Energy
21 columns
600 rows
8 formats

Aerial and Drone Inspection of Transmission Lines

This dataset provides detailed records of aerial and drone-based inspections of transmission corridors, including high-resolution imagery, sensor data, and condition assessments of towers, conductors, insulators, and surrounding vegetation. It enables comprehensive asset monitoring, predictive maintenance, and vegetation management for transmission infrastructure operators.

last updated 1 Nov 2025
by GoMask

The brief that made it

Predictive maintenance and asset health monitoring

Sample rows

preview · 8 of 600 rows · all 21 columns

inspection_idstring	sensor_typestring	thermal_anomaly_temperaturefloat	vegetation_encroachmentboolean	tower_conditionstring	inspection_datedate	inspection_typestring	transmission_line_idstring	tower_idstring	tower_latitudefloat	tower_longitudefloat	image_idstring	image_typestring	image_timestampdatetime	conductor_conditionstring	insulator_conditionstring	hardware_conditionstring	thermal_anomaly_detectedboolean	inspector_idstring	image_urlstring	inspection_notesstring
INSP-00001	visual	blank	false	normal	2024-01-01	helicopter	TL-1001	TWR-001	34.0522	-118.2437	IMG-00001	visual	2024-01-01T10:15:03	normal	normal	normal	false	INSPECTOR-001	https://images.example.com/TL-1001/TWR-001/IMG-00001.jpg	Routine visual inspection found all components in good condition. No vegetation issues.
INSP-00002	ir	627.5	true	bent	2024-02-15	uav_drone	TL-1002	TWR-002	45.4215	-75.6997	IMG-00002	infrared	2024-02-15T14:23:59	damaged	cracked	loose	true	UAVTEAM-101	https://s3.example.com/TL-1002/TWR-002/IMG-00002.png	Anomaly detected: high temperature on conductor, cracked insulator and loose hardware. Vegetation encroachment observed.
INSP-00003	lidar	blank	false	damaged	2024-02-29	uav_drone	TL-1003	TWR-003	89.9999	180	IMG-00003	lidar	2024-02-29T09:01:13	unknown	unknown	missing	false	QA-BOT	https://s3.example.com/TL-1003/TWR-003/IMG-00003.las	QA inspection at polar boundary. Multiple unknown conditions detected. Hardware missing.
INSP-00004	visual	blank	false	normal	2024-03-10	helicopter	TL-1004	TWR-004	51.5074	-0.1278	IMG-00004	visual	2024-03-10T11:45:36	normal	normal	normal	false	TEAM-UK-01	https://images.example.com/TL-1004/TWR-004/IMG-00004.jpg	Spring routine inspection found no deviations. All hardware stable.
INSP-00005	visual	blank	false	rusted	2024-03-15	helicopter	TL-1005	TWR-005	39.9042	116.4074	IMG-00005	visual	2024-03-15T08:19:05	corroded	contaminated	normal	false	CN-TEAM-03	http://images.example.com/TL-1005/TWR-005/IMG-00005.jpeg	Corrosion noted on conductor and rust on tower base. Insulator contaminated, needs cleaning.
INSP-00006	ir	401.5	true	rusted	2024-03-18	uav_drone	TL-1006	TWR-006	37.7749	-122.4194	IMG-00006	infrared	2024-03-18T17:33:21	damaged	cracked	loose	true	SAFETY-URGENT	https://s3.example.com/TL-1006/TWR-006/IMG-00006.png	Urgent: Damaged conductor and cracked insulator. Rust observed. Vegetation intruding.
INSP-00007	lidar	blank	false	bent	2024-03-21	uav_drone	TL-1007	TWR-007	0	0	IMG-00007	lidar	2024-03-21T13:05:42	normal	normal	normal	false	EQ-TEST-TEAM	https://s3.example.com/TL-1007/TWR-007/IMG-00007.las	QA inspection at equator. Tower shows minor bent structure. No other issues.
INSP-00008	visual	blank	false	normal	2024-03-25	helicopter	TL-1008	TWR-008	48.8566	2.3522	IMG-00008	visual	2024-03-25T09:42:12	normal	normal	normal	false	FR-TEAM-02	https://images.example.com/TL-1008/TWR-008/IMG-00008.jpg	Scheduled inspection completed. All tower and line components normal.
INSP-00009	visual	blank	false	normal	2024-03-26	helicopter	TL-1009	TWR-009	40.7128	-74.006	IMG-00009	visual	2024-03-26T16:22:47	normal	normal	loose	false	NY-TEAM-01	http://images.example.com/TL-1009/TWR-009/IMG-00009.jpg	Routine helicopter inspection. Hardware slightly loose, no vegetation detected.
INSP-00010	visual	blank	false	normal	2024-04-02	helicopter	TL-1010	TWR-010	52.52	13.405	IMG-00010	visual	2024-04-02T12:20:11	normal	normal	normal	false	DE-TEAM-07	https://images.example.com/TL-1010/TWR-010/IMG-00010.jpg	No issues found during scheduled inspection. All systems operate normally.
INSP-00011	ir	154.7	true	rusted	2024-04-08	uav_drone	TL-1011	TWR-011	55.7558	37.6173	IMG-00011	infrared	2024-04-08T18:07:35	corroded	contaminated	loose	true	RU-MOSCOW-TEAM	https://s3.example.com/TL-1011/TWR-011/IMG-00011.png	Vegetation observed near tower base. Corroded conductor and loose hardware. Thermal anomaly present.
INSP-00012	lidar	blank	false	normal	2024-04-14	uav_drone	TL-1012	TWR-012	-33.8688	151.2093	IMG-00012	lidar	2024-04-14T05:56:58	normal	normal	normal	false	LIDAR-TEAM-001	https://s3.example.com/TL-1012/TWR-012/IMG-00012.las	Lidar scan shows normal tower structure. No anomalies detected.
INSP-00013	visual	blank	false	normal	2024-04-19	helicopter	TL-1013	TWR-013	35.6895	139.6917	IMG-00013	visual	2024-04-19T10:54:22	normal	normal	normal	false	JP-TEAM-02	http://images.example.com/TL-1013/TWR-013/IMG-00013.jpg	Routine helicopter patrol. No vegetation or hardware issues detected.
INSP-00014	visual	blank	false	normal	2024-04-24	helicopter	TL-1014	TWR-014	41.8781	-87.6298	IMG-00014	visual	2024-04-24T14:38:08	normal	normal	normal	false	US-CHICAGO-T02	https://images.example.com/TL-1014/TWR-014/IMG-00014.jpg	Spring inspection complete. No issues found.
INSP-00015	ir	1000	true	bent	2024-05-01	uav_drone	TL-1015	TWR-015	59.9343	30.3351	IMG-00015	infrared	2024-05-01T21:45:02	damaged	cracked	loose	true	RU-SPB-TEAM	https://s3.example.com/TL-1015/TWR-015/IMG-00015.png	Extreme temperature anomaly detected. Damaged conductor and bent tower. Vegetation present.
INSP-00016	lidar	blank	false	unknown	2024-05-08	uav_drone	TL-1016	TWR-016	-90	-180	IMG-00016	lidar	2024-05-08T07:10:23	normal	unknown	unknown	false	POLAR-OPS	https://s3.example.com/TL-1016/TWR-016/IMG-00016.las	Boundary QA inspection at south pole. Insulator and hardware status unknown.
INSP-00017	visual	blank	false	rusted	2024-05-14	helicopter	TL-1017	TWR-017	36.1699	-115.1398	IMG-00017	visual	2024-05-14T10:02:21	corroded	contaminated	normal	false	US-NV-T01	https://images.example.com/TL-1017/TWR-017/IMG-00017.jpg	Corroded conductor and rust found. Insulator contaminated, recommend cleaning.
INSP-00018	ir	401.5	true	damaged	2024-05-19	uav_drone	TL-1018	TWR-018	60	-120	IMG-00018	infrared	2024-05-19T22:31:44	damaged	cracked	missing	true	CAN-TEAM-07	https://s3.example.com/TL-1018/TWR-018/IMG-00018.png	Major anomaly: Damaged conductor, missing hardware, cracked insulator. Vegetation encroachment.
INSP-00019	lidar	blank	false	normal	2024-05-23	uav_drone	TL-1019	TWR-019	0	-180	IMG-00019	lidar	2024-05-23T04:44:18	normal	normal	normal	false	QA-BOT	https://s3.example.com/TL-1019/TWR-019/IMG-00019.las	QA boundary test at prime meridian. All components normal.
INSP-00020	visual	blank	false	normal	2024-05-30	helicopter	TL-1020	TWR-020	55.9533	-3.1883	IMG-00020	visual	2024-05-30T13:29:59	normal	normal	normal	false	TEAM-SCOTLAND	https://images.example.com/TL-1020/TWR-020/IMG-00020.jpg	No issues noted. Routine visual check in spring.

What the 600 rows show

from the 600-row sample

Ir (sensor type) stands out: 163 of its 163 rows have vegetation_encroachment = true, against 3 of 437 for the rest.

28%vegetation_encroachment = true
401.5median thermal_anomaly_temperature
2inspection types
3image types
4insulator conditions
4hardware conditions

Vegetation encroachment rate by sensor_typevegetation_encroachment = true

thermal_anomaly_temperature163 rows, in bands of 200

Median 401.5, from -49.9 to 1,000.

tower_condition600 rows · 6 values

normal317
damaged114
bent85
rusted73
unknown10
corroded1

21 columns by typefrom the column list below

string 14
float 3
date 1
datetime 1
boolean 2

Columns

21 columns in four groups

blueprint · 21 columns
column	type	description	example
Text 14 columns
`inspection_id`	string	Unique identifier for each inspection eventunique	INSP-00001
`inspection_type`	string	Type of inspection platform used (e.g., helicopter, UAV drone)helicopter · uav_drone	helicopter
`sensor_type`	string	Type of sensor used during inspection (e.g., visual, IR, LiDAR)visual · ir · lidar	visual
`transmission_line_id`	string	Unique identifier for the transmission line being inspected	TL-1001
`tower_id`	string	Unique identifier for the transmission tower being inspected	TWR-001
`image_id`	string	Unique identifier for each captured imageunique	IMG-00001
`image_url`	string	URL or file path to the high-resolution image	https://images.example.co…
`image_type`	string	Type of image captured (e.g., visual, infrared, lidar)visual · infrared · lidar	visual
`conductor_condition`	string	Assessment of conductor condition (e.g., normal, damaged, corroded)normal · damaged · corroded · unknown · optional	normal
`insulator_condition`	string	Assessment of insulator condition (e.g., normal, cracked, contaminated)normal · cracked · contaminated · unknown · optional	normal
`hardware_condition`	string	Assessment of hardware condition (e.g., normal, loose, missing)normal · loose · missing · unknown · optional	normal
`tower_condition`	string	Assessment of tower condition (e.g., normal, rusted, bent, damaged)normal · rusted · bent · damaged · unknown · optional	normal
`inspection_notes`	string	Additional notes or comments from the inspectoroptional	No issues detected.
`inspector_id`	string	Unique identifier for the inspector or inspection teamoptional	INSPECTOR-001
Numbers 3 columns
`tower_latitude`	float	Latitude coordinate of the transmission tower-90 to 90	34.0522
`tower_longitude`	float	Longitude coordinate of the transmission tower-180 to 180	-118.2437
`thermal_anomaly_temperature`	float	Temperature value of detected thermal anomaly (Celsius)-50 to 1,000 · optional	627.5
Dates and times 2 columns
`inspection_date`	date	Date when the inspection was conducted	2024-01-01
`image_timestamp`	datetime	Timestamp when the image was captured	2024-01-01T10:15:03
True or false 2 columns
`vegetation_encroachment`	boolean	Indicates if vegetation encroachment is detected near the transmission lineoptional	false
`thermal_anomaly_detected`	boolean	Indicates if a thermal anomaly was detected in the inspectionoptional	false

Use it for

An energy dashboard
The vegetation_encroachment rate, thermal_anomaly_temperature by sensor_type and a breakdown of tower_condition. Excel, Power BI or Tableau.
Why do 166 of 600 rows have vegetation_encroachment = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Inspections600
INSP-00001visual
INSP-00002627.5ir
INSP-00006401.5ir
A software demo
Believable inspections with inspection_date, inspection_type and sensor_type to fill a screen in front of a buyer.

This dataset600 rows21 columns

Yours1,000 rows21 columns

blueprint · aerial-and-drone-inspection-of-transmission-lines

Behind this dataset

Same schema. As many rows as you need.

These 600 rows came out of a blueprint — 21 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Transmission line ID and structure span numbers
Inspection date and flight crew/drone operator
Sensor types: visual camera (RGB), infrared/thermal, LiDAR point cloud
Defects detected: conductor strand breakage, corona, insulator damage
Vegetation encroachment: distance to conductors (feet)
Hardware condition: broken/missing dampers, spacers, armor rods
Thermal anomalies: hot spots on connectors or splices
LiDAR clearance measurements for NESC compliance

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: aerial-and-drone-inspection-of-transmission-lines

What should your data show?

No signup. No card.

Sample rows

What the 600 rows show

Columns

Use it for

An energy dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Energy

What should your data show?