Smart solutions for smart cities: Urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John's, NL, Canada

2021 ◽  
Vol 280 ◽  
pp. 111676 ◽  
Author(s):  
Masoud Mahdianpari ◽  
Jean Elizabeth Granger ◽  
Fariba Mohammadimanesh ◽  
Sherry Warren ◽  
Thomas Puestow ◽  
...  
Keyword(s):  
High Resolution ◽  
Smart Cities ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Wetland Mapping ◽  
Urban Wetland ◽  
High Resolution Satellite Imagery ◽  
Airborne Lidar Data ◽  
The City ◽  
Very High

scholarly journals Assessing Alluvial Fan of Bergama Using High Resolution DEM Generated from Airborne LiDAR Data

2020 ◽  
Author(s):  
Beycan Hocaoğlu ◽  
Müge Ağca
Keyword(s):  
High Resolution ◽  
Flood Plain ◽  
Alluvial Fan ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Morphological Process ◽  
Digital Elevation ◽  
Geomorphological Evolution ◽  
Airborne Lidar Data ◽  
The City

Topography represented by high resolution digital elevation models are able to inform past and present morphological process on the terrain. High resolution LiDAR data taken by the General Directorate of Map at the surroundings of the Bergama city shows great opportunities to understand the morphological process on alluvial fan on which the city is located and the flood plain of Bakırçay river near the alluvial fan. In this paper the LiDAR data collected in 2015 have been used to create DEM’s to understand the geomorphological evolution of the alluvial fan and the flood plain around it. Since the proximal roots and medial parts of the alluvial fan have been the scene for a long human settlement most topographical traces of the morphological process have been distorted. Nevertheless, the traces of past and present morphological process at the distal fan which consist the contact zone with the flood plain are very clear on the DEM created from LiDAR data. The levees and some old courses of Bergama and Bakırçay rivers have been shown on the maps which are also important to understand the ancient roads which follows these levees.

scholarly journals CNN-Based Individual Tree Species Classification Using High-Resolution Satellite Imagery and Airborne LiDAR Data

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1697
Author(s):  
Hui Li ◽  
Baoxin Hu ◽  
Qian Li ◽  
Linhai Jing
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Tree Species ◽  
Airborne Lidar ◽  
Support Vector ◽  
Lidar Data ◽  
Individual Tree ◽  
Sensing Applications ◽  
High Resolution Satellite Imagery ◽  
Airborne Lidar Data

Deep learning (DL) has shown promising performances in various remote sensing applications as a powerful tool. To explore the great potential of DL in improving the accuracy of individual tree species (ITS) classification, four convolutional neural network models (ResNet-18, ResNet-34, ResNet-50, and DenseNet-40) were employed to classify four tree species using the combined high-resolution satellite imagery and airborne LiDAR data. A total of 1503 samples of four tree species, including maple, pine, locust, and spruce, were used in the experiments. When both WorldView-2 and airborne LiDAR data were used, the overall accuracies (OA) obtained by ResNet-18, ResNet-34, ResNet-50, and DenseNet-40 were 90.9%, 89.1%, 89.1%, and 86.9%, respectively. The OA of ResNet-18 was increased by 4.0% and 1.8% compared with random forest (86.7%) and support vector machine (89.1%), respectively. The experimental results demonstrated that the size of input images impacted on the classification accuracy of ResNet-18. It is suggested that the input size of ResNet models can be determined according to the maximum size of all tree crown sample images. The use of LiDAR intensity image was helpful in improving the accuracies of ITS classification and atmospheric correction is unnecessary when both pansharpened WorldView-2 images and airborne LiDAR data were used.

scholarly journals Topographical changes revealed by high-resolution airborne LiDAR data: The 1999 Tsaoling landslide induced by the Chi–Chi earthquake

2006 ◽  
Vol 88 (3-4) ◽  
pp. 160-172 ◽  
Author(s):  
Rou-Fei Chen ◽  
Kuo-Jen Chang ◽  
Jacques Angelier ◽  
Yu-Chang Chan ◽  
Benoît Deffontaines ◽  
...  
Keyword(s):  
High Resolution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data
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