Scanning Slopes with the DREVO AeroSense Drone

Aerial diagnostics of terrain, water, vegetation and geotechnical risks

TREVO AeroSense Drone— a specialized unmanned system for remote inspection of mountain slopes, ridges, upper catchment areas, spring zones, roads, and emergency channels.

The system is designed for more than just capturing photographs. Its primary purpose is to create a measurable digital model of the area, detect changes, and transmit the results to:

digital passport of the upper zone;

DREVO Mountain Digital Twin;

summit monitoring system;

DREVO Mountain Springs Recovery;

fire module;

early warning system;

Drone scanning produces orthophotomaps, point clouds, and digital surface and terrain models, and, with repeated flights, can detect changes in the position of slopes, roads, landslides, and riverbeds. Similar photogrammetry and LiDAR methods are used for highly detailed landslide mapping and comparison of their status over time.

The main principle:

The DREVO AeroSense Drone is designed to do more than just show the slope; it can also measure its geometry, temperature, vegetation, humidity anomalies, and changes over time.

1. Basic scanning tasks

The system solves the following groups of problems:

1.1 Topographic mapping

construction of a digital elevation model;

determination of slopes and exposure;

identification of ridges and watersheds;

search for depressions and microdepressions;

mapping of emergency water routes;

determination of the area of micro-catchments;

measurement of volumes of excavations, embankments and deposits.

1.2. Geotechnical survey

crack detection;

identification of landslide ledges;

scree mapping;

fixation of rockfall areas;

determination of road deformation;

search for new wet outlets;

comparison of slope positions between surveys.

1.3. Hydrological analysis

construction of surface runoff directions;

determination of water concentration points;

search for blocked channels;

mapping of waterlogged zones;

examination of springs;

Post-rainfall erosion analysis;

assessment of the filling of reservoirs and upper bowls.

1.4. Vegetation monitoring

assessment of overall coverage;

detection of stress areas;

dry biomass mapping;

search for invasive species;

Fire Recovery Analysis;

definition of windblow;

assessment of the condition of forest corridors.

1.5. Fire monitoring

thermal anomaly detection;

search for smoldering fires;

Post-lightning inspection;

dry vegetation assessment;

mapping of the burned area;

determination of the state of fire breaks.

2. Architecture of the complex

The DREVO AeroSense Drone consists of five main subsystems:

aerial platform;

navigation and geodetic system;

payload;

on-board computing module;

ground and cloud processing system.

The system can be implemented not by one universal device, but by several specialized configurations.

3. Configurations of the unmanned platform

3.1. DREVO AeroSense Survey

Lightweight mapping drone.

Intended for:

RGB photogrammetry;

orthophotography;

multispectral analysis;

regular monitoring;

surveys of small areas.

Advantages:

compactness;

rapid deployment;

low flight costs;

convenience of repeat flights.

3.2. WOOD AeroSense Pro

Medium industrial platform with interchangeable payload.

Can carry:

LiDAR;

thermal imager;

spectral chamber;

zoom camera;

laser rangefinder;

gas and aerosol sensors.

3.3. WOOD AeroSense Heavy

Heavy platform for:

high-precision LiDAR;

several synchronous sensors;

work on large watersheds;

long flight;

delivery of small autonomous sensors;

emergency inspection.

3.4. WOOD AeroSense VTOL

Vertical takeoff aircraft with airplane mode.

Suitable for:

long mountain corridors;

large watersheds;

forest areas;

surveys of hard-to-reach regions.

A multirotor platform is better at hovering and scanning a single slope, while a VTOL covers larger areas more effectively.

4. Navigation and geodetic reference

For engineering monitoring, conventional satellite navigation is not sufficient.

The system should include:

multi-band GNSS;

RTK or PPK;

inertial measurement unit;

barometric altimeter;

laser rangefinder;

ground control points;

synchronization of sensor time.

Commercial mapping systems already use RTK for centimeter positioning and precise georeferencing of photogrammetric data.

Checkpoints

Permanent benchmarks are being created on the territory:

geodetic marks;

identifiable panels;

metal or stone points;

GNSS benchmarks;

control objects near roads and landslides.

Repeated flights must use the same coordinate system.

5. RGB photogrammetry

The RGB camera is the basic sensor.

It is used for:

construction of an orthophotoplan;

creating a dense point cloud;

construction of a digital surface model;

road mapping;

gullies measurements;

crack detection;

fixation of vegetation;

visual inspection of structures.

The Structure from Motion method allows a series of overlapping images to create a 3D terrain geometry, a point cloud, an orthoimage, and a digital elevation model. The USGS uses this drone imagery processing to study landslides and surface changes.

Requirements for the RGB module

Preferred:

mechanical shutter;

global shutter or minimum rolling shutter;

high detail;

fixed lens calibration;

recording the exact exposure time;

RAW format;

Possibility of inclined shooting.

6. LiDAR scanning

LiDAR measures the distance to a surface using laser pulses and generates a three-dimensional point cloud.

Particularly important for:

forested slopes;

areas with complex terrain;

landslides;

rocky areas;

roads;

ravines;

after the fires.

LiDAR is capable of recording multiple reflections and partially obtaining information about the ground surface through gaps in vegetation. High-precision airborne LiDAR systems combine a laser scanner, GNSS, and inertial navigation.

Modern industrial modules can combine LiDAR, IMU, and an RGB camera into a single payload. For example, the DJI Zenmuse L2 combines a frame LiDAR, a high-precision IMU, and an RGB camera.

LiDAR Products

original point cloud;

classified cloud;

surface model;

earth model without vegetation;

vegetation height;

cross sections;

volumes of material;

roughness map;

local slope map.

7. Multispectral module

Multispectral imaging records reflection in multiple ranges.

Recommended channels:

blue;

green;

red;

red edge;

near infrared.

Existing compact industrial systems use separate green, red, red-edge and near infrared channels in conjunction with an RGB camera.

Main tasks

plant stress determination;

assessment of coating density;

search for areas of moisture deficiency;

Post-fire damage mapping;

distinguishing between vegetation types;

early detection of degradation;

control of planting restoration.

Calculation indices

Can be used:

NDVI;

HONOR;

GNDVI;

CLAY;

bare soil indices;

Burnt area indices.

The index should not be automatically interpreted as a diagnosis. The results must be verified by ground-based surveys.

8. Hyperspectral camera

The hyperspectral module records tens or hundreds of narrow spectral ranges.

It can be used for:

distinguishing closely related plant species;

identification of physiological stress;

determination of the composition of the soil surface;

search for mineralogical anomalies;

pollution mapping;

formation of spectral passports of plants.

Limitations:

large volume of data;

complex calibration;

dependence on lighting;

high cost;

the need for reference spectral libraries.

Therefore, the hyperspectral module is recommended for research and control missions, and not for every regular flight.

9. Thermal imaging module

A radiometric thermal imaging camera measures infrared radiation and allows the surface temperature of each pixel to be estimated.

Application

search for damp areas;

detection of spring outlets;

leak detection;

search for smoldering fires;

assessment of the thermal state of vegetation;

underground fire detection;

analysis of heating of rock surfaces;

inspection of solar installations and equipment.

Modern drone thermal systems can combine a radiometric thermal imager and a visible camera.

Limitations of thermography

The result depends on:

time of day;

solar heating;

winds;

air humidity;

surface emissivity;

observation angle;

cloudiness;

vegetation.

A thermal anomaly shows a temperature difference, but does not automatically explain its cause.

10. Zoom camera and tilt shooting

The zoom camera is used for spot inspection:

rock cracks;

bridges;

pipes;

landslide ledges;

windstorm;

fire outbreaks;

inaccessible structures.

Angled shooting is necessary because a vertical camera has poor visibility:

steep slopes;

rock walls;

lateral surfaces of gullies;

vertical cracks;

retaining walls;

entrances to culverts.

For a complex slope, missions with multiple camera angles are created.

11. Laser rangefinder

The laser rangefinder is used for:

measuring the distance to an object;

clarification of crack coordinates;

zoom camera guidance;

control of the distance to the slope;

determining the height of the cliff;

safe flight around the walls.

It is especially useful in manual inspection mode.

12. Electromagnetic and radar module

The DREVO AeroSense Pro concept can use a compact electromagnetic or radar sensor.

Possible tasks:

search for humidity contrasts;

detection of shallow cavities;

assessment of layering;

snow cover survey;

determination of the thickness of individual materials.

However, the capabilities of the airborne electromagnetic scanner must be assessed realistically.

A small drone does not replace:

ground penetrating radar survey;

seismic;

electrical resistivity tomography;

drilling;

full-fledged geophysics.

The drone sensor should be used as a means of preliminary detection of anomalies.

13. Acoustic and aerosol module

DREVO AeroSense can additionally measure:

aerosol particles;

smoke;

dust;

pollen;

disputes;

volatile compounds;

local sound anomalies.

In a mining project, this is useful for:

fire monitoring;

dust storm analysis;

erosion assessments;

pollution detection;

study of plant communities.

However, the airflow from the propellers distorts the measurements. The sensor must be moved out of the main propeller flow, or a hover mode, a sampling tube, or a separate gliding module must be used.

14. DREVO Aero AI Edge Onboard

The on-board computing module performs primary data processing directly during the flight.

Functions:

image quality control;

blur detection;

detection of missing areas;

crack detection;

search for thermal anomalies;

smoke detection;

vegetation classification;

assessment of the free cross-section of channels;

fixation of dangerous objects;

dynamic route change.

For example, if the camera detects a new crack, the drone can:

stop standard mission;

perform a circular flight;

photograph an object from different angles;

turn on the thermal imager;

measure the distance;

mark coordinates;

continue the main mission.

15. Scanning modes

15.1. Areal orthophotography

Flight on parallel routes over the area.

Results:

orthophotoplan;

surface model;

general status map.

15.2. Contour scanning of the slope

The drone follows the terrain at a constant distance from the surface.

Used on steep slopes.

15.3. Corridor survey

For:

roads;

stream;

combs;

forest corridors;

engineering networks.

15.4. Circular scanning

For a single object:

peaks;

landslide;

rocks;

reservoir;

structures.

15.5. Repeated route scanning

The route, altitude, speed and camera angles are repeated as accurately as possible.

This is critical for change analysis.

15.6. Emergency reconnaissance

Executed after:

downpour;

fire;

landslide;

storms;

earthquakes;

massive windfall.

16. Flight planning on a slope

On mountainous terrain it is impossible to set one constant absolute height.

Terrain following mode is required.

The planner takes into account:

digital elevation model;

vegetation height;

power lines;

rocky outcrops;

masts;

trees;

turbulence;

loss of GNSS;

no-communication zones;

emergency landing sites.

Frame overlap

For photogrammetry, longitudinal and transverse overlap are specified.

On steep, forested and difficult sections the overlap increases.

The exact value is selected based on:

relief;

lens;

height;

required detail;

vegetation;

wind.

17. Working in strong winds

Ridge flights require separate wind regulations.

Controlled by:

average speed;

gusts;

turbulence;

direction relative to the slope;

expected battery consumption;

the possibility of returning against the wind.

The flight is terminated if:

gusts are approaching the technical limit of the device;

it is impossible to maintain a stable distance to the slope;

data quality becomes unacceptable;

the return route requires excessive energy reserves;

icing begins;

there is a risk of loss of control.

The manufacturer's limit should not be taken as normal operating conditions.

18. Flight in fog, snow and rain

Fog and clouds can lead to:

loss of visual contact;

condensation;

icing;

optical sensor errors;

reduction in the quality of LiDAR and cameras;

deterioration of communication.

Even payloads with claimed protection against dust and moisture have limited operating conditions. For example, the Zenmuse L2 has a claimed IP54 rating when used within the manufacturer's specifications.

A planned mapping flight should not be performed:

in a thunderstorm;

in case of icing;

in dense fog;

in heavy rain;

in wet snow;

if a safe landing is not possible.

19. Ground calibration

Before the flight the following is performed:

GNSS testing;

IMU check;

camera calibration;

focus check;

spectral panel shooting;

Thermal standard verification;

time synchronization;

checking the memory card;

communication test;

checking control points.

After the flight, the spectral standard is taken again, especially if the lighting has changed.

20. Main output products

20.1. Orthophotoplan

Geometrically corrected image of the territory.

20.2. Point cloud

A three-dimensional set of surface coordinates.

20.3. DSM

Digital model of the top surface, including trees and structures.

20.4. DTM

Model of the earth's surface without vegetation and objects.

20.5. Vegetation Height Map

It turns out to be the difference between DSM and DTM.

20.6. Slope map

Shows the steepness of each section.

20.7. Exposure map

Defines the orientation of the surface.

20.8. Map of flow directions

Shows the probable movement of surface water.

20.9. Runoff accumulation map

Highlights the locations where threads are merged.

20.10. Heat map

Shows temperature anomalies.

20.11. Spectral map

Shows the condition of vegetation and surface.

21. Definition of changes

The most valuable function of the system is the comparison of different dates.

The following are analyzed:

surface movement;

change in height;

increase in cracks;

growth of gullies;

sediment accumulation;

change of channel;

loss of vegetation;

growth of shrubs;

tree felling;

change of road;

condition of engineering structures.

LiDAR and photogrammetric point clouds can be used to compare landslide geometry and estimate its movement. The USGS uses point clouds, photogrammetry, and LiDAR to analyze mass movements and post-fire slope processes.

22. Landslide analysis

The algorithm identifies:

main ledge;

lateral borders;

tensile cracks;

bulges at the bottom;

vegetation disturbances;

road displacement;

new damp spots;

material accumulation zones.

The drone displays surface geometry but does not automatically determine the depth of the sliding surface.

The following is required for an engineering report:

piezometers;

inclinometers;

geophysics;

drilling;

laboratory properties of soil.

23. Erosion analysis

The system determines:

length of gullies;

depth;

width;

volume of removed material;

growth rate;

connection with the road;

damage to terraces;

sedimentation zones.

A repeat scan after a rainstorm allows us to estimate the actual amount of erosion.

24. Road analysis

DREVO AeroSense Drone surveys:

tracks;

ditches;

pipes;

bridges;

embankments;

cracks;

subsidence;

release blur;

snow drifts;

falling trees.

The following are marked automatically:

blocked culverts;

water on the canvas;

change in slope geometry;

new bypass routes;

damage to emergency overflow.

25. Analysis of sources and springs

Thermal and visible imaging helps to identify:

cold or warm anomalies;

wet stripes;

change in vegetation;

new exits;

disappearance of the damp spot;

muddy stream after a downpour.

But the presence of a thermal anomaly does not prove the presence of a spring.

The detected area is checked:

ground survey;

flow measurement;

temperature;

electrical conductivity;

geology.

26. Vegetation analysis

The system classifies:

bare soil;

herbs;

shrubs;

trees;

dry vegetation;

damaged areas;

burnt areas;

invasive communities.

For each plant polygon the following are formed:

square;

average height;

density;

spectral index;

thermal state;

seasonal change;

fire risk;

survey priority.

27. Fire missions

Before the fire

fuel mapping;

dry biomass measurement;

checking fire corridors;

tank control;

inspection of power lines.

During the fire

search for the front;

detection of point foci;

assessment of the direction of propagation;

road control;

assistance in finding people.

Flights are carried out only in coordination with fire services so as not to interfere with manned aircraft.

After the fire

mapping of the burned area;

search for decay;

soil damage analysis;

definition of mudflow catchments;

control of emergency water routes;

recovery planning.

28. Work after an abnormal downpour

Priority sequence:

emergency waterways;

roads and bridges;

upper troughs;

landslide areas;

springs;

terraces and storage tanks;

vegetation;

sediments.

The drone should not fly too low over unstable waterways where falling rocks, severe turbulence, or loss of connection are possible.

29. Artificial Intelligence

AI is used for preliminary analysis:

surface segmentation;

change detection;

vegetation classification;

crack detection;

detection of traffic jams;

determination of thermal anomalies;

fire fuel assessments;

comparison with the previous flight.

AI should not independently issue a final diagnosis:

"landslide is inevitable";

"drinking water";

"the spring has been restored";

"safe slope".

It forms:

probability;

degree of change;

confidence level;

need for ground testing.

30. Integration with a digital passport

After processing, the data is automatically linked to passport objects.

For each section the following are updated:

scan date;

elevation model;

vegetation condition;

erosion;

road damage;

landslide signs;

condition of structures;

fire load;

new photos;

risk level;

recommended tasks.

Previous versions are not deleted.

31. Integration with Mountain Digital Twin

The digital twin uses drone data to:

relief clarification;

roughness updates;

changes in vegetation cover;

runoff calculation;

flood modeling;

fire simulation;

sediment volume assessment;

checks of emergency routes;

determining areas for future survey.

32. Scanning regulations

Basic scanning

Performed before the start of restoration work.

Seasonal

then the snow melts;

before the fire season;

after the dry season;

before the period of heavy rains.

Planned annual

Full condition comparison.

Eventful

After:

abnormal downpour;

fire;

landslide;

earthquakes;

strong wind;

massive windfall;

road destruction.

High-risk areas

Can be scanned:

monthly;

after every significant event;

when ground sensors are triggered.

33. Accuracy and acceptable variations

For each object, a minimum change is specified that the system must reliably detect.

For example:

minor erosion;

change of road track;

opening of a large crack;

landslide displacement;

loss of vegetation;

accumulation of sediments.

The required accuracy is determined by:

flight altitude;

camera type;

number of control points;

overlap;

the need for LiDAR;

frequency of repeat missions.

It is impossible to demand millimetre accuracy from a cheap survey without appropriate geodetic control.

34. Quality control of results

The following are checked:

fullness of coverage;

sharpness of photos;

georeferencing accuracy;

stitching errors;

cloud density;

presence of shadows;

missed sections;

discrepancy with benchmarks;

coincidence of control points;

sensor calibration.

The result receives the quality class:

Q1 - engineering;

Q2 - cartographic;

Q3 - overview;

Q4 - emergency incomplete;

Q5 - not suitable for measurements.

35. System limitations

DREVO AeroSense Drone does not replace:

hydrogeologist;

geotechnics;

ground geodesy;

drilling;

laboratory analysis;

piezometers;

inclinometers;

field inspections.

The drone shows the surface and its changes well.

It defines in a limited way:

groundwater depth;

landslide depth;

internal structure of the rock;

chemical composition of water;

slope stability without an engineering model.

36. Flight safety

It is necessary to take into account:

national aviation legislation;

restricted areas;

flights beyond visual line of sight;

proximity to airports;

protected areas;

presence of people;

power lines;

birds;

fire-fighting aviation;

rescue operations.

Autonomy does not eliminate the operator's responsibility.

37. Effects on animals

Flying may be disturbing:

nesting birds;

large mammals;

bats;

herds;

rare species.

For sensitive areas the following are specified:

seasonal restrictions;

minimum height;

bypass routes;

hang limitation;

reduction in the number of flights;

observation of a biologist.

38. Data storage

The original data is stored separately from the processed data.

The archive includes:

photographs;

video;

LiDAR;

GNSS and IMU;

flight logs;

camera parameters;

checkpoints;

orthophoto;

relief models;

quality report;

AI results;

expert opinions.

Formats must be open and suitable for long-term use.

39. Digital Mission Passport

Each flight receives a separate entry:

mission identifier;

date and time;

operator;

apparatus;

payload;

route;

height;

speed;

weather;

wind;

lighting;

sensor settings;

checkpoints;

data volume;

quality class;

detected anomalies;

follow-up actions.

40. The result of one complete mission

The complete mission of the DREVO AeroSense Drone should produce:

current orthophotoplan;

digital elevation model;

digital surface model;

classified point cloud;

slope map;

water direction map;

vegetation map;

heat map;

erosion map;

landslide signs map;

road condition map;

fire fuel map;

list of new anomalies;

comparison with the previous mission;

tasks for the ground group.

41. Implementation sequence

Step 1: Basic RGB Drone

Creation of orthophoto and primary elevation model.

Stage 2. Geodetic network

Installation of benchmarks and RTK/PPK binding.

Stage 3. Multispectral module

Monitoring of vegetation and soil.

Stage 4. Thermal imager

Springs, fires, humidity anomalies.

Stage 5. LiDAR

Forested and geotechnically challenging areas.

Stage 6. AI Edge

Automatic detection of objects and changes.

Stage 7. Digital Twin

Data fusion with ground-based sensors.

Stage 8. Autonomous stations

Automatic start after an alarm or according to a schedule where legally and technically permissible.

The final principle

The DREVO AeroSense Drone becomes the aerial measurement instrument for the entire DREVO Living Mountains program.

It unites:

photogrammetry;

LiDAR;

multispectral imaging;

thermal imaging analysis;

spot inspection;

AI processing;

re-measurement;

digital passport;

Mountain Digital Twin.

The value of a drone is not determined by the number of images it takes, but by its ability to re-measure the same slope, detect real changes, and translate them into concrete engineering action.