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We present a deep learning-based framework for individual tree crown delineation in aerial and satellite images. This is an important task, e.g., for forest yield or carbon stock estimation. In contrast to earlier work, the presented method creates irregular polygons instead of bounding boxes and also provides a tree cover mask for areas that are not separable. Furthermore, it is trainable with low amounts of training data and does not need 3D height information from, e.g., laser sensors. We tested the approach in two scenarios: (1) with 30 cm WorldView-3 satellite imagery from an urban region in Bengaluru, India, and (2) with 5 cm aerial imagery of a densely forested area near Gartow, Germany. The intersection over union between the reference and predicted tree cover mask is 71.2% for the satellite imagery and 81.9% for the aerial images. On the polygon level, the method reaches an accuracy of 46.3% and a recall of 63.7% in the satellite images and an accuracy of 52% and recall of 66.2% in the aerial images, which is comparable to previous works that only predicted bounding boxes. Depending on the image resolution, limitations to separate individual tree crowns occur in situations where trees are hardly separable even for human image interpreters (e.g., homogeneous canopies, very small trees). The results indicate that the presented approach can efficiently delineate individual tree crowns in high-resolution optical images. Given the high availability of such imagery, the framework provides a powerful tool for tree monitoring. The source code and pretrained weights are publicly available at https://github.com/AWF-GAUG/TreeCrownDelineation. [ABSTRACT FROM AUTHOR]

Over the last several years, semantic image segmentation based on deep neural networks has been greatly advanced. On the other hand, single-instance segmentation still remains a challenging problem. In this paper, we introduce a framework for segmenting instances of a common object class by multiple active contour evolution over semantic segmentation maps of images obtained through fully convolutional networks. The contour evolution is cast as an energy minimization problem, where the aggregate energy functional incorporates a data fit term, an explicit shape model, and accounts for object overlap. Efficient solution neighborhood operators are proposed, enabling optimization through metaheuristics such as simulated annealing. We instantiate the proposed framework in the context of segmenting individual fallen stems from high-resolution aerial multispectral imagery, providing problem-specific energy potentials. We validated our approach on 3 real-world scenes of varying complexity, using 730 manually labeled polygon outlines as ground truth. The test plots were situated in regions of the Bavarian Forest National Park, Germany, which sustained a heavy bark beetle infestation. Evaluations were performed on both the polygon and line segment level, showing that the multi-contour segmentation can achieve up to 0.93 precision and 0.82 recall. An improvement of up to 7 percentage points (pp) in recall and 6 in precision compared to an iterative sample consensus line segment detection baseline was achieved. Despite the simplicity of the applied shape parametrization, an explicit shape model incorporated into the energy function improved the results by up to 4 pp of recall. Finally, we show the importance of using a high-quality semantic segmentation method (e.g. U-net) as the basis for individual stem detection, as the quality of the results degraded dramatically in our baseline experiment utilizing a simpler method. Our method is a step towards increased accessibility of automatic fallen tree mapping in forests, due to higher cost efficiency of aerial imagery acquisition compared to laser scanning. The precise fallen tree maps could be further used as a basis for plant and animal habitat modeling, studies on carbon sequestration as well as soil quality in forest ecosystems. [ABSTRACT FROM AUTHOR]

Information on crop phenology is essential when aiming to better understand the impacts of climate and climate change, management practices, and environmental conditions on agricultural production. Today's novel optical and radar satellite data with increasing spatial and temporal resolution provide great opportunities to derive such information. However, so far, we largely lack methods that leverage this data to provide detailed information on crop phenology at the field level. We here propose a method based on dense time series from Sentinel-1, Sentinel-2, and Landsat 8 to detect the start of seven phenological stages of winter wheat from seeding to harvest. We built different feature sets from these input data and compared their performance for training a one-dimensional temporal U-Net. The model was evaluated using a comprehensive reference data set from a national phenology network covering 16,000 field observations from 2017 to 2020 for winter wheat in Germany and compared against a baseline set by a Random Forest model. Our results show that optical and radar data are differently well suited for the detection of the different stages due to their unique characteristics in signal processing. The combination of both data types showed the best results with 50.1% to 65.5% of phenological stages being predicted with an absolute error of less than six days. Especially late stages can be predicted well with, e.g., a coefficient of determination (R 2) between 0.51 and 0.62 for harvest, while earlier stages like stem elongation remain a challenge (R 2 between 0.06 and 0.28). Moreover, our results indicate that meteorological data have comparatively low explanatory potential for fine-scale phenological developments of winter wheat. Overall, our results demonstrate the potential of dense satellite image time series from Sentinel and Landsat sensor constellations in combination with the versatility of deep learning models for determining phenological timing. • Method to combine optical and SAR remote sensing imagery for phenology estimation. • One-dimensional U-Net exploits the synergy of time series from diverse sensor types. • Evaluated using 16,000 phenological field observations for winter wheat. • Meteorological data have low explanatory potential for fine-scale phenology. [ABSTRACT FROM AUTHOR]

Energy system modeling is essential in analyzing present and future system configurations motivated by the energy transition. Energy models need various input data sets at different scales, including detailed information about energy generation and transport infrastructure. However, accessing such data sets is not straightforward and often restricted, especially for energy infrastructure data. We present a detection model for the automatic recognition of pipeline pathways using a Convolutional Neural Network (CNN) to address this lack of energy infrastructure data sets. The model was trained with historical low-resolution satellite images of the construction phase of British gas transport pipelines, made with the Landsat 5 Thematic Mapper instrument. The satellite images have been automatically labeled with the help of high-resolution pipeline route data provided by the respective Transmission System Operator (TSO). We have used data augmentation on the training data and trained our model with four different initial learning rates. The models trained with the different learning rates have been validated with 5-fold cross-validation using the Intersection over Union (IoU) metric. We show that our model can reliably identify pipeline pathways despite the comparably low resolution of the used satellite images. Further, we have successfully tested the model's capability in other geographic regions by deploying satellite images of the NEL pipeline in Northern Germany. [ABSTRACT FROM AUTHOR]