Conference Agenda

Overview and details of the sessions and sub-session of this conference. Please select a date or session to show only sub-sessions at that day or location. Please select a single sub-session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
S.5.3: URBAN & DATA ANALYSIS
Time:
Wednesday, 13/Sept/2023:
2:00pm - 3:30pm

Session Chair: Prof. Daniela Faur
Session Chair: Dr. Weiwei Guo
Room: 214 - Continuing Education College (CEC)


57971 - Automated Environmental Changes

Round table discussion


Show help for 'Increase or decrease the abstract text size'
Presentations
2:00pm - 2:45pm
Oral
ID: 272 / S.5.3: 1
Oral Presentation
Data Analysis: 57971 - Automated Identifying of Environmental Changes Using Satellite Time-Series

Multi-source and Multi-temporal Remote Sensing Images for Shipbuilding Production State Monitoring

Yan Song1, Yuhong Tu1, Zekai Liu1, Wanrou Qin1, Yunsheng Wang2

1China University of Geosciences(Wuhan), China, People's Republic of; 2Finnish Geospatial Research Institute

Abstract

Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. Using satellite remote sensing data to monitor the production state of shipyard dynamically has the advantages of high efficiency and objectivity. Further, dock, shipway, assembly area, material storage area and other shipbuilding places are the indispensable part of shipbuilding industry, which can reflect the production activity of the shipyard. This study performs object detection for docks based on high-resolution remote sensing images and deep learning methods. Meanwhile, according to the imaging characteristics of optical remote sensing images and SAR images of shipbuilding places, we used satellite remote sensing data to dynamically monitor the shipyard production state from spatial and time series perspective.

The study firstly uses an object detection network based on the deformable spatial attention module (DSAM), which can be used to detect the docks on high spatial resolution remote sensing image. This network solve the object detection problems caused by the limit of actual docks and the diversity of docks. Secondly, since the backbone of the dock object detection network is with the excellent feature extraction capability for docks, this study connects the backbone with a lightweight status recognition network (Status Head) to determine the dock production status information based on the features extracted from the backbone. Thirdly, we analyzed the backscattered features of shipbuilding places on SAR satellite images, and proposed a method to monitor shipyard production state by using multi-time SAR data. This method can reduce the error caused by insufficient time resolution when using high resolution optical remote sensing data to monitor the production state. Finally, the proposed method can accurately recognize the shipyard production state through experimental verification, which reflects the potential of satellite remote sensing images in shipyard production state monitoring, and also provides a new research thought perspective for other coastal industrial production state monitoring.

272-Song-Yan-Oral_Cn_version.pdf
272-Song-Yan-Oral_PDF.pdf


2:45pm - 3:30pm
ID: 323 / S.5.3: 2
Oral Presentation

Round table discussion

. .

.

.



 
Contact and Legal Notice · Contact Address:
Privacy Statement · Conference: 2023 Dragon 5 Symposium
Conference Software: ConfTool Pro 2.6.149
© 2001–2024 by Dr. H. Weinreich, Hamburg, Germany