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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM60810

SMART SURVEYING WITH DRONE TECHNOLOGY USING AI BASED OBJECT DETECTION AND AUTOMATED CONTOUR MAPPING

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM60810

Volume:

10 |

4 |

April 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AG

Achal Gurnule

KP

Kishori Pimparkhede

NN

Nisha Nyaykare

SD

Suhani Dhabale

HP

Himanshu Patle

ST

Suyosh Thaware

YN

Yugal Nagose

PKP

Prof. Kajal Pachdhare

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Abstract

The rapid advancement of drone technology combined with Artificial Intelligence (AI) has significantly transformed modern surveying techniques. This research Paper Presents a smart surveying approach using unmanned aerial vehicles (UAVs) integrated with AI-based object detection and automated contour mapping. The proposed system utilizes high-resolution aerial imagery captured by drones and processes it using machine learning algorithms to detect and classify ground objects efficiently. Additionally, contour mapping is automated through advanced image processing and elevation data extraction, reducing the need for manual intervention. This method improves accuracy, reduces time consumption, and enhances cost- effectiveness compared to traditional surveying techniques. The study highlights then potential of integrating AI with drone technology in application such as land surveying, urban planning, agriculture, and environmental monitoring.

KEYWORDS

Drone Technology Smart Surveying Contour Detection Object Detection

Cite This Article

Achal Gurnule, Kishori Pimparkhede, Nisha Nyaykare, Suhani Dhabale, Himanshu Patle, Suyosh Thaware, Yugal Nagose, Prof. Kajal Pachdhare, "SMART SURVEYING WITH DRONE TECHNOLOGY USING AI BASED OBJECT DETECTION AND AUTOMATED CONTOUR MAPPING," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, April 2026.

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Publication Date

4/20/2026

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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