AStrategicApproachforUtilityCompaniesLeveragingGeoAI:/me01IntroductionIntroductionThe

primaryobjectiveofutilitycompanies

istoeffectively

planandmanage

their

infrastructuretoensure

reliabledeliveryofessentialservices

likewater,electricity,natural

gasandtelecommunicationto

itscustomers.

Geospatial

data

accuratelymapsutilitiesonthesurface

ofthe

earth,

alongwith

its

technical

and

physical

parameters.

Therefore,

it

iscriticalforeffective

infrastructure

management,enablingprecise

supervisionofassetssuchas

powerlines,pipelines,

andtelecom

towerswithintheelectricity,water,gasandtelecommunication

sectors.

Ithelps

indecision

making

byassisting

intheoptimisation

ofnetworkconfigurations,

identifying

upgrading

needs

andacceleratingfaultdetectionand

maintenance

processes.GeospatialandAImarketgrowth:A

rapidtransformationTheacceleratingadoptionofgeospatialand

AItechnologieshighlights

theirexpanding

roleacrossindustries,particularly

i

n

utilitiesand

energy.Thisisthe

idealmomenttoembraceand

leverage

GeospatialArtificialIntelligence(GeoAI)which

is

an

integrationofGeospatial

dataandanalysiswith

AItechniquesandtechnologiesforextracting

meaningfulinsightsfromthedata.The

GeospatialAImarket

in

Middle

Eastand

NorthAfricarecordedavalueofUS$57.3mn

in

2023and

is

anticipated

toreach

approximately

$222.8mn

by2031,growing

at

a

CAGR

of

18.5%

during

the

forecast

period

2024-2031.6

Thisindicatesthetransformative

possibilityit

holdsfortheentire

region.Inthe

nextfewsectionswe

willfocusonunderstandingdifferentcomponentsofGeoAI

,differentuse

cases,

challenges

in

implementingGeoAIandfuturetrendsfortheregionalutilitiessector

inMiddle

East.Utilities

&energyAImarket:

From$15.45B(2024)to$75.53B(2034).Globalgeospatialmarket:Valuedat$560.18B

in2024,projectedtohit

$1Tby2028(CAGR15.9%).Keycontributor:Theutilitiessector(electricity,water,gas)

holds18%marketshare.MiddleEastgeospatialmarket:Expectedtogrowfrom$1.16B(2024)to$1.71B(2029)(CAGR

8.15%),drivenbysmartgrids,meters,andasset

management.GlobalAImarket:Estimatedat$196.63B(2023),setto

soarto$1.81Tby2030

(CAGR

36.6%).MiddleEastAI

market:Growingfrom$11.92B(2023)to$166.33B(2030),fueledbynationalAIstrategiesandsectoradoption.02Convergenceof

geospatial

analyticswithAIArtificial

IntelligenceProblemSolving&Search

StrategiesRoboticRepresentationPl

anningand

SchedulingSpeechVisual

PerceptionReinforcement

learningAutom

atic

Linear/LogisticNeuralNetworks

RegressionEnsemble

Adaptive

Resonance

K-MeansMethods

Theory(ART)

Network

(RNN)

ClusteringMultilayerAnomaly

Perceptro

ns

(M

LP)

Autoencoders

Radial

Basis

DecisionDetection

Functi

onNetworks

treesModulDeep

learningLong

Short

-TermGenerativeMemory

Networks

Adversarial

Networks(LSTM)

(GAN)TransformerModels(BERT,GPTetc)Recurrent

NeuralNetworks

(RNN)Deep

BeliefNetworks

(DBN)ArtificialIntelligenceMachineLearningDeepLearningGenerativeAIArtificial

Intelligence(AI)isthescienceandengineeringofmaking

intelligent

machinesthat

cansimulatehuman

learning,comprehension,problemsolving,decision

making,creativity

andautonomy.MachineLearning

(ML)isasubsetofAIwhich

involves

creating

models

by

training

an

algorithm

to

learn

patterns,make

predictions

ordecision

basedondatawithout

being

explicitly

programmed.

DeepLearning

isasubsetofmachinelearningthat

uses

multilayered

neural

networks,

that

is

inspired

by

how

thehuman

brainoperatestotakecomplexdecisions.

GenerativeAI(GenAI)

is

a

subset

of

DeepLearningwhichcan

produce

newcontentliketext,

imagery,

audio,andsyntheticdata

basedonthe

inputsfrom

humans

intheform

ofnatural

language

prompts.Geospatialanalytics

isthescienceofanalysinggeographicalandspatialdatato

identify

patterns,trends

andrelationshipsbetweendifferentassets,

peopleor

places.

Itintegrates

remote

sensing,

GeographicInformationSystem

(GIS),Global

PositionSystem

(GPS)

andbigdatatoanalyse

location-basedinformation

across

multiple

dimensions

toproduceoutputsintheform

ofmaps,

graphs,

statistics

and

cartograms.ConvergenceofgeospatialanalyticswithAI

Figure

1:GeospatialAnalytics

Figure2:Artificial

IntelligenceConvolutional

Neural

NetworksDeep

Reinforcement

LearningDeepAutoencodersSpatiotemporalAnalysisBig

GeospatialdataAnalysisNatural

Language

Processi

ng

(NLP

)I

nte

lligentSel

f

Organising

MapBolt

zmannMachinesSuitabilityAnalysisProximity

AnalysisNetworkAnalysisHotspotAnalysisClusterAnalysisVectorAnalysisar

Neural

NetworksHopfield

NetworksPrincipalComponentAnalysis(PCA)MachineLearning3D

AnalysisK-NearestNeighbours

(KNN)Supportvectormachines

(SVM

)AutomatedProgram

mingNaiveBayes

Classificat

ionExpertSystemsRandom

ForestRecurrentNeuralRecognitionReasoningKnowlageParameterGeospatial

AnalyticsArti?cial

Intelligence

(AI)Geospatial

Arti?cialIntelligence

(GeoAI)FocusUnderstanding

spatialrelationships,

patterns,and

trendsSolving

general

problems

using

intelligent

algorithms

andtechniques

across

domainsSolving

geospatial

problems

usingAI/ML

techniquesPrimary

datatypesSpatial

data

(vector,

raster)and

non-spatial

attributesAnytype

of

data:

text,

images,

video,

numerical,

categorical,

etcSpatial

andtemporal

data

with

AI-ready

formatsKey

techniquesSpatial

statistics,

geospatialmodelling,

cartographyMachine

learning,

deeplearning,

natural

languageprocessing,

computer

visionDeep

learning,

machinelearning,

computer

visionapplied

to

spatial

dataOutputsMaps,

spatial

models,

reports

and

dashboardsPredictions,classifications,

recommendations,decisionsupportsystemsPredictive

spatial

models,automated

feature

extraction,

intelligent

insightsAutomationLimited;

manual

intervention

is

neededFully

automated,dependingon

the

system's

design

andcomplexityHigh

automationthroughAI/MLmodels

and

neuralnetworksScalabilityModerate;

dependent

ongeospatial

data

tools

andprocessingHighly

scalable

using

cloudand

distributed

computing

environmentsHigh;leverages

AI-drivenscalability

with

cloud

anddistributed

computingComplexityRelatively

simpler;

focuses

on

spatial

data

analysiswork?owsVaries;

can

rangefrom

simple

algorithms

to

overly

complex

neural

networksMore

complexdue

tointegration

ofAI/ML

withgeospatial

dataUsage

exampleOptimising

power

line

routesAI

Chatbotfor

citizens

foroutage

reportingPredictive

maintenance

ofpower

lines

using

droneimageryGeoAI

isan

amalgamationofgeospatialdata,science,

andtechnologywith

AIto

extract

meaningful

insightsandsolve

spatial

problems.If

weconsiderAIasthedevelopmentofmachinesthat

canthink

and

reason

like

humans,

GeoAI

represents

anintersectionofAIandgeographyindevelopingadvancedsystems

that

make

use

of

geospatial

big

data

to

perform

spatial

reasoningandlocation-basedanalysis,

much

likehumans.

Figure3:GeospatialArtificial

Intelligence(GeoAI)Whileeachofthesetechnologieshasunique

strengths,

limitations

andapplications,

it

is

crucial

to

understand

how

theycanbeeffectively

leveragedtoaddress

businesschallenges

inthe

utilitysector.

The

table

below

provides

acomparativeoverviewofkey

parameters

andguidanceonselectingthe

appropriatetechnology.Arti?cialIntelligenceGeospatial

AnalyticsGeoAIGeospatial

analytics

gains

significantpower

whenenhancedby

AI-driven

capabilities,

such

asobject

detection

fromimagery,automation,scalabilityforlargedatasets,improvedaccuracy,fasterprocessingand

immersive

technology.GeoAIcombinespredictive,prescriptiveinsightswithgeospatialdatafrom

drones,

satellite

imageryand

helps

to

solve

theproblem

inspatialcontext.

Italso

helpstoautomategeospatial

analyticsto

make

themautonomous

andwork

with

minimum

humansupervision.

GeoAIbrings

thegeographiccontexttosolve

realworld

problems

using

multiple

AItechniquesobjectdetection,spatialoptimisation

,

natural

language

processing,integration

with

multiple

data

sources,etc.GeoAIapplicationsspanacross

sectorssuchas

urban

planning(smartcity

development),

utilities

(pipeline

monitoring),

agriculture(precisionfarming),transportation(traffic

management),environmentalconservation(climatechangemodelling),andpublichealth

(epidemictracking),enablingdata-driven

decision-making

andenhanced

operationalefficiency.However,thefocusofthis

paperwillbeapplications

ofGeoAIfor

utilitysector,

including

electricity,water,gas

andtelecommunication.ComponentsofGeoAIfortheutilitysector03Thetable

belowshows

howeachcomponentcan

be

supported

by

industry

standard

open

source

or

proprietary

tools/platforms.ComponentSupported

tools

and

platformsGeospatial

dataRaster

data

can

be

created

from

di?erent

types

of

sensors

like

Satellite,

Drone,

Aerial,

etc.

Vector

data

can

be

created

bydigitizing

raster

data

or

by

taking

measurements

of

earth’s

surface

or

assets

through

di?erent

surveying

techniques

like

total

station,

GPS

,

LiDAR,

etc.Geospatial

analyticsOpen-source

platforms:

QG

IS,

GRASS

GIS,

PostGIS

Geo

Server,

SAGA

GIS,

R

with

spatial

packagesProprietary

platform:

Esri

ArcGIS,

Hexagon

Geo

media,

ERDAS

IMAGINE,BentleyMaps,

etc.AI,

ML,

DeepLearning

algorithms,techniquesOpen-source

platforms:

TensorFlow,PyTorch

,

Apache

Spark

MLlib

,

Scikit-Learn.Proprietary

platform:

Google

Vertex

AI,

AWS

Sage

Maker

,

Azure

AI,

IBM

Watson

,

OpenAI

GPT

(ChatGPT),

NVIDIA

Jetson,

etc.Data

Processingand

StorageOpen-source

platforms:OpenStack,CloudStack,

Eucalyptus,Open

Horizon,Edge

X

Foundry,

etc.Proprietary

platform:

Amazon

Web

Services

(AWS),

Microsoft

Azure,

Google

Cloud

Platform

(GCP),IBM

Cloud,

Oracle

Cloud,

NVIDIA

Jetson,MicrosoftPercept,

etc.Visualisation

toolsOpen-source

platforms:

QG

IS,

GRASS

GIS,

PostGIS

Geo

Server,

SAGA

GIS,

R

with

spatial

packages,

Cesium,

Unity,

Google

Earth,

etc.Proprietary

platform:

Esri

ArcGIS,

Hexagon

Geo

media,

ERDAS

IMAGINE,

Bentley

Maps,

PowerBI,

Tableau,Microsoft

HoloLens,etc.Data

integration

andinteroperabilitystandardsDataIntegrations

is

supportedby

the

above

Enterprise

applicationsin

Open-source

and

Proprietary

platforms.Interoperability

standards

are

followed

by

most

of

the

industry

standards

platforms

include

International

Organization

for

Standardization

(ISO),

World

Wide

Web

Consortium

(W3C)

and

Open

Geo

spatial

Consortium

(OGC)

standardsEthical

andregulatoryframeworksInternational

regulations

which

dealsGlobal

Ethical

andRegulatoryFrameworksinclude

Organisation

for

EconomicCo-operation

andDevelopment

(OECD)

AIPrinciples,UNESCO

AIEthics

RecommendationDataprotection

andPrivacy

laws:General

DataProtection

Regulation(GDPR),

Personal

DataProtection

Law(PDPL)Cyber

laws:National

Institute

of

Standards

and

Technology

(NIST)

CybersecurityFramework,

KSAEssential

Cybersecurity

Controls

(ECC)ComponentsofGeoAIforthe

utilitysectorThe

recipefordeveloping,implementinganeffectiveandrobust

GeoAIsolution

for

utilitysectorshould

includeall

the

keyingredients

likegeospatialdata,geospatialanalytics,AI,

MLalgorithms,data

processing,storage,

visualisationtools,data

integration,interoperabilitystandards

andethical

regulatoryframeworks.

Figure4:

ComponentsofGeoAIforthe

utilitysectorVisualisationtoolsMaps,

charts,

graphs,infographics,

3D

models,

digitaltwin,AR/VR

modelsComponentsof

GeoAI

forGeospatial

AnalyticsNetworkanalysis,

outageanalysis,

upstream

trace,downstreamtrace,

serviceareaanalysis,

leakdetectionGeospatial

dataVector

data:Water

pipelines,

electriccables,

valves

raster

data:

droneimagesoftransmissiontowerAI,

ML

algorithmsAutoencoders

for

anomaly

detectionRandom

Forest

algorithmsforanalyzing

historical

dataDataprocessing

&storageCloudcomputing,

big

dataandedge

computingcapabilitiesEthical

&

regulatoryframeworksUNESCOAIethics

recommendation,PDPL,

KSA

essential

cybersecuritycontrolsDataIntegration

&interoperabilitystandardsISO,W3C,

OGC

standardsutility

sectorGeoAIusecasesin

utilitysector04GeoAIusecases

inthe

utilitysectorThe

proliferationofGeoAIin

MiddleEastforthe

utilitysectorhas

been

primarily

driven

by

region’s

urgentrequirementsforsustainabledevelopment,efficientresource

managementandtechnological

modernisation.GeoAIis

increasinglyrecognised

as

keyenablerinaddressingcriticalchallengesacross

utility

sectors

while

aligning

withregionalaspirationsforsustainableeconomies

andsmartcities.Followingaresomeoftheapplications

ofGeoAIinthe

utilitysector:Inthefirststepthedrone

capturesthousandsofimagesofthe

powerlines.

Drones

can

reachand

cover

areas

of

transmission

lineswhicharenotaccessibleby

roadwaysforthemaintenanceteam.Inthesecondstep,thousands

ofgeotagged

imagesarestored

inthegeospatial

databasewith

locationalinformation.The

user

canclick

onanylocation

onthe

map

andviewthe

images

ofthe

power

lines

at

that

location.This

helpstoovercomethechallengeofmanuallyviewing

all

images

andaccurately

identify

broken

insulators.Inthe

third

step,

deep

learning

algorithmswhich

are

trained

on

images

of

broken

insulators,flashed

insulators

and

similar

scenarios

can

detect

the

anomaly

from

thousands

of

images

in

few

minutes,

thus

saving

huge

time,

efforts

andcostwhile

beingaccurate.Inthefourth

step,deeplearningalgorithmshighlightbroken

insulatorswith

different

colours

by

plotting

them

on

mapfor

easy

identification.The

usercanclick

onthemap

andverifythe

image

ofthe

broken

insulators.Inthefinal

step,the

map

locationswith

photosaresenttothe

maintenancefieldcrew

on

their

mobile

phones.

Thisnot

onlyenablesthemtoaccuratelyreachthelocationofrepair

butalsoensures

that

they

carry

appropriatetools

andreplacementdevicesforcomplete

thejob.Thisworkflow

helpsthepowerutilitiesteamtoaccuratelyidentify

exact

locationsofthe

broken

insulators

for

focusedmaintenancewhichsavestime,effort

andcosts.Followingaresomeofthesuccessstories

demonstratinghowdifferent

utility

companies

have

implemented

GeoAI.Oneofthemajorgasnetworkoperators

in

Europe

usedacombination

ofoptical

and

radar

satellite

imageryformonitoringover

160kmofhigh-pressuregaspipeline,whichisrouted

through

forests,fields

aswell

as

through

built-

upareas.VariousGeoAItoolswere

employedfortimeseriesanalysis,change

detection

andotherspatial

analyses,

revealinggrounddeformations

inpipelineareas.These

deformationswere

classified

intocategories

rangingfromgreaterthan2cm/yeartoless

than

10

cm/year.Vegetationexposuretopipelineswere

classifiedfrom

3mtolessthan

<10m

basedon

the

proximity

ofthevegetation

to

thepipelines.The

useofGeoAItoolssavedthem

operation

maintenance

cost,

time

and

provided

them

insights

on

their

pipelinewhichweresuspectabletorisk

bygrounddeformationandvegetationintimely

manner.Theywereabletotake

correctiveactionson

identifiedfocusedareas.7Casestudy:

Detecting

brokeninsulatorsonpower

linesusing

GeoAIElectrictransmissionanddistributioncompanies

manage

powerlinesthat

usuallyspanacross

largeareas,

usually

thousandsofkilometers.Itisoften

a

daunting

taskforthe

maintenance

team

to

identify

manually

broken

insulators

byvisual

inspection.Withtheuseofdronesthatcapture

images

of

the

power

lines,

the

laborious

taskofvisualinspection

iscompleted.Thefigurebelowshowsaworkflow

of

using

GeoAItools

to

automatethe

process

of

identifying

locationsofthebroken

insulatorwithevidence.Outputon

MapThe

output

of

theDeepLearningAlgorithmisplotted

onMap.Clicking

onmap

shows

thebroken

insulatorsFocusedMaintenanceThe

broken

insulator

photo

&

geospatiallocation

is

sent

tomaintenance

teamon

mobileGeotaggedimagesGeotaggedimages

of

the

transmissionlines

are

stored

inGeo

spatialdatabaseDroneCaptureDrone

capturesthousands

ofimages

of

Powerlines

whichcovershundredsof

kmsAsset

repaircompleteThe

Broken

Insulatoris

fixed

saving

time,

efforts

&

costDeepLearningDeepLearningmodel

is

used

todetect

brokeninsulatorsA

leadingtelecommunication

in

MiddleEastfacedchallengesinoptimising

their

network

performancewhich

includedidentifyingweaksignalzones,

managingnetworktrafficduring

peakevents

likeconcerts

orsports

matches,

planning

efficient5Gdeploymentacross

diverse

urban

andrurallandscapes.The

company

implementeddifferent

GeoAI

use

caseslikeCallroutingoptimisationwhichdeployed

ML

algorithmstodirectcallsthrough

the

least

congested

routes,

ensuring

uninterruptedcommunication.

Realtimespatial

analyticswere

usedduring

sports

events,

musicconcerts

to

monitoruserdensityandadjustnetwork

resourcesin

real

timeto

maintain

optimal

performance.BydeployingdifferentGeoAItoolsthecompanysaw

a25%

reduction

indeployment

costs

through

better

network

planning.Theyalsoobserveda20%increase

inmobile

internet

speedswhich

resulted

in

improving

services

and

resultedinusersatisfaction.The

company

alsoobservedfaster

response

times

tooutages

and

disruptions,

reducing

servicedowntime.8ChallengesinGeoAIImplementationforutilities05ChallengesinGeoAIimplementationfor

utilitiesImplementingGeoAIsolutionforutilityprovidersoffersthe

possibility

of

increasing

efficiency,

availing

resourcesappropriately,andimprovingthe

levelofintelligence

inthe

use

ofavailable

geo-spatial

data.

However,the

approachfor

achievingthesegoals

isfraughtwithdifferentchallenges.These

includecontrolling

large

anddispersed

datasets,assimilatingGeoAIsolutionswithexistingonesandhighinitial

implementationcosts.

More

so,

utilities

aredealingwith

internal

conflicts,shortageofAIskills

andisexposedtoheavyregulations.

Othergrey

areas,

namely

AI

model

biasand

invasionofprivacy

alsoaddcomplications.

Meetingthesechallengeswillrequire

defining

clear

strategiesthat

allowfor

phasedimplementationofthechanges,clearcommunicationof

the

changes,

ease

of

expansion

as

well

ascollaborationbetweenthetechnology

providersandtheusers.01

Datachallenges?

Qualityand

accuracy:

Manyutilityproviders

lackup-to-datedata;completeness

ofthe

data

is

also

an

issue

alongwithfragmenteddata.?

Integration:Consolidatingdatafrom

disparatesourceslikeIoT,

geospatial

systems,Asset

management,

CRM,

ERP

andotherlegacysystems

isachallenge

duetoabsenceofstandardised

data

models.?Volumemanagement:Thevolume

of

data

from

remote

sensing

sources

likedrones,

satellites,field

survey,

IOT

isgrowingcontinuouslyandprocessingandmanagingthe

data

effectively

is

challenging.02Technologyandinfrastructure?

Legacysystem:

Itisdifficulttointegratewith

legacysystemsdueto

lack

of

interoperability.?

Initialinvestments:

ImplementationofGeoAIsystem

my

require

initial

investmentsortechnology

refresh

whichcanaddadditional

coststo

existing

IT

budget.?

Scalability:The

utilitygridsexpand

itisessentialto

maintainthe

scalability

of

GeoAI

solutions

andassociated

techstack.03Organisational

barrier?

Resistanceto

change:

Employees

may

resistAIadoptionduetounfamiliarity

ofthetechnology

andfearof

job

replacement.?

Lackofexpertise:The

utilitycompanies

may

nothave

expertsin

Geospatial

andAItechnology

in

house,

whichmakesthey

relyon

external

vendors.?Workflowdisruptions:

Mundanetaskswhichcan

be

automated

using

AI

may

temporarily

disrupts

the

existingworkflowcreatingconfusionamongthestaff.04Regulatoryandsecurityconcerns?

Data

privacy:

Itisnecessarytobecautiousabout

privacy

concerns

when

collecting

consumer

data

and

using

it

foranalytical

purposes.?

Cybersecurityrisk:

SinceGeoAIsystemareintegratedtocritical

utility

infrastructure

they

may

be

vulnerable

tocyberattacksandhencerequiredeffectivesecurity

measures.?

Regulatorycompliance:

Itisimportanttoensurethat

GeoAIsystems

meetthelocal

and

international

standards,whichmayvary

insome

parts

ofthe

world.05Interpretabilityof

AI

models?

Complexity:Advanced

GeoAImodelsareusuallybasedondeep

learning

andsometimes

it

is

difficultto

explaintheirdecisionsmakingthem

act

like“black

box”

.?Accountability:Lack

ofinterpretability

can

leadto

operational

risks

and

reduced

trust

among

stakeholders,especiallyinsafety-criticalapplications.

Henceitis

importantto

use

Employing

Explainable

AI

(XAI)

and

using

interpretable

models,when

possible,toaddresstheseconcerns.06Ethical

Implications?

Bias:Thetrainingsetsused

inAImay

contain

different

types

of

biases

likegeographical,

selection,

coverage,

reporting,algorithmic,

etc.Itisimportanttoconsiderlocalisation

aspects

andother

relevant

aspects

fortargeted

implementationwhenconsidering

trainingdatasets?Job

replacement:Automationofsurvey,field

inspections

may

result

inworkforce

reduction

which

is

likely

to

raise

concernsamongtheworkforces.?

Fairness:

Itisimportanttodistributethe

benefitsderivedfromAIderived

insights

e.g.

uninterrupted

power

supplyinruralandurban

areas

remains

a

challenge.Empoweringutilities

through

GeoAI06Problemdefinitionandstrategicalignment1.IdentifycorechallengesIdentifycore

businesschallenges.Classifythem

intodifferentcategoriesofplanning,operations,maintenance,

regulatory.2.

Alignwithbusiness

goalsAlignGeoAIinitiativeswithorganisationstrategicgoalsandobjectives.AlignGeoAIinitiativeswithdifferentstakeholders

inthevalue

chain.3.Maturity

levelassessmentCurrentstateassessmentinfouraspects:people,

process,

data

andtechnology.Benchmarkingyourmaturityagainstpeers.Identifygapsandareasforimprovementtoalignwith

best

practices.Outlinestepsforadvancingtothe

nextmaturitystage.Emp