Geographic Generalization in Airborne RGB Deep Learning Tree Detection

Marconi, Sergio; Bohlman, Stephanie A.; Zare, Alina; White, Ethan P.

doi:10.1101/790071

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Estimating tree counts from high-resolution aerial imagery is an expanding area of D&L research. In most examples of tree counting applications, researchers rely heavily upon information beyond RGB spectral bands, such as photogrammetric or LiDAR-derived elevation datasets and/or multi-spectral datasets, and often test their methods on managed environments such as plantations where vegetation grows in regular patterns [1,6,13,[19][20][21][22][23][24]. There are a few examples of tree counts being estimated over a range of forest conditions, including natural conditions, from high-resolution RGB aerial imagery alone.…”

Section: Introductionmentioning

confidence: 99%

Tree-CRowNN: A Network for Estimating Forest Stand Density from VHR Aerial Imagery

Lovitt,

Richardson,

Zhang

et al. 2023

Remote Sensing

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Estimating the number of trees within a forest stand, i.e., the forest stand density (FSD), is challenging at large scales. Recently, researchers have turned to a combination of remote sensing and machine learning techniques to derive these estimates. However, in most cases, the developed models rely heavily upon additional data such as LiDAR-based elevations or multispectral information and are mostly applied to managed environments rather than natural/mixed forests. Furthermore, they often require the time-consuming manual digitization or masking of target features, or an annotation using a bounding box rather than a simple point annotation. Here, we introduce the Tree Convolutional Row Neural Network (Tree-CRowNN), an alternative model for tree counting inspired by Multiple-Column Neural Network architecture to estimate the FSD over 12.8 m × 12.8 m plots from high-resolution RGB aerial imagery. Our model predicts the FSD with very high accuracy (MAE: ±2.1 stems/12.8 m2, RMSE: 3.0) over a range of forest conditions and shows promise in linking to Sentinel-2 imagery for broad-scale mapping (R2: 0.43, RMSE: 3.9 stems/12.8 m2). We believe that the satellite imagery linkage will be strengthened with future efforts, and transfer learning will enable the Tree-CRowNN model to predict the FSD accurately in other ecozones.

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Section: Introductionmentioning

confidence: 99%

Tree-CRowNN: A Network for Estimating Forest Stand Density from VHR Aerial Imagery

Lovitt,

Richardson,

Zhang

et al. 2023

Remote Sensing

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TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing

Ahlswede

Schulz²,

Gava³

et al. 2023

Earth Syst. Sci. Data

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Abstract. Airborne and spaceborne platforms are the primary data sources for large-scale forest mapping, but visual interpretation for individual species determination is labor-intensive. Hence, various studies focusing on forests have investigated the benefits of multiple sensors for automated tree species classification. However, transferable deep learning approaches for large-scale applications are still lacking. This gap motivated us to create a novel dataset for tree species classification in central Europe based on multi-sensor data from aerial, Sentinel-1 and Sentinel-2 imagery. In this paper, we introduce the TreeSatAI Benchmark Archive, which contains labels of 20 European tree species (i.e., 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany. We propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data. Finally, we provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods. We found that residual neural networks (ResNet) perform sufficiently well with weighted precision scores up to 79 % only by using the RGB bands of aerial imagery. This result indicates that the spatial content present within the 0.2 m resolution data is very informative for tree species classification. With the incorporation of Sentinel-1 and Sentinel-2 imagery, performance improved marginally. However, the sole use of Sentinel-2 still allows for weighted precision scores of up to 74 % using either multi-layer perceptron (MLP) or Light Gradient Boosting Machine (LightGBM) models. Since the dataset is derived from real-world reference data, it contains high class imbalances. We found that this dataset attribute negatively affects the models' performances for many of the underrepresented classes (i.e., scarce tree species). However, the class-wise precision of the best-performing late fusion model still reached values ranging from 54 % (Acer) to 88 % (Pinus). Based on our results, we conclude that deep learning techniques using aerial imagery could considerably support forestry administration in the provision of large-scale tree species maps at a very high resolution to plan for challenges driven by global environmental change. The original dataset used in this paper is shared via Zenodo (https://doi.org/10.5281/zenodo.6598390, Schulz et al., 2022). For citation of the dataset, we refer to this article.

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Geographic Generalization in Airborne RGB Deep Learning Tree Detection

Cited by 2 publications

References 29 publications

Tree-CRowNN: A Network for Estimating Forest Stand Density from VHR Aerial Imagery

Tree-CRowNN: A Network for Estimating Forest Stand Density from VHR Aerial Imagery

TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing

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