Deep learning-based object detection and geographic coordinate estimation system for GeoTiff imagery

B. M. Pratama, D. Gunawan, R. A.G. Gultom

Research output: Contribution to journalConference articlepeer-review

Abstract

A deep learning-based system has been created to autonomously analyze GeoTiff aerial imagery in order to retrieve information about objects type and their geographic coordinates. This research focuses on applying a Convolutional Neural Network (CNN) to detect objects and estimate the geographic coordinate of airplanes, ships and cars in those images. The system prototype was tested to measure the accuracy and precision for object detection. Furthermore, a Mean Absolute Error (MAE) analysis is done to the system to measure object coordinate estimation performance. The accuracy and precision for object detection of the system prototype are 81,05% and 93,29%, respectively. The system has MAE values which vary from 0,000012° to 0,000034° for object coordinate estimation.

Original languageEnglish
Article number012003
JournalJournal of Physics: Conference Series
Volume1577
Issue number1
DOIs
Publication statusPublished - 15 Jul 2020
Event2nd International Conference on Electronics Representation and Algorithm: Innovation and Transformation for Best Practices in Global Community, ICERA 2019 - Yogyakarta, Indonesia
Duration: 12 Dec 201913 Dec 2019

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