MAPPING URBAN TREES WITHIN CADASTRAL PARCELS USING AN OBJECT-BASED CONVOLUTIONAL NEURAL NETWORK

18 citations as recorded by crossref.

1. Multiscale Object-Based Classification and Feature Extraction along Arctic Coasts A. Clark et al.
2. Algorithmic urban planning for smart and sustainable development: Systematic review of the literature T. Son et al.
3. Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos Earthquake O. Kaplan & G. Kaplan
4. Combining OBIA, CNN, and UAV imagery for automated detection and mapping of individual olive trees O. Ameslek et al.
5. Use of Bi-Temporal ALS Point Clouds for Tree Removal Detection on Private Property in Racibórz, Poland P. Przewoźna et al.
6. Remotely Sensed Tree Characterization in Urban Areas: A Review L. Velasquez-Camacho et al.
7. A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes M. Farahani et al.
8. Geographic Object-Based Image Analysis: A Primer and Future Directions M. Kucharczyk et al.
9. Automatic Wheat Lodging Detection and Mapping in Aerial Imagery to Support High-Throughput Phenotyping and In-Season Crop Management B. Zhao et al.
10. Classifying open water features using optical satellite imagery and an object-oriented convolutional neural network M. Merchant
11. Developing an integrated approach based on geographic object-based image analysis and convolutional neural network for volcanic and glacial landforms mapping M. Kazemi Garajeh et al.
12. A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs J. Detka et al.
13. Identification and Mapping of Individual Trees from Unmanned Aerial Vehicle Imagery Using an Object-Based Convolutional Neural Network O. Ameslek et al.
14. Nationwide urban tree canopy mapping and coverage assessment in Brazil from high-resolution remote sensing images using deep learning J. Guo et al.
15. Mapping and Monitoring of Land Cover/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional Neural Network) and by Post-classification Comparison (PCC) G. Xie & S. Niculescu
16. Application of Convolutional Neural Networks With Object-Based Image Analysis for Land Cover and Land Use Mapping in Coastal Areas: A Case Study in Ain Témouchent, Algeria N. Zaabar et al.
17. Study on transfer learning ability for classifying marsh vegetation with multi-sensor images using DeepLabV3+ and HRNet deep learning algorithms M. Liu et al.
18. Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN) S. Timilsina et al.