research in these areas can provide new approaches for the further
development of autonomous operational systems without or with little human
intervention.

## Acknowledgements

This work has been partially supported by the government of Canada under the
Canada-Chile Leadership Exchange Scholarship. We acknowledge the support of
the Natural Sciences and Engineering Research Council of Canada (NSERC),
[funding reference number RGPIN-2018-06233].

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