DEVELOPING OPTIMIZED DRONE SYSTEMS FOR SURFACE-MINE SLOPE MONITORING AND EARLY-STAGE CRACK DETECTION

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DEVELOPING OPTIMIZED DRONE SYSTEMS FOR SURFACE-MINE SLOPE
MONITORING AND EARLY-STAGE CRACK DETECTION
Lukman A. Alabede
Uni e si y o Jos, Nige ia and and Al ai UAV Technologies. Kwame Nku umah
Uni e si y o Science and Technology
ABSTRACT
Su ace-mine slope s abili y emains one o he mos c i ical de e minan s o ope a ional sa e y, p oduc ion
con inui y, and geo echnical isk managemen . T adi ional moni o ing echniques such as pe iodic o al-s a ion
measu emen s, g ound-based LiDAR, and manual inspec ions o en s uggle o cap u e sub le de o ma ion
pa e ns o ea ly-s age c acking ha e ol e apidly ac oss la ge, i egula slope aces. As mines expand la e ally
and e ically, hese limi a ions c ea e blind spo s in haza d de ec ion, inc easing ulne abili y o slope ailu es,
equipmen losses, and wo ke endange men . Eme ging ad ances in d one-based sensing sys ems p o ide a
ans o ma i e pa hway o enhancing he p ecision, speed, and spa ial each o slope-moni o ing p og ams. F om
a b oade pe spec i e, d one pla o ms equipped wi h high- esolu ion LiDAR, mul ispec al imaging, he mal
senso s, and isual–ine ial SLAM echnologies deli e dense spa ial da ase s ha can de ec mic o- ac u es,
bench-wall de o ma ions, ock-mass discon inui ies, and sub le he mal anomalies indica i e o impending
ins abili y. These unmanned sys ems signi ican ly educe da a-collec ion ime while imp o ing access o s eep
highwalls, emo e benches, and geo echnically sensi i e a eas ha pose challenges o g ound c ews. Na owing
he ocus, op imized d one sys ems ailo ed o slope moni o ing ely on ad anced algo i hms o c ack
segmen a ion, change de ec ion, and empo al de o ma ion acking. Machine-lea ning models enhance ea ly-
s age c ack iden i ica ion by analyzing geome ic i egula i ies, e lec ance a ia ions, and spec o he mal
g adien s ha p ecede isible ailu e mechanisms. When inco po a ed in o digi al- win slope models and eal- ime
geo echnical dashboa ds, d one-de i ed da a enables p oac i e decision-making, a ge ed ein o cemen , and
p edic i e isk ale s. By in eg a ing senso op imiza ion, e icien ligh -pa h planning, scalable da a p ocessing,
and AI-d i en analy ics, nex -gene a ion d one sys ems ede ine he u u e o su ace-mine slope su eillance.
These capabili ies ans o m slope moni o ing om in e mi en obse a ion in o a con inuous, p edic i e, and
highly au oma ed sa e y in elligence amewo k.
Keywo ds:
Su ace-mine moni o ing; Slope s abili y; D one sensing sys ems; C ack de ec ion; LiDAR mapping; P edic i e
geo echnics
1. INTRODUCTION
1.1 Backg ound on Slope S abili y in Su ace Mines
Slope s abili y is a c i ical de e minan o ope a ional sa e y and p oduc ion con inui y in su ace mines, whe e
highwall ailu es, bench collapses, and la ge-scale slope de o ma ions can lead o ca as ophic consequences [1].
As exca a ion p og esses, he mechanical balance o soil and ock masses e ol es, in luenced by li hological
a iabili y, g oundwa e p essu e, blas ing ib a ions, and exca a ion geome y [2]. These ac o s in e ac in
complex ways, c ea ing ins abili y condi ions ha may p og ess g adually o eme ge suddenly. Mode n mines
ou inely push o g ea e dep hs and s eepe angles o main ain economic e iciency, inc easing geomechanical
s esses and educing na u al suppo s uc u es [3]. Wea he ing, ain all in il a ion, and empe a u e cycles
u he weaken slope cohesion, accele a ing c ack p opaga ion and join sepa a ion [4]. Consequen ly, p ecise and
con inuous moni o ing is essen ial o de ec p ecu so y de o ma ion signa u es be o e hey escala e in o la ge-
scale ailu es. T adi ional geo echnical p ac ice emphasizes pe iodic su eying, g ound-based ins umen s, and
isual inspec ions, bu hese app oaches a e inc easingly inadequa e o cap u ing he as -e ol ing na u e o slope
beha io in la ge open pi s [5]. Ad anced moni o ing solu ions a e he e o e necessa y o suppo sa e mine
planning, p oac i e haza d mi iga ion, and mo e esilien ope a ional s a egies.
1.2 Limi a ions o T adi ional Moni o ing Technologies
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While con en ional moni o ing sys ems such as o al s a ions, p isms, ex ensome e s, and g ound-based ada
emain cen al o geo echnical su eillance, hey ace signi ican cons ain s in spa ial co e age, empo al
esolu ion, and esponsi eness [6]. Poin -based ins umen s p o ide highly accu a e de o ma ion da a bu only a
disc e e loca ions, making i di icul o iden i y dis ibu ed ins abili y pa e ns ac oss wide slopes [7]. Many
ins umen s equi e clea line-o -sigh and s able en i onmen al condi ions, which can be dis up ed by og, dus ,
blas ing, and changing opog aphy [8]. Visual inspec ions expose pe sonnel o haza dous a eas and depend hea ily
on subjec i e in e p e a ions [9]. G ound-based ada sys ems o e b oade co e age bu a e cos ly, equi e ixed
ins alla ion poin s, and s uggle in complex geome ies whe e benches, amps, and be ms block di ec beam pa hs
[10]. Mos impo an ly, adi ional sys ems a e no inhe en ly designed o con inuous, high- equency moni o ing,
esul ing in delayed de ec ion o apid o nonlinea de o ma ion ends [8]. Wi h mines expanding in scale and
complexi y, hese limi a ions can lea e c i ical gaps in si ua ional awa eness, emphasizing he need o mo e
adap i e, mobile, and high- esolu ion moni o ing pla o ms.
1.3 Rise o D one-Based Geo echnical In elligence Sys ems
D one-enabled moni o ing sys ems ha e eme ged as a ans o ma i e solu ion o cap u ing high- esolu ion
geo echnical da a ac oss as and di icul - o-access slope en i onmen s. Equipped wi h LiDAR, pho og amme y,
mul ispec al imaging, and he mal senso s, d ones can apidly gene a e de ailed su ace models ha e eal c acks,
discon inui ies, ension zones, and ea ly de o ma ion indica o s [5]. Unlike s a ic ins umen s, d ones o e
unpa alleled mobili y, enabling ou ine o on-demand su eys ha adap o e ol ing mine geome ies wi hou
exposing pe sonnel o isk [1]. Thei abili y o collec dense 3D poin clouds and spec al signa u es suppo s
au oma ed de ec ion o haza dous zones using machine-lea ning algo i hms and geospa ial analy ics [9]. D one-
de i ed da ase s also in eg a e seamlessly wi h slope-s abili y models, imp o ing p edic ions o ailu e
mechanisms and enhancing mine planning decisions [7]. As au onomy, senso pe o mance, and onboa d
p ocessing con inue o ad ance, d ones a e becoming cen al o nex -gene a ion geo echnical in elligence
amewo ks ha suppo con inuous moni o ing, apid haza d ecogni ion, and da a-d i en enginee ing
in e en ions [4].
2. DRONE SENSING AND GEOSPATIAL MAPPING FOUNDATIONS
2.1 Sensing Modali ies o Highwall and Bench-Face Assessmen
Accu a e sensing is undamen al o d one-enabled slope moni o ing, as highwalls and bench aces exhibi spa ially
complex de o ma ion pa e ns in luenced by geology, exca a ion geome y, wea he ing, and ope a ional s ess.
Mode n d ones in eg a e mul iple sensing modali ies o cap u e geome ic, spec al, and he mal cha ac e is ics o
slope su aces a esolu ions ha exceed hose o g ound-based ins umen s [6]. These sensing sys ems allow
enginee s o iden i y ension c acks, block sepa a ions, di e en ial displacemen zones, and haza dous loosened
ma e ial while also e ealing sub le p ecu so s ha may e ol e in o la ge-scale collapses.
LiDAR emains he p ima y ool o ex ac ing p ecise slope geome y, while mul ispec al and hype spec al
senso s p o ide mine alogical and wea he ing diagnos ics essen ial o ma e ial-weakening assessmen [11].
The mal imaging adds an en i onmen al dimension by de ec ing empe a u e g adien s a ec ed by mois u e
in il a ion, en ila ion hea loss, o sun exposu e, all o which may co ela e wi h s uc u al de e io a ion [8].
By s i ching oge he mul iple da a laye s LiDAR poin clouds, spec al e lec ance signa u es, and he mal
dis ibu ions d ones c ea e comp ehensi e geo echnical da ase s wi h bo h spa ial and ma e ial con ex . These
composi e da ase s suppo au oma ed c ack de ec ion, block classi ica ion, and wea he ing analyses, imp o ing
he eliabili y o ea ly-wa ning sys ems o slope ins abili y [12]. When pai ed wi h machine-lea ning models, he
senso s can de ec pa e ns ha human obse e s may o e look, enabling con inuous e inemen o haza d
p edic ions as new da a accumula es [15].
The mul i-senso capabili ies o d ones also o e come he limi a ions o s a ic moni o ing ools by o e ing
lexible, epea able co e age ac oss ex ensi e pi walls and benches. This holis ic sensing app oach ul ima ely
s eng hens he accu acy and p edic i e alue o slope-in eg i y assessmen s, enabling mine ope a o s o in e ene
p oac i ely and educe sa e y isks [16].
2.1.1 LiDAR o High-Resolu ion Slope Geome y and C ack Mo phology
LiDAR sys ems p o ide cen ime e -le el accu acy in cap u ing slope geome y, making hem indispensable o
mapping benches, aul sca ps, and shea planes wi h high ideli y. By emi ing apid lase pulses and measu ing
hei e u n imes, LiDAR econs uc s de ailed 3D poin clouds ha ep esen he ue su ace mo phology o
exposed ock aces, e en in a eas ha a e shadowed o haza d-p one [9]. These poin clouds highligh ension
c acks, block edges, de o ma ion bel s, and small-scale dis o ions ha o en p ecede la ge ailu es.
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LiDAR da a can be compa ed ac oss mul iple d one ligh s o quan i y displacemen o e ime, e ealing c ack
widening, bench- ace bulging, and ock-mass elaxa ion zones [14]. Such empo al di e encing suppo s
p edic i e modelling, enabling geo echnical enginee s o iden i y apidly e ol ing ins abili y condi ions be o e
hey escala e.
2.1.2 Mul ispec al and Hype spec al Imaging o Ma e ial Wea he ing
Mul ispec al and hype spec al senso s de ec a ia ions in mine alogy, mois u e con en , and wea he ing
in ensi y by cap u ing e lec ance ac oss mul iple wa eleng hs. These a ia ions, in isible o s anda d RGB
came as, e eal ea ly indica o s o ock weakening such as oxida ion, clay o ma ion, o mine al al e a ion linked
o p olonged exposu e and hyd o he mal e ec s [10]. Re lec ance anomalies can also highligh decomposed ock
o disagg ega ed ma e ial ha may beha e as ailu e-p one zones du ing ain all e en s o blas ing cycles [13].
Hype spec al imaging, in pa icula , dis inguishes sub le spec al signa u es associa ed wi h al e a ion mine als,
enabling de ailed classi ica ion o high- isk li hological uni s along slope aces [6]. This ma e ial-le el in elligence
suppo s mo e accu a e geo echnical modelling and a ge ed ein o cemen s a egies.
2.1.3 The mal Imaging o Mois u e In usion and Hidden F ac u e Indica o s
The mal imaging senso s de ec empe a u e g adien s ha e eal hidden mois u e in il a ion, en ila ion
in luence, o ac u e p opaga ion. Mois u e-laden ock ypically e ains hea di e en ly han d y ock, c ea ing
he mal con as s de ec able by d one-moun ed senso s [15]. F ac u es o oids may simila ly appea as anomalous
cooling o hea ing zones due o ai low ci cula ion. These indica o s help iden i y in e nal weaknesses no isible
a he su ace [7].
Figu e 1: O e iew o Key D one Senso Modali ies o Su ace-Mine Slope Moni o ing.
2.2 Na iga ion and Fligh -S abili y Requi emen s o S eep Slopes
E ec i e slope moni o ing equi es d ones o ope a e sa ely and accu a ely in s eep, con ined, and
ae odynamically uns able en i onmen s. Highwalls gene a e complex ai low condi ions, including upd a s,
wind-shea pocke s, and empe a u e-d i en u bulence ha challenge ligh s abili y [9]. Na iga ion accu acy is
equally c i ical, as d ones mus main ain consis en s and-o dis ance and epea able ligh pa hs o ensu e
geome ic da a is compa able ac oss mul iple su eys [11].
Te ain-awa e na iga ion sys ems enable d ones o ollow s eep bench p o iles p ecisely while adjus ing o
sudden changes in opog aphy. Mul i-di ec ional obs acle-a oidance senso s help p e en collisions wi h
p o uding ock ledges o blas ing-induced deb is [14].
Na iga ion eliabili y also depends on compensa ing o inconsis en sunligh , mo ing shadows, and dus clouds,
all o which can dis o isual-based algo i hms. As a esul , mode n slope-moni o ing d ones ely on hyb id
posi ioning s a egies ha combine isual–ine ial odome y (VIO), LiDAR-SLAM, and ine ial-senso da a o
main ain accu a e ajec o ies e en when op ical cues deg ade [12].
2.2.1 Visual–Ine ial SLAM and Te ain-Adap i e Posi ioning
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Visual–ine ial SLAM in eg a es came a image y wi h ine ial-measu emen -uni (IMU) da a o es ima e d one
posi ion in eal ime. Howe e , s eep slopes equen ly in oduce challenges such as low- ex u e su aces, deep
shadows, and isually homogeneous ock aces ha limi SLAM pe o mance [16]. To add ess his, d ones
inco po a e e ain-adap i e ligh logic ha dynamically adjus s al i ude and o ien a ion o main ain op imal
ea u e isibili y [6].
When pai ed wi h LiDAR-SLAM, hese sys ems ensu e s able localiza ion e en in dus y o low-ligh condi ions,
enabling consis en slope- ace co e age and high-quali y da a cap u e [13].
2.2.2 Wind-Shea Compensa ion, Upd a Handling, and Ho e P ecision
S eep highwalls c ea e wind-shea zones and upwa d he mal columns ha des abilize ligh weigh d ones.
Ad anced ligh con olle s use p edic i e ae odynamics and IMU eedback loops o coun e ac sudden gus s,
main aining smoo h ligh ajec o ies [10]. Ho e s abili y is especially impo an when cap u ing c ack
mo phologies, as e en small posi ional d i can dis o measu emen s.
Adap i e h us modula ion helps compensa e o upd a s, while edundan s abiliza ion senso s educe
oscilla ions ha migh deg ade LiDAR o mul ispec al measu emen s [8]. P ecision ho e capabili y ensu es
epea able imaging ac oss su eys, imp o ing empo al de o ma ion modelling.
2.3 En i onmen al and Ope a ional Cons ain s
En i onmen al condi ions s ongly in luence senso pe o mance, ligh planning, and da a quali y. Sun angle,
ai bo ne pa icula es, empe a u e ex emes, and mechanical ib a ion all con ibu e o measu emen unce ain y
[7]. Ope a o s mus ca e ully synch onize ligh s wi h a o able en i onmen al condi ions o ely on co ec ion
algo i hms o compensa e o dis o ion and noise.
Dus gene a ed du ing hauling o blas ing can obs uc came as, sca e LiDAR pulses, and deg ade mul ispec al
accu acy [11]. Ex eme hea accele a es ba e y deple ion and a ec s IMU calib a ion, while cold condi ions
educe powe ou pu and al e senso beha io [9]. Unde s anding hese cons ain s is essen ial o ensu ing
consis en da a in eg i y.
2.3.1 Sun-Shadow Va iabili y, Dus , and Re lec ance Dis o ion
Su ace mines expe ience in ense sun-shadow con as due o s eep slopes and deep benches. These ligh ing
a ia ions complica e pho og amme y, dis o e lec ance eadings, and educe mul ispec al eliabili y [12]. Dus
clouds add u he noise, obscu ing c acks and sca e ing op ical signals. Compensa ion equi es eal- ime
exposu e co ec ion and pos -p ocessing no maliza ion [6].
2.3.2 Tempe a u e Ex emes, Ba e y Load, and Senso Deg ada ion
High empe a u es educe ba e y endu ance and can shi he mal-senso baselines, while cold en i onmen s slow
chemical eac ions inside ba e ies and IMUs [14]. Con inuous ib a ion du ing long ligh s accele a es senso
d i and educes calib a ion s abili y. Ensu ing he mal egula ion and ib a ion isola ion is he e o e undamen al
o eliable slope-assessmen missions [16].
3. HIGH-FIDELITY SLOPE MAPPING AND CRACK-DETECTION WORKFLOWS
3.1 Geome ic Modelling and Su ace Recons uc ion
Geome ic modelling is cen al o d one-enabled slope assessmen , p o iding a high- ideli y ep esen a ion o
bench aces, highwall con ou s, and e ol ing s uc u al discon inui ies. By in eg a ing dense poin clouds, su ace-
mesh econs uc ions, and e ain-de i ed me ics, d ones acili a e an analy ical unde s anding o slope geome y
ha su passes capabili ies o g ound-based ools [14]. These models no only cap u e he isual appea ance o he
ock mass bu also encode i s geome ic beha io , enabling p ecise calcula ions o slope angles, cu a u e p o iles,
oid o ma ions, and block ou lines.
Poin -cloud da ase s gene a ed om LiDAR and pho og amme y mus unde go sys ema ic p ocessing o emo e
noise, co ec o occlusions, and enhance spa ial consis ency ac oss ligh missions [16]. This includes no mal-
ec o es ima ion, neighbo hood il e ing, and s a is ical mapping o poin densi y. Once e ined, hese clouds
se e as he ounda ion o mo e ad anced modelling wo k lows, such as mesh econs uc ion and olume ic
in e p e a ion.
Su ace econs uc ion ans o ms disc e e poin s in o con inuous digi al su aces ha e eal ac u e bounda ies,
aspe i y s uc u es, and de o ma ion-linked oughness pa e ns. These econs uc ed su aces can be analyzed o
iden i y bench s eepening, e osion zones, o ension c acks ha a e no immedia ely e iden in aw da a [19].
Su ace oughness mapping is pa icula ly aluable, as oughness co ela es s ongly wi h wea he ing in ensi y,
blas dis u bance, and long- e m s uc u al a igue.
In addi ion, slope-angle de i a ion de i ed om econs uc ed geome y o e s a quan i a i e pe spec i e on
geo echnical isk. S eep o i egula slopes may e lec o e -exca a ion, blas -induced dila ion, o g a i a ional
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elaxa ion, all o which inc ease ins abili y po en ial [22]. By compu ing angle changes o e ime, d one-based
geome ic modelling allows enginee s o de ec sub le p ecu so s o ailu e.
When combined, densi ied poin clouds, mesh su aces, and de i ed me ic laye s c ea e a mul i-dimensional
geome y amewo k ha enhances bo h immedia e haza d de ec ion and long- e m s abili y o ecas ing ac oss
su ace-mine slopes [24].
3.1.1 Poin -Cloud Densi ica ion and Noise Fil e ing
Poin -cloud densi ica ion inc eases spa ial de ail by in e pola ing addi ional poin s wi hin spa se egions while
p ese ing geome ic au hen ici y. This p ocess is essen ial o cap u ing na ow c acks, hin block edges, and
sub le undula ed su aces ypical o exposed highwalls [17]. Noise il e ing emo es e oneous e u ns caused by
dus , sunligh in e e ence, o e lec i e mine al su aces. Techniques such as s a is ical ou lie emo al, adius-
based noise supp ession, and oxeliza ion e ine he da ase in o a clean, analyzable s uc u e [20].
Fil e ed and densi ied poin clouds enable p ecise segmen a ion o s uc u al ea u es, s eng hening downs eam
wo k lows such as ac u e mapping, mesh c ea ion, and de o ma ion modelling [14]. These p ocessed da ase s
also suppo mul i- empo al alignmen when compa ing successi e d one su eys.
3.1.2 Mesh Recons uc ion, Su ace Roughness Modelling, and Slope-Angle De i a ion
Mesh econs uc ion con e s poin clouds in o con inuous, opologically consis en su aces ha app oxima e he
physical geome y o slope aces. Algo i hms such as Poisson econs uc ion o Delaunay iangula ion gene a e
iangula meshes ha accu a ely cap u e ac u es, ledges, and p o usions [19]. These meshes suppo de ailed
geo echnical analyses, pa icula ly when compu ing cu a u e, block ou lines, and shea -plane geome ies.
Su ace oughness modelling uses local neighbo hood compa isons o quan i y mic o- a ia ions in wall ex u e.
Highe oughness may indica e blas damage, wea he ing-induced disin eg a ion, o ac u ing along weak mine al
seams [23]. Roughness g adien s ac oss he slope can also highligh a eas expe iencing ac i e su ace deg ada ion
o s uc u al a igue.
Slope-angle de i a ion in ol es calcula ing dip and dip-di ec ion ields ac oss he econs uc ed mesh. Sha p
inc eases in slope angle may e eal zones o unde cu ing o g a i a ional elaxa ion, while angle la ening may
indica e ma e ial loss o e osion [24]. These me ics assis enginee s in iden i ying haza dous geome ies long
be o e isual signs o ailu e eme ge.
Table 1: Compa ison o Geospa ial Recons uc ion Me hods o Slope-Moni o ing Applica ions
Me hod
Da a Sou ce
Key S eng hs
Key Limi a ions
LiDAR Recons uc ion
D one LiDAR
High geome ic accu acy; excellen
c ack de ec ion
La ge iles; sensi i e o
e lec i e su aces
Pho og amme ic (S M)
RGB image y
Low-cos ; high ex u e de ail
Ligh ing-dependen ; lowe
dep h accu acy
T iangula ed Mesh
Models
LiDAR o S M
poin clouds
Con inuous su aces; use ul o slope-
angle and oughness
Requi es smoo hing;
a i ac s i da a spa se
Voxel-Based Volumes
Dense poin
clouds
Use ul o block modelling and oid
de ec ion
Lowe su ace de ail; high
s o age needs
Hype spec al Su ace
Mapping
Spec al image
cubes
Iden i ies al e a ion/wea he ing
Lowe geome ic p ecision;
calib a ion equi ed
3.2 Ea ly-S age C ack De ec ion Using Mul i-Senso Da ase s
Ea ly-s age c ack de ec ion is among he mos c i ical applica ions o d one-based geo echnical moni o ing, as
mic o- ac u es o en p ecede la ge slope mo emen s ha escala e in o bench ailu e o ock alls [16]. D ones
equipped wi h LiDAR, mul ispec al, hype spec al, and he mal senso s p o ide complemen a y da ase s capable
o de ec ing c acks a mul iple scales and unde di e se en i onmen al condi ions.
LiDAR poin clouds highligh discon inui ies h ough ab up changes in ele a ion g adien s o su ace no mals,
while pho og amme y emphasizes isual c ack edges and shadowing pa e ns. Mul ispec al and hype spec al
senso s enhance de ec ion by iden i ying mine alogical and mois u e a ia ions aligned wi h de eloping ac u es,
and he mal senso s de ec empe a u e di e en ials caused by mois u e seepage o ai low h ough c acks [21].
Combining hese sensing modali ies s eng hens obus ness, especially in low-con as geological se ings whe e
c acks may blend in o su ounding ex u es. Machine-lea ning edge de ec o s, mo phological il e s, and
cu a u e-based ea u e ex ac ion allow au oma ed iden i ica ion o high- isk c ack zones [18]. When applied

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consis en ly ac oss su ey cycles, hese mul i-senso wo k lows build a empo al eco d o c ack ini ia ion,
g ow h, and coalescence an essen ial inpu o p edic i e modelling.
3.2.1 Edge-Segmen a ion and Fea u e-Ex ac ion Techniques
Edge-segmen a ion echniques iden i y c ack bounda ies by analyzing g adien s, in ensi y changes, and su ace
i egula i ies wi hin poin clouds and image y. Algo i hms such as Canny il e ing, Sobel segmen a ion, and
Laplacian-o -Gaussian me hods de ec ab up discon inui ies ha signal c ack o ma ion [15].
Fea u e ex ac ion enhances segmen a ion by inco po a ing LiDAR-de i ed no mal ec o s, cu a u e ields, and
dep h discon inui ies. These ea u es help di e en ia e ue c acks om noise a i ac s caused by dus o su ace
oughness [22]. Combined wi h machine-lea ning classi ie s, hese ea u es d ama ically inc ease de ec ion
accu acy.
3.2.2 Spec al, The mal, and Mois u e-G adien Indica o s o Mic o-C acks
Spec al imaging de ec s wa eleng h-speci ic e lec ance pa e ns linked o mine al al e a ion o oxida ion inside
de eloping c acks. Hype spec al senso s iden i y sub le abso p ion ea u es indica ing clay mine als, i on- ich
al e a ion, o mois u e p esence common ma ke s o weakening ock [14].
The mal imaging complemen s spec al indica o s by e ealing hea - e en ion anomalies c ea ed by mois u e-
illed ac u es o ai low exchange wi hin open c acks [20]. Mois u e- ich zones ypically appea coole , enabling
he mal senso s o highligh c ack pa hways e en when isually hidden.
Mois u e-g adien mapping de i ed om he mal-spec al usion u he enhances mic o-c ack iden i ica ion.
Localized humidi y inc eases o en occu in ac u es ha ac as wa e condui s, especially a e ain all o blas ing
e en s [24]. These en i onmen al signa u es p o ide ea ly-wa ning indica o s o ma e ial deg ada ion and
po en ial slope ins abili y.
3.3 Classi ica ion and Tempo al Change De ec ion
Classi ica ion and empo al change de ec ion con e aw c ack obse a ions in o ac ionable geo echnical
in elligence. Mode n d one da ase s allow sys ema ic ca ego iza ion o c ack se e i y, geome y, and p opaga ion
beha io , enabling enginee s o p io i ize in e en ion in high- isk zones [19].
Machine-lea ning classi ie s dis inguish be ween ensile c acks, shea c acks, ex olia ion su aces, and blas -
induced ac u es based on geome y, spec al composi ion, and he mal signa u es [17]. Once classi ied, c ack
ea u es can be acked ac oss successi e d one ligh s o de ec widening, elonga ion, o o a ion consis en wi h
p og essi e ailu e mechanisms.
Tempo al change de ec ion algo i hms compa e mul i- empo al poin clouds and image y o quan i y de o ma ion
along c ack lines. Mic o-displacemen s on he o de o millime e s can be de ec ed wi h high- esolu ion LiDAR,
making i possible o iden i y accele a ing de o ma ion well be o e mac oscopic ailu e occu s [23].
3.3.1 Machine-Lea ning Models o C ack Type and Se e i y Classi ica ion
Machine-lea ning models such as andom o es s, SVMs, and con olu ional neu al ne wo ks use geome ic and
spec al ea u es o classi y c acks by ype and se e i y. These models in e p e dep h con inui y, o ien a ion, edge
sha pness, e lec ance beha io , and he mal anomalies o di e en ia e be ween benign and haza dous c acks [21].
Classi ie s imp o e as aining da ase s expand, s eng hening p edic i e eliabili y.
3.3.2 Mul i-Tempo al De o ma ion T acking and C ack-P opaga ion Analysis
Mul i- empo al acking quan i ies c ack e olu ion using sequen ial d one su eys. Techniques such as cloud- o-
cloud di e encing, empo al cu a u e mapping, and displacemen - ec o analysis de ec millime e -scale
de o ma ion [24]. P opaga ion analysis e eals g ow h a es, di ec ional ends, and po en ial coalescence o
mul iple ac u es in o la ge ins abili y sys ems, suppo ing p edic i e modelling o slope ailu e p og ession.
4. PREDICTIVE ANALYTICS, DIGITAL TWINS, AND GEOTECHNICAL FORECASTING
4.1 Senso Fusion and Mul i-Laye Risk Models
Senso usion lies a he co e o ad anced d one-enabled slope- ailu e p edic ion, allowing mul iple sensing
modali ies o be combined in o a uni ied ep esen a ion o geo echnical isk. Highwall ins abili y a ely eme ges
om a single measu able ac o ; ins ead, i esul s om he in e play o s uc u al geome y, li hological
weakening, mois u e in usion, he mal g adien s, and p og essi e de o ma ion pa e ns. D one sys ems equipped
wi h LiDAR, mul ispec al came as, he mal image s, and en i onmen al senso s gene a e complex da ase s ha
indi idually cap u e ace s o ins abili y bu achie e hei g ea es p edic i e alue when syn hesized in o mul i-
laye isk models [22].
A used isk amewo k aligns geome ic me ics such as slope angle, cu a u e, oughness, and c ack mo phology
wi h spec al indica o s o mine al al e a ion, he mal signa u es o mois u e pa hways, and en i onmen al
a iables like ambien humidi y o su ace empe a u e luc ua ions. These in eg a ed laye s highligh co ela ions
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ha would be di icul o disce n independen ly. Fo example, a zone exhibi ing bo h ele a ed he mal-cooling
anomalies and hype spec al mois u e signa u es may indica e a subsu ace ac u e ac ing as a wa e condui ,
which is a p ecu so o po en ial ock-mass deg ada ion [25].
Machine-lea ning classi ie s enhance he senso - usion p ocess by lea ning om his o ical slope ailu es,
iden i ying mul i- a iable pa e ns ha co ela e s ongly wi h ins abili y. These models weigh he ela i e
impo ance o ea u es such as c ack densi y, de o ma ion ec o s, oxida ion ma ke s, and mic o-clima ic
a iabili y [27]. The esul ing mul i-laye isk classi ica ion maps indica e zones o ele a ed ins abili y p obabili y
and suppo ea ly-wa ning decision sys ems o mine ope a ions.
O e all, senso usion ans o ms aw d one da a in o a sophis ica ed, mul i-dimensional ep esen a ion o slope
heal h. By uni ying geome y, ma e ial condi ion, and en i onmen al beha io , hese used isk models c ea e a
obus ounda ion o p edic i e analy ics and geo echnical o ecas ing ac oss la ge and complex su ace-mine
en i onmen s [30].
4.1.1 In eg a ing LiDAR, Mul ispec al, The mal, and En i onmen al Da a
In eg a ing he e ogeneous da ase s equi es ha monizing spa ial esolu ions, co ec ing adiome ic
inconsis encies, and aligning senso ou pu s so ha each laye con ibu es meaning ully o he combined isk
model. LiDAR supplies s uc u al geome y c ack openings, discon inui y o ien a ions, and de o ma ion pa e ns
while mul ispec al imaging e eals mine alogical weakening associa ed wi h oxida ion o clay- ich zones p one
o slippage [24].
The mal imaging con ibu es a dynamic laye by iden i ying mois u e e en ion and ai low-d i en empe a u e
g adien s, o en e ealing hidden ac u es no seen in op ical da ase s [26]. En i onmen al senso s add con ex ual
da a such as humidi y, sola loading, and su ace empe a u e luc ua ions ha can a ec ma e ial s eng h o
accele a e wea he ing p ocesses.
When used, hese inpu s suppo pixel- o poin -le el a ibu e s acking, allowing machine-lea ning algo i hms
o e alua e slope condi ions holis ically a he han h ough isola ed indica o s [28].
4.1.2 Gene a i e and Physics-Guided AI Models o Failu e P obabili y Es ima ion
Gene a i e and physics-guided AI models enhance ailu e-p obabili y es ima ion by combining empi ical d one
da a wi h geomechanical p inciples. Gene a i e models simula e housands o po en ial ailu e s a es using
obse ed c ack g ow h, displacemen a es, and ma e ial-wea he ing pa e ns [22].
Physics-guided neu al ne wo ks inco po a e ounda ional ock-mechanics equa ions such as Moh -Coulomb
ailu e c i e ia, shea -s eng h en elopes, and s ess- edis ibu ion pa e ns o cons ain model ou pu s wi hin
physically plausible bounda ies [29]. This hyb id app oach p e en s un ealis ic p edic ions while main aining
adap abili y o si e-speci ic d one da a.
The esul is a p obabilis ic isk su ace ha maps he likelihood o localized o la ge-scale ailu e ac oss benches
and highwalls [25].
Table 2: P edic i e Indica o s o Slope Failu e and Thei D one-De i ed Da a Sou ces
P edic i e Indica o
D one-De i ed Da a Sou ce
Wha I Re eals
C ack Widening &
P opaga ion
Mul i- empo al LiDAR poin
clouds
Accele a ing de o ma ion and ac u e
g ow h
The mal Cooling/Hea ing
Anomalies
D one he mal imaging
Mois u e ing ess, hidden ac u es, o
en ila ion pa hways
Ma e ial Wea he ing
Signa u es
Mul ispec al / hype spec al
e lec ance
Oxida ion, clay o ma ion, and weakened
ock zones
Su ace Roughness Inc ease
LiDAR-de i ed oughness
me ics
Blas damage, s ess elaxa ion, o su ace
deg ada ion
Bench o Highwall
Displacemen
Cloud- o-cloud geome ic
di e encing
Ea ly-s age slope mo emen and block
ins abili y
4.2 Digi al-Twin Slope Simula ion Models
Digi al- win en i onmen s ex end he capabili ies o senso - usion models by c ea ing con inuously upda ed
i ual eplicas o mine slopes. These wins in eg a e geome ic models, ma e ial classi ica ions, mic o-clima e
a iables, and mul i- empo al de o ma ion his o ies in o a dynamic simula ion ha e ol es in sync wi h d one
obse a ions [23]. By upda ing in nea eal ime, he digi al win mi o s he ac ual slope en i onmen , allowing
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geo echnical eams o isualize ac i e de o ma ion, iden i y zones o accele a ing s ain, and simula e po en ial
ailu e ou comes.
D one-de i ed de o ma ion da a plays a c ucial ole. Mul i- empo al poin -cloud compa isons eed in o nume ical
modelling amewo ks ini e-elemen models (FEM), disc e e-elemen models (DEM), o hyb id FEM-DEM
couplings ha simula e s ess dis ibu ion and ailu e modes unde di e en loading condi ions [30]. Digi al wins
also suppo scena io analysis by allowing enginee s o es in e en ions such as scaling, ein o cemen , d ainage
imp o emen s, o exca a ion-sequence adjus men s be o e implemen ing hem ope a ionally.
Hea -map o e lays, spec al-wea he ing classi ica ions, and mois u e-de i ed isk laye s help p edic whe e
ac u es may p opaga e o whe e blocks may de ach. These p edic ion laye s no only enhance s a egic planning
bu also suppo day- o-day haza d mi iga ion by highligh ing a eas equi ing immedia e inspec ion o exclusion
[24].
4.2.1 Upda ing Geomechanical Models wi h D one-Based De o ma ion Inpu s
Geomechanical models adi ionally ely on pe iodic su ey da a o isola ed ins umen eadings. Using d one-
de i ed de o ma ion inpu s, digi al wins upda e hei in e nal bounda y condi ions and s ess-s a e ep esen a ions
wi h high empo al equency [28]. Millime e -le el displacemen changes, c ack elonga ion, bench-c es
subsidence, and wall- ace bulging a e inco po a ed in o FEM o DEM simula ions o e ine p edic ions o po en ial
sliding, oppling, o wedge ailu es [22].
4.2.2 Scena io Simula ion and Risk-Hea map Visualiza ion
Scena io simula ion models p edic how slopes espond o en i onmen al igge s including ain all, blas ing
ib a ions, o high empe a u es [30]. Ou pu s a e isualized as dynamic isk hea maps, which encode haza d
in ensi y using colo g adien s ac oss he slope su ace [27]. These hea maps help supe iso s quickly iden i y
egions equi ing moni o ing, isola ion, o ein o cemen . Enginee s can also compa e scena ios such as inc eased
g oundwa e p essu e o al e ed exca a ion sequences o de e mine he mos s able ope a ional pa hways o mine
planning [25].
Figu e 2: Digi al-Twin Wo k low o Slope Failu e Fo ecas ing Using D one Da a.
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4.3 In eg a ion wi h Mine-Wide Sa e y and Dispa ch Sys ems
In eg a ing p edic i e slope- ailu e analy ics wi h mine-wide sa e y and dispa ch sys ems ans o ms d one ou pu s
in o ac ionable ope a ional in elligence. Da a om digi al- win models and senso - usion isk laye s eeds di ec ly
in o sa e y pla o ms ha moni o h esholds, e alua e haza d se e i y, and igge au oma ed in e en ions [23].
Dispa ch sys ems inco po a e hese ou pu s o e ou e equipmen , es ic wo ke access, and op imize exca a ion
schedules in esponse o eme ging geo echnical isks.
By embedding p edic i e analy ics in o cen alized con ol ooms, mines achie e eal- ime si ua ional awa eness,
imp o ing bo h sa e y and p oduc i i y.
4.3.1 Au oma ed Ale s and Slope-Haza d T igge Th esholds
Au oma ed ale s ac i a e when moni o ed indica o s c ack p opaga ion a e, he mal anomalies, slope-angle
de ia ion, o de o ma ion accele a ion c oss p ede ined h esholds [29]. These igge s, in o med by d one da a,
ensu e apid escala ion o haza dous condi ions and enable ea ly in e en ion p ocedu es such as con olled
scaling o e acua ion [24].
4.3.2 Real-Time Dashboa ding and Ope a ional Decision Suppo
Real- ime dashboa ds isualize mul i-laye slope condi ions using in eg a ed de o ma ion maps, spec al
indica o s, and ailu e-p obabili y su aces. Ope a o s ecei e decision ecommenda ions such as haul- oad
e ou ing, emo e-equipmen eassignmen , o a ge ed geo echnical inspec ions [26]. These dashboa ds link d one
in elligence wi h ope a ional wo k lows, imp o ing bo h esponsi eness and planning accu acy.
5. DEPLOYMENT, OPERATIONAL INTEGRATION, AND REGULATORY CONSIDERATIONS
5.1 Mission Planning and Ope a ional F amewo ks
E ec i e mission planning is essen ial o ensu ing eliable, epea able, and sa e d one ope a ions ac oss su ace-
mine slopes. Slope-moni o ing missions equi e delibe a e s uc u ing o ligh pa hs, senso -ac i a ion schedules,
ai -sa e y bounda ies, and g ound-suppo p o ocols o gua an ee da a consis ency while minimizing ope a ional
isk. Mines ypically ope a e unde dynamic condi ions blas ing, haul- uck mo emen , bench cons uc ion, and
a iable wea he each o which may in luence he sa e y and quali y o d one ligh s [28]. As a esul , ope a ional
amewo ks mus inco po a e adap i e planning ools capable o adjus ing missions based on en i onmen al shi s,
scheduling cons ain s, and e ol ing slope geome ies.
Each mission begins wi h de ining highwall-speci ic ligh co ido s ha accoun o bench o ien a ion, s and-o
dis ances, and isibili y equi emen s o LiDAR, pho og amme ic, and spec al senso s. These pa ame e s
suppo eliable su ace econs uc ion and c ack de ec ion. Addi ional planning ac o s include ensu ing he
d one’s line-o -sigh o mission-c i ical ea u es, moni o ing GPS mul ipa h in e e ence nea s eep ock aces,
and es ablishing al e na e e u n- o-home (RTH) poin s o mi iga e unexpec ed wind shea o ba e y d ain [30].
Mission planning amewo ks also inco po a e eal- ime moni o ing o d one eleme y ba e y s a e, wind load,
obs acle wa nings, and ine ial d i . Li e acking enables ope a o s o in e ene o abo missions when
en i onmen al condi ions de ia e om allowable limi s. Ope a ional amewo ks ypically in eg a e con ingency
p o ocols o eme gency landings, signal loss, o o e hea ing e en s ha may occu due o sus ained sunligh
exposu e on exposed highwalls [33].
S anda dized wo k lows ensu e da a uni o mi y ac oss missions. Mines o en deploy ligh - empla e lib a ies o
ou ine inspec ions o c i ical benches, enabling compa a i e mapping o e weeks o mon hs. Embedding hese
p ocedu es in o a uni o m ope a ional amewo k ensu es consis ency and educes human e o , a key ad an age
as mul i-senso missions become mo e complex. Such s uc u ed planning ensu es ha d one-enabled slope
moni o ing emains sa e, e icien , and echnically obus ac oss all su ace-mine en i onmen s [35].
5.1.1 Highwall Fligh Co ido s, Wea he Windows, and Sa e y P o ocols
Highwall moni o ing equi es d ones o ly wi hin na owly de ined co ido s pa allel o slope aces. These
co ido s main ain op imal s and-o dis ance o LiDAR beam accu acy and pho og amme ic cla i y while
p e en ing collisions wi h p o uding ledges o o e hangs [31]. Ope a o s mus accoun o sun angle, gla e, and
shadow mo emen , which in luence bo h SLAM na iga ion and c ack- isibili y quali y.
Sa e y p o ocols dic a e a oiding ligh ope a ions du ing pe iods o excessi e wind, he mal upd a s, o pos -
blas dus u bulence. Mines ypically es ablish wea he h esholds in ol ing maximum wind eloci y, dus -
pa icula e ceilings, and sola -load limi s o p e en ha dwa e o e hea ing and senso dis o ion [29]. Manda o y
p e- ligh isk assessmen s ensu e wo ke s a e excluded om ac i e ligh zones o minimize o e head-in e ac ion
haza ds.
5.1.2 Mul i-D one Coo dina ion o La ge-Bench and Mul i-Slope Co e age