An Augmented Reality System Architecture for Flood Management

Alexios Stavroulakis

, Despina Dimelli

, Michail Roumeliotis

and Aikaterini Mania

School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece

School of Architecture, Technical University of Crete, Chania, Greece

Keywords: Augmented Reality, Flood Management, Real-Time Operation, Dynamic Data Visualization.

Abstract: Flooding represents a considerable danger to both human lives and possessions, rendering it a prevalent natural

hazard. Navigation and risk assessment methods, in dynamically changing flood environments, are dependent

on flood visualization methods. Addressing the limitations of 2D visualization as well as Virtual Reality (VR)

setups that are employed in off-site simulations, this paper presents the system architecture of a novel system

for real-time urban flood management utilizing head-worn AR, integrating extreme scale data. Our system

architecture will offer a depiction of urban flood inundation in the city of Dortmund, Germany, dynamically

visualizing potential evacuation routes and water levels, on-site, in urban areas, while rescuers are in opera-

tion. The system’s integration with large-scale data analytics will allow the dynamic combination of weather

forecasts, sensor networks, historical flood data and urban topography.

INTRODUCTION

Amidst the increasing frequency and severity of

flooding incidents in recent years, emergency re-

sponse and rescue operations face significant chal-

lenges. Navigation and risk assessment methods, in

dynamically changing flood environments, are depen-

dent on flood visualization methods, which should ef-

ficiently communicate risk, without amplifying haz-

ards for first responders (Carver, 2019). Modern dig-

ital visualization tools are increasingly vital in en-

hancing the management of flood risks in urban areas

(Leskens et al., 2017). Rather than commonly used

2D visualization (Towe et al., 2020), prominent tech-

nologies, including virtual reality (VR), augmented

reality (AR), and digital twin simulations, are exten-

sively utilized to depict urban flood scenarios (Oyshi

et al., 2022). Among these, VR has gained extensive

usage (Calil et al., 2021), (Sermet and Demir, 2019)

informing for simulated scenarios, without, though,

awareness of the real world. While research effort

focusing on urban flood visualization are dedicated

to the phases of readiness and prevention, there is

a noticeable gap in the capabilities for real-time ob-

servation and handling of urban flooding events in

https://orcid.org/0000-0002-0234-1293

https://orcid.org/0000-0002-5465-8356

https://orcid.org/0000-0002-9457-5117

AR (Bakhtiari et al., 2023). The reliance on mobile

phones or tablets in previous AR urban flood visu-

alization has constrained on-site operations because

interaction is not hands-free (Mirauda et al., 2018).

This paper introduces the architecture of a novel

system for real-time urban flood management utiliz-

ing the capabilities of head-worn AR, integrating ex-

treme scale data. Previous work has been limited to

utilizing low-scale static data for visualization (Sarri

et al., 2022). Our system’s architecture, when imple-

mented, will visualize multiple urban flood scenarios

by consuming dynamic data, enhancing users’ situ-

ational awareness through cutting-edge AR. Accord-

ing to the system architecture presented, the user will

wear a Hololens 2 AR headset, allowing for hands-

free operation. Our system will dynamically generate

potential evacuation routes and predicted flood level

on-site, in real-time, contributing to the planning of

an efficient rescue. This represents a notable advance-

ment over past work, where routing to avoid pluvial

floods was primarily conducted within VR environ-

ments, focusing on simulation rather than operation

on the field.

Our contributions include:

•

A novel AR-based system architecture for flood

Stavroulakis, A., Dimelli, D., Roumeliotis, M. and Mania, A.

An Augmented Reality System Architecture for Flood Management.

DOI: 10.5220/0012778800003696

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2024), pages 211-218

ISBN: 978-989-758-694-1; ISSN: 2184-500X

211

visualization that utilizes extreme scale and com-

plex data analytics regarding critical urban infras-

tructure and weather forecasts, based on open data

sources. Our system’s architecture offers a depic-

tion of urban flood inundation and will

dynamically simulate potential evacuation routes

and water levels. Unlike VR that confines rescuers

in immersive simulations in laboratories, our

system encompasses on-site rescuers’

intervention.

•

An AR system architecture that integrates a first-

person perspective for situational awareness im-

provement allowing users to experience seamless

integration of the real-world enriched with digital

elements based on spatial data such as

topography, urban networks, densities and uses,

and weather forecast data, keeping users

informed.

•

Enhanced decision-making of flood risks and safe

navigation paths based on AR visualization but

also awareness of real-world surroundings, inte-

grating emergency response protocols, ensuring

safety navigation paths.

RELATED WORK

2.1

Augmented Reality and Floods

In addressing the increasing frequency of global

flooding events and the need for heightened public

awareness, past work showcases AR as an effective

medium for educating the public on local flood risks

(Tomkins and Lange, 2019), (Puertas et al., 2020).

Users are able to engage with potential flood levels

in their local flood zones, by leveraging an AR app

that offers in-situ modeling of basic 3D building pro-

totypes (cuboids) along a riverside, facilitating the vi-

sualization of an augmented flood plane (Haynes and

Lange, 2016). Notably, users can actively adjust the

flood plane height, enhancing the interactive experi-

ence. A real-time prototype for mobile augmented re-

ality (MAR) is put forward merging real-time build-

ing model updates, interactive flood visualization, and

seamless integration with live sensor readings, includ-

ing water level, humidity, and soil moisture, accessi-

ble through a network (Haynes et al., 2018). These

sensor readings contribute to detailed real-time anno-

tations. Feedback indicates the need for increased ge-

ometric model complexity to enhance operation on-

site. A MAR coastal erosion 3D visualization system

is put forward leveraging geographical data visualiz-

ing future shoreline changes due to coastal erosion

(Katsiokalis et al., 2020). The study operates under

challenging, for screens, outdoor bright lights achiev-

ing accurate registration of 3D sea segments with

the real-world coastline, viewed seamlessly through

a smartphone screen. However, the application’s effi-

ciency is affected during bad weather and sea waves,

causing the 3D content to drift in the scene. MAR

systems require the user to hold a mobile device

which, while flooding is taking place, is restrictive.

To address this issue, our system architecture

provides 3D visualization utilizing head-worn AR,

enabling users to operate hands-free, while

forecasting of events is streamed to the AR user, in

real-time.

Another approach for effective flood

visualization, is combining AR with a 3D-printed

terrain model (Zhang et al., 2020). The study explores

adaptive flood data processing and hybridizing virtual

flood and terrain models. The researchers simulate a

barrier lake dam-break scenario, comparing between

a flood visualization placed on a 3D printed terrain

model and one on a 3D digital terrain model. Results

show improved flood hazard understanding when a

3D printed model is involved. In our work, we offer a

system architecture which includes real-time urban

flood management, through head-worn AR on-site

rather than in the control room, without the need for

3D printed models.

2.2

Urban Management

Flooding is one of the serious natural climate con-

cerns, that is intensified by climate change and can

cause major economic, social, and environmental

consequences. Urban flooding, which refers to the

inundation of a densely populated area due to excess

rainfall on a continuous and impervious stretch of

land that mostly arises due to an overwhelming ca-

pacity of the drainage system and reduced infiltration

rate, (Eldho et al., 2018) is a major problem in many

parts of the world. Urban flooding, which can origi-

nate from coastal, pluvial, or fluvial flooding (Figure

1), is the primary source of flood losses worldwide.

Among the categories of flood hazards, pluvial flood-

ing, which is brought on by excessive precipitation

combined with insufficient stormwater infrastructure

or restricted infiltration capacity, has traditionally re-

ceived less attention as it is thought to be controlled

and generally causes less harm. Nonetheless, data in-

dicate that pluvial flooding is a major contributor to

cumulative damage over time, and that hazard expo-

sure changes, aging infrastructure, urbanization, and

global warming are all increasing the likelihood of

these catastrophes, while the necessary infrastructure

to mitigate floods is lacking in most urban areas, ren-

GISTAM 2024 - 10th International Conference on Geographical Information Systems Theory, Applications and Management

212

dering them acutely vulnerable (Brody et al., 2022).

The exposure of an urban area to flooding comprises

the population, its uses and infrastructure, environ-

mental and cultural assets, economic activities, and

all the city’s elements and facilities that cause changes

in physical processes, socioeconomic growth, migra-

tion, and economic changes. The growing trend of

urban flooding is a global phenomenon that has

become an important field of study and provides a

significant challenge, especially for modeling

communities and urban planners.

Figure 1: Types of urban flood.

Urban planning must balance competing needs

and maximize benefits from waterfront economic and

recreational activities and ecosystem services while

ensuring minimum loss of life and property through

safe location, safe construction, and safe activities.

Flood risk reduction measures are categorized into

structural and nonstructural measures. The structural

measures are mainly major public projects that re-

quire moderate-to-major planning and design efforts,

while on the other hand, the nonstructural measures

aim to improve urban planning and management. The

nonstructural measures can be categorized into emer-

gency planning and management, including warn-

ing, evacuation, preparedness, and flood insurance;

speeding up recovery to increase resilience by en-

hancing building design and construction; and flood

avoidance and reduction. The last category is di-

rectly linked with urban planning as it is related to

land use planning as land use, public spaces, relo-

cation, and forestation plans, and architectural plan-

ning as dry and wet flood proofing techniques, struc-

tural retrofitting or reinforcement, and facility mainte-

nance and repair plans (Abdrabo et al., 2022). Effec-

tive flood risk management is related to urban plan-

ning factors, such as the adjustment of urban infras-

tructure, and land-use practices that can integrate sus-

tainable drainage systems, create impermeable sur-

faces, preserve and restore natural floodplains, incor-

porate green infrastructure, propose limits in the con-

struction of flood-prone areas, and formulate emer-

gency response plans (Cea and Costabile, 2022), (Aj-

tai et al., 2023). Our proposed system architecture

addresses the management of urban floods with the

use of hydro-meteorological, topography, and urban

data that ensure real-time rescue operation and coor-

dination.

SYSTEM OVERVIEW

This paper proposes the system architecture for AR-

based flood visualization. An innovative approach is

put forward for designing a system managing ur- ban

floods and conducting on-site rescue operations in

real-time through the use of head-word AR, com-

bined with large-scale data integration. Targeting to

enhance real-time decision-making in flood scenar-

ios, our system’s architecture facilitates effective co-

ordination and execution of rescue operations. Uti-

lizing AR headsets, first responders and emergency

management personnel will access and interact with

real-time visual representations of urban floods, in-

cluding water levels, affected areas, and safe routes.

The system’s integration with large-scale data analyt-

ics will allow the dynamic combination of weather

forecasts, sensor networks, historical flood data and

urban topography. The result of data processing is

stored in Kafka topic(s). Subsequently, the Hololens

2 device connects to the corresponding topic through

a client as a consumer and receives the appropriate

messages. Upon receipt of the message from the AR

device, it undergoes processing to isolate the valuable

information necessary for visualization. The AR en-

vironment is designed in a way that ensures vital in-

formation like flood level forecasting will be accessi-

ble without overwhelming the user or blocking users’

field of view. The head-mounted AR device offers a

hands-free experience, enabling users to remain fully

engaged with their surroundings while receiving cru-

cial data updates and navigational assistance.

3.1

Urban Management: Dortmund

The proposed AR-based flood visualization system is

going to be evaluated in Dortmund. The city of Dort-

mund, situated in the metropolitan area of Ruhr and

the heart of Westphalia, is in the catchment area of

three river systems, and has a stable population of

590.000 according to the 2024 census. It is a postin-

dustrial city that, in recent years, has been developing

An Augmented Reality System Architecture for Flood Management

213

the sectors of IT, logistics, and biotechnology. In re-

cent decades, many plans have attempted to manage

the profound structural change in the economy and

create the future of the municipality and region. The

authorities, inhabitants, and experts of the city aim to

develop fewer industrial sites, more residential areas,

a lot more landscape, and more area for nature. Urban

planning promotes equality and balance between the

economic, environmental, and social sectors, sustain-

ability, and participation in all planning strategies,

and aims to establish a reliable framework for public

and private investment in public space, retail, office

build- ings, housing, and large-scale projects (Sierau,

2005). Dortmund has suffered from floods in its

current his- tory, so it is important to manage floods

in a way that will secure resilient urban environments.

In this direc- tion, images that create a sense of 3D

space and depth create a more realistic and interactive

experience for the viewer, so they can provide

important information for flood management

(Bakhtiari et al., 2023). AR can be applied to indicate

the progress and expansion of the flood occurrence

and to assess the flood dam- age and vulnerability of

urban structures. AR models of the built environment

enhances realism and enables more accurate

assessments of flood damage. Hence, combining AR

technology with flood data can pro- vide decision-

makers with valuable insights in urban flood

management (Schröter et al., 2018).

Figure 2: Copernicus Emergency Management system:

Flood monitoring and forecasting.

The urban elements that need to be visualized are

related to the city’s exposure hazards and vul-

nerability. These are topography data as Global

digital terrain models (DTMs)—Shuttle Radar To-

pography Mission (SRTM) and Multi-Error-Removed

Improved-Terrain (MERIT) as well as locally avail-

able data such as laser imaging, detection, and rang-

ing (LiDAR); high-resolution satellite or orthophoto

imagery; drone survey data; and bathymetric sur-

veys of water bodies (Ferguson, et al., 2023). Data

to be integrated may be the Hydrometeorological data

global datasets such as Multi-Source Weighted-

Ensemble Precipitation (MSWEP) or European Cen-

tre for Medium-Range Weather Forecasts (ECMWF)

Reanalysis v5 (ERA5) or the Copernicus Emergency

Management system for flood monitoring and fore-

casting (Figure 2) and local datasets with time series

of rainfall and winds, water levels, or river discharges.

Another important urban element is the existing flood

protection infrastructure and numerical models for

flood hazard modeling, as well as the layout and di-

mensions of primary and secondary drainage infras-

tructure such as road drainage, canals, culverts, pump

stations, and tidal gates, as well as the dimensions and

characteristics of coastal or fluvial embankments,

dunes, and existing hydrological, hydraulic, and risk

models (Ferguson et al., 2023). Existing flood, ex-

posure, or vulnerability data such as global or local

flood hazard maps, (mapped flood hot spots), Open-

StreetMap data, cadastre system data, building types,

population distribution, and characteristics are also

elements that may be visualized as having decisive

role in flood management. Finally, the city’s infras-

tructure, such as roads, drinking water, sanitation,

drainage, and flood protection infrastructure; health

care and school facilities; and environmental and cul-

tural assets; are urban elements that have a decisive

role in city’s resilience and urban flood management.

In the proposed system architecture, the available data

are the available data sets from the EU sites as Coper-

nicus browser (Copernicus Browser, 2024), the Eu-

ropean floodsviewer (European floodsviewer, 2024),

fire data (Copernicus fire data, 2024) and urban data

as mobility networks, public infrastructures, available

3d models, e.t.c

3.2

System Architecture

The proposed architecture, as illustrated in Figure 3

and Figure 4, integrates both static and dynamic into

the DATA Processing Layer. These sources include

geo-location data, information from weather sensors,

topography data, flood history records, satellite im-

agery, and the user’s GPS location. The data under-

goes mostly real-time processing within the Data Pro-

cessing layer, utilizing machine learning algorithms

and big data processing tools such as Apache Flink.

Continuously, the processing results include forecasts

of water levels in urban floods, identification of dan-

gerous and safe areas and establishment of evacua-

tion routes stored in a Kafka Topic. The data from the

Kafka Topic is retrieved through an intermediate

GISTAM 2024 - 10th International Conference on Geographical Information Systems Theory, Applications and Management

214

server located in the Server/Client Layer and is for-

matted in GeoJSON (GeoJSON, 2016). The server

deserializes the GeoJSON data before transmitting

it to the HoloLens 2 device. In Hololens 2, data

undergoes additional processing to align real-world

data with the AR world coordinate system. Hololens

2 user interfaces and interaction is being developed

in Unity3D, employing the Mixed Reality Toolkit

(MRTK3, 2023).

3.3

Design of the AR Experience

When on-site, the user will wear the Hololens 2 which

is connected to the appropriate Kafka topic via the in-

termediate server. When the HoloLens 2 device con-

nects to the server, it begins consuming the data re-

quired for visualizing valuable information. As seen

in Figure 4, the data consumed from the server under-

goes two different processes simultaneously:

Figure 3: System Architecture and Layers.

Figure 4: AR Design.

An Augmented Reality System Architecture for Flood Management

215

•

A process which visualizing the given data

into the 3D world

•

A process for visualizing the data on a 2D

map, which map will be positioned in the

upper right corner of the field of view (FOV)

of the HoloLens 2.

Our AR visual components design, included in the

proposed architecture, is based on five different pat-

terns: a) Proxy, which is a visualization near the user

that resembles a referent that is farther away, b) Panel

is a conventional visualization shown together with

the real world objects, semantically linked to a refer-

ent, but lacks a geometric relationship to the physical

environment, c) Labels are a pattern typically used in

an embedded view. Labels are intended to supply ad-

ditional information to referents, d) Glyphs that are

simply visual encodings of some information associ-

ated with one or more referents, which are placed so

they are touching the referent(s) and e) Trajectories

which are a special kind of glyph that connects two or

more endpoints (Lee et al., 2023). The information that

we will be visualized on the Hololens 2 is:

2D Map: The pattern that we use for 2D map com-

ponent is the ”Proxy”. The 2D map will be always be

centralized around user’s GPS location. Additionally,

on the map, routes, points of interest (schools, hos-

pitals etc) and areas will be displayed. We use the

mapbox platform for 2D Map visualization and for

conversion processing (Mapbox, 2010).

Routes: We begin by receiving a set of GPS loca-

tions retrieved from a GeoJSON file from the ”Line

String” property. For real-world visualization, the

pattern that we use is the ”Trajectory”. We will then

align the real GPS locations with the local coordinate

system of the Hololens 2 device through an AR cali-

bration process and visualize those points on the real

world. We will connect these points one by one with

a line, starting from the user’s GPS location which is

also aligned with the local coordinate system of the

Hololens 2 device. For the 2D map visualization, we

follow the same logic, with the only difference being

the calibration process, which must be aligned with

the 2D map coordinate system. The 2D map visual-

ization object will be toggled on and off to ensure that

the user’s field of view (FOV) remains unobstructed.

A concept of route is illustrated in Figure 5.

Figure 5: Route Visualization Concept.

Points of Interest (POI): Utilizing the Glyph pat- tern,

we apply the same logic as with ”Routes” to vi-

sualize any point of interest on a 2D map and in the

3D world. The key difference is that we don’t connect

these points with lines A concept of point of interest

visualization is illustrated in Figure 6.

Figure 6: POI Visualization Concept.

Areas: Following the same logic as with ”Routes”, we

extract valuable data from the GeoJSON residing in

the ”Polygon” property. Next, we will generate GPS

points on the 2D map and connect them sequentially

with lines, connecting also the last point with the first.

In the real world, a message will be displayed on lens

utilizing the ”Panel” pattern, informing the user that

they have entered a specific area by combining the

user’s GPS location with the defined area shape.

Flood (Areas): We consider the ”Floods” as ”Ar-

eas,” and we follow the same logic as described above

for visualizing them both in the 2D map and in the 3D

world. The key difference is that in the 3D world, we

will also visualize the possible water levels that may

be reached during the next time window in a manner

that does not obstruct the user’s field of view (FOV),

utilizing the Panel pattern. A concept of ”Flood” vi-

sualization is illustrated in Figure 7.

GISTAM 2024 - 10th International Conference on Geographical Information Systems Theory, Applications and Management

216

Figure 7: Flood Area Visualization Concept.

4 CONCLUSIONS

This paper proposes a novel system architecture for

AR flood visualization that will, when implemented,

utilize extreme scale and complex data analytics re-

garding critical urban infrastructure and weather fore-

casts, based on open data sources. Our system archi-

tecture dynamically visualizes potential evacuation

routes and water levels, while a rescuer wears AR ap-

paratus and moves on-site. We expect that the first-

person perspective of head-word AR will improve sit-

uational awareness and allow informed decisions and

intervention in high-risk areas. Initial testing will be

conducted in the area of Dortmund, Germany. Our

system will be complemented by gaze-based interac-

tion, allowing for hand-free operation as well as level-

of-detail management of digital information, as su-

perimposed onto the real-world so that the real-world

is not obstructed. Moreover, rescuers’ physiological

monitoring will allow communication of distress.

ACKNOWLEDGEMENTS

This work was supported by the EU project

CREXDATA under Horizon Europe agreement No.

101092749.

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