Automated residential building detection from airborne LiDAR data with deep neural networks
Authors:
- Zixiang ZhouZZZixiang Zhou
- Jie GongJGJie Gong
- 1 April 2018
- journal article
- research article
- Published by Elsevier in Advanced Engineering Informatics
- Vol. 36, 229-241
Abstract
No abstract availableKeywords
Funding Information
- U.S. Department of Transportation
- Office of the Assistant Secretary for Research and Technology (RITARS-14-H-RUT)
This publication has 30 references indexed in Scilit:
- Automated Analysis of Mobile LiDAR Data for Component‐Level Damage Assessment of Building Structures during Large Coastal Storm EventsComputer-Aided Civil and Infrastructure Engineering, 2018
- SSD: Single Shot MultiBox Detector2016
- Deep-Learning-Based Classification for DTM Extraction from ALS Point CloudRemote Sensing, 2016
- Deep Residual Learning for Image Recognition2016
- Image-Based 3D Reconstruction for Posthurricane Residential Building Damage AssessmentJournal of Computing in Civil Engineering, 2016
- Component-Based Vulnerability Analysis for Residential Structures Subjected to Storm Surge Impact from Hurricane SandyNatural Hazards Review, 2016
- A Remote Sensing-based Approach for Assessing and Visualizing Post-Sandy Damage and Resiliency Rebuilding Needs2014
- Use of Mobile Lidar Data to Assess Hurricane Damage and Visualize Community VulnerabilityTransportation Research Record: Journal of the Transportation Research Board, 2014
- Building large urban environments from unstructured point data2011
- A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point CloudsSensors, 2008