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Analysis of UAV-acquired wetland orthomosaics using GIS, computer vision, computational topology and deep learning
Authors
Benjamin Burkhard
Mariano Cabezas
+6 more
Maximo Larry Lopez Caceres
Yago Diez
Jens Groß
Sarah Kentsch
Luca Tomhave
Katsushi Waki
Publication date
1 January 2021
Publisher
Basel : MDPI AG
Doi
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Abstract
Invasive blueberry species endanger the sensitive environment of wetlands and protection laws call for management measures. Therefore, methods are needed to identify blueberry bushes, locate them, and characterise their distribution and properties with a minimum of disturbance. UAVs (Unmanned Aerial Vehicles) and image analysis have become important tools for classification and detection approaches. In this study, techniques, such as GIS (Geographical Information Systems) and deep learning, were combined in order to detect invasive blueberry species in wetland environments. Images that were collected by UAV were used to produce orthomosaics, which were analysed to produce maps of blueberry location, distribution, and spread in each study site, as well as bush height and area information. Deep learning networks were used with transfer learning and unfrozen weights in order to automatically detect blueberry bushes reaching True Positive Values (TPV) of 93.83% and an Overall Accuracy (OA) of 98.83%. A refinement of the result masks reached a Dice of 0.624. This study provides an efficient and effective methodology to study wetlands while using different techniques. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This
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Last time updated on 24/06/2021
Institutionelles Repositorium der Leibniz Universität Hannover
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Last time updated on 30/04/2021