scholarly journals Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms

2018 ◽  
Vol 10 (2) ◽  
pp. 294 ◽  
Author(s):  
Dezhi Wang ◽  
Bo Wan ◽  
Penghua Qiu ◽  
Yanjun Su ◽  
Qinghua Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Mangrove Species ◽  
Object Based ◽  
Species Mapping

scholarly journals Evaluation of Machine Learning Algorithms for Object-Based Mapping of Landslide Zones Using UAV Data

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 305
Author(s):  
Efstratios Karantanellis ◽  
Vassilis Marinos ◽  
Emmanuel Vassilakis ◽  
Daniel Hölbling
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Scale Parameters ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Landslide Mapping ◽  
Aerial Platforms

Landslides are a critical geological phenomenon with devastating and catastrophic consequences. With the recent advancements in the geoinformation domain, landslide documentation and inventorization can be achieved with automated workflows using aerial platforms such as unmanned aerial vehicles (UAVs). As a result, ultra-high-resolution datasets are available for analysis at low operational costs. In this study, different segmentation and classification approaches were utilized for object-based landslide mapping. An integrated object-based image analysis (OBIA) workflow is presented incorporating orthophotomosaics and digital surface models (DSMs) with expert-based and machine learning (ML) algorithms. For segmentation, trial and error tests and the Estimation of Scale Parameter 2 (ESP 2) tool were implemented for the evaluation of different scale parameters. For classification, machine learning algorithms (K- Nearest Neighbor, Decision Tree, and Random Forest) were assessed with the inclusion of spectral, spatial, and contextual characteristics. For the ML classification of landslide zones, 60% of the reference segments have been used for training and 40% for validation of the models. The quality metrics of Precision, Recall, and F1 were implemented to evaluate the models’ performance under the different segmentation configurations. Results highlight higher performances for landslide mapping when DSM information was integrated. Hence, the configuration of spectral and DSM layers with the RF classifier resulted in the highest classification agreement with an F1 value of 0.85.

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