TY - JOUR
T1 - Deep learning-based object detection and geographic coordinate estimation system for GeoTiff imagery
AU - Pratama, B. M.
AU - Gunawan, D.
AU - Gultom, R. A.G.
N1 - Publisher Copyright:
© 2020 IOP Publishing Ltd. All rights reserved.
PY - 2020/7/15
Y1 - 2020/7/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85088898592&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1577/1/012003
DO - 10.1088/1742-6596/1577/1/012003
M3 - Conference article
AN - SCOPUS:85088898592
SN - 1742-6588
VL - 1577
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012003
T2 - 2nd International Conference on Electronics Representation and Algorithm: Innovation and Transformation for Best Practices in Global Community, ICERA 2019
Y2 - 12 December 2019 through 13 December 2019
ER -