Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks

Abstract

Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in true color, or red/green blue (RGB) imagery using a deep learning detection network. Individual crown delineation is a persistent challenge in studies of forested ecosystems and has primarily been addressed using three-dimensional LIDAR. We show that deep learning models can leverage existing lidar-based unsupervised delineation approaches to initially train an RGB crown detection model, which is then refined using a small number of hand-annotated RGB images. We validate our proposed approach using an open-canopy site in the National Ecological Observation Network (NEON). Our results show that combining LIDAR and RGB methods in a self-supervised model improves predictions of trees in natural landscapes. The addition of a small number of hand-annotated trees improved performance over the initial self-supervised model. While undercounting of individual trees in complex canopies remains an area of development, deep learning can increase the performance of remotely sensed tree surveys.

Deep Learning-Driven Detection and Mapping of Rockfalls on Mars

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Leveraging Environmental Research and Observation Networks to Advance Soil Carbon Science

“…Lastly, the networks should be test beds for developing new tools, data collection techniques, and models that are particularly promising for improved understanding of SOM dynamics. Examples include the increasing popularity of multiscale geophysical techniques for investigating the shallow subsurface in the CZO network (Parsekian et al, ) and the broad application of airborne LiDAR and hyperspectral remote sensing techniques at NEON (Weinstein et al, ). Moreover, there are likely to be data‐rich nodes within the networks that provide opportunities to prototype cross‐disciplinary syntheses.…”

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

“…Over the last two decades, advancements in remote sensing technologies such as the use of reflectance spectroscopy, airborne and satellite technology, and statistical analysis approaches thereof have made it easy to understand several key processes and components of plants such as plant population [1][2][3], grain yield and biomass [4][5][6][7][8], pigment or chlorophyll [9][10][11], water stress response [12][13][14][15], nutritional status [16][17][18][19][20][21] or pest and disease identification [22][23][24][25]. Yet, in-field proximal sensing to estimate the nutritional status of the crops is an economical and technical challenge [26].…”