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Article

A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data

1
Esri Germany and Switzerland, Ringstr. 7, 85402 Kranzberg, Germany
2
TUM Department of Aerospace and Geodesy, Technical University of Munich, Arcisstraße 21, 80333 München, Germany
3
Department of Information Technology, Bavarian State Institute of Forestry, Hans Carl-von-Carlowitz-Platz 1, 85354 Freising, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(13), 2121; https://doi.org/10.3390/rs12132121
Received: 19 May 2020 / Revised: 24 June 2020 / Accepted: 28 June 2020 / Published: 2 July 2020
(This article belongs to the Collection Feature Papers for Section Environmental Remote Sensing)
Forest damage due to storms causes economic loss and requires a fast response to prevent further damage such as bark beetle infestations. By using Convolutional Neural Networks (CNNs) in conjunction with a GIS, we aim at completely streamlining the detection and mapping process for forest agencies. We developed and tested different CNNs for rapid windthrow detection based on PlanetScope satellite data and high-resolution aerial image data. Depending on the meteorological situation after the storm, PlanetScope data might be rapidly available due to its high temporal resolution, while the acquisition of high-resolution airborne data often takes weeks to a month and is, therefore, used in a second step for more detailed mapping. The study area is located in Bavaria, Germany (ca. 165 km2), and labels for damaged areas were provided by the Bavarian State Institute of Forestry (LWF). Modifications of a U-Net architecture were compared to other approaches using transfer learning (e.g., VGG19) to find the most efficient architecture for the task on both datasets while keeping the computational time low. A custom implementation of U-Net proved to be more accurate than transfer learning, especially on medium (3 m) resolution PlanetScope imagery (intersection over union score (IoU) 0.55) where transfer learning completely failed. Results for transfer learning based on VGG19 on high-resolution aerial image data are comparable to results from the custom U-Net architecture (IoU 0.76 vs. 0.73). When using both architectures on a dataset from a different area (located in Hesse, Germany), however, we find that the custom implementations have problems generalizing on aerial image data while VGG19 still detects most damage in these images. For PlanetScope data, VGG19 again fails while U-Net achieves reasonable mappings. Results highlight the potential of Deep Learning algorithms to detect damaged areas with an IoU of 0.73 on airborne data and 0.55 on Planet Dove data. The proposed workflow with complete integration into ArcGIS is well-suited for rapid first assessments after a storm event that allows for better planning of the flight campaign followed by detailed mapping in a second stage. View Full-Text
Keywords: forest damage assessment; windthrow; convolutional neural networks; GIS; remote sensing forest damage assessment; windthrow; convolutional neural networks; GIS; remote sensing
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MDPI and ACS Style

Deigele, W.; Brandmeier, M.; Straub, C. A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data. Remote Sens. 2020, 12, 2121. https://doi.org/10.3390/rs12132121

AMA Style

Deigele W, Brandmeier M, Straub C. A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data. Remote Sensing. 2020; 12(13):2121. https://doi.org/10.3390/rs12132121

Chicago/Turabian Style

Deigele, Wolfgang, Melanie Brandmeier, and Christoph Straub. 2020. "A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data" Remote Sensing 12, no. 13: 2121. https://doi.org/10.3390/rs12132121

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