Quantifying the stratigraphic completeness of delta shoreline trajectories

Understanding the incomplete nature of the stratigraphic record is fundamental for interpreting stratigraphic sequences. Methods for quantifying stratigraphic completeness for one-dimensional stratigraphic columns, defined as the proportion of time intervals of some length that contain stratigraphy, are commonplace; however, quantitative assessments of completeness in higher dimensions are lacking. Here we present a metric for defining stratigraphic completeness of two-dimensional shoreline trajectories using topset-foreset rollover positions in dip-parallel sections and describe the preservation potential of a shoreline trajectory derived from the geometry of the delta surface profile and the kinematics of the geomorphic shoreline trajectory. Two end-member forward models are required to fully constrain the preservation potential of the shoreline dependent on whether or not a topset is eroded during base level fall. A laboratory fan-delta was constructed under nonsteady boundary conditions, and one-dimensional stratigraphic column and two-dimensional shoreline completeness curves were calculated. Results are consistent with the hypothesis derived from conservation of sediment mass that completeness over all timescales should increase given increasing dimensions of analysis. Stratigraphic trajectories and completeness curves determined from forward models using experimental geomorphic trajectories compare well to values from transects when subsampled to the equivalent stratigraphic resolution as observed in the actual preserved sequence. The concept of stratigraphic completeness applied to two-dimensional trajectory analysis and the end-member forward models presented here provide novel tools for a conceptual understanding of the nature of stratigraphic preservation at basin scales

Fully convolutional neural networks applied to large-scale marine morphology mapping

In this study we applied for the first time Fully Convolutional Neural Networks (FCNNs) to a marine bathymetric dataset to derive morphological classes over the entire Irish continental shelf. FCNNs are a set of algorithms within Deep Learning that produce pixel-wise classifications in order to create semantically segmented maps. While they have been extensively utilised on imagery for ecological mapping, their application on elevation data is still limited, especially in the marine geomorphology realm. We employed a high-resolution bathymetric dataset to create a set of normalised derivatives commonly utilised in seabed morphology and habitat mapping that include three bathymetric position indexes (BPIs), the vector ruggedness measurement (VRM), the aspect functions and three types of hillshades. The class domains cover ten or twelve semantically distinct surface textures and submarine landforms present on the shelf, with our definitions aiming for simplicity, prevalence and distinctiveness. Sets of 50 or 100 labelled samples for each class were used to train several U-Net architectures with ResNet-50 and VGG-13 encoders. Our results show a maximum model precision of 0.84 and recall of 0.85, with some classes reaching as high as 0.99 in both. A simple majority (modal) voting combining the ten best models produced an excellent map with overall F1 score of 0.96 and class precisions and recalls superior to 0.87. For target classes exhibiting high recall (proportion of positives identified), models also show high precision (proportion of correct identifications) in predictions which confirms that the underlying class boundary has been learnt. Derivative choice plays an important part in the performance of the networks, with hillshades combined with bathymetry providing the best results and aspect functions and VRM leading to an overall deterioration of prediction accuracies. The results show that FCNNs can be successfully applied to the seabed for a morphological exploration of the dataset and as a baseline for more in-depth habitat mapping studies. For example, prediction of semantically distinct classes as “submarine dune” and “bedrock outcrop” can be precise and reliable. Nonetheless, at present state FCNNs are not suitable for tasks that require more refined geomorphological classifications, as for the recognition of detailed morphogenetic processes

Monitoring Coastal Environments using UAS Imagery and Deep Learning

Coastal monitoring is a complex mapping problem for environments that exhibit distinct physical variations through the energy expended from water and sediment movement. In recent years, sensor platforms that capture imagery from these environments have reached centimeter level pixel resolution, which allowed object-based image processing methods to become a standard mapping tool. However, this tool still adheres to shallow machine learning methods, whereby the construction of a learning system is broken into two steps: feature extraction and machine learning model optimisation. In the last decade, deep learning and convolutional neural networks have established state-of-the-art performance on a myriad of computer vision applications. However, deep learning models perform best with large, labelled, training datasets. For coastal monitoring, ground-truth observations can be acquired either in-situ or through post-processed imagery, but both avenues require manual process in producing the ground-truth annotations. In turn, this requires laborious and expensive efforts with domain expertise of coastal processes, posing a bottleneck and challenge for accurate coastal monitoring. In this thesis, practical applications of coastal monitoring using deep learning and convolutional neural networks are discussed. These methods attempt to improve the performance and generalisation of convolutional neural networks with limited amounts of labelled data, which could ease costs of producing ground-truth annotations. A number of approaches are described that reduce the effort required to produce them, or analyse the feasibility of non-domain expert labels

Crowdsourcing experiment and fully convolutional neural networks for coastal remote sensing of seagrass and macro-algae

Recently, convolutional neural networks (CNNs) and fully convolutional neural networks (FCNs) have been successfully used for monitoring coastal marine ecosystems, in particular vegetation. However, even with recent advances in computational modelling and data acquisition, deep learning models require substantial amounts of good quality reference data to effectively self-learn internal representations of input imagery. The classical approach for coastal mapping requires experts to transcribe insitu records and delineate polygons from high-resolution imagery such that FCNs can self-learn. However, labelling by a single individual limits the training data, whereas crowdsourcing labels can increase the volume of training data, but may compromise label quality and consistency. In this paper we assessed the reliability of crowdsourced labels on a complex multi-class problem domain for estuarine vegetation and unvegetated sediment. An inter-observer variability experiment was conducted in order to assess the statistical differences in crowdsourced annotations for plant species and sediment. The participants were grouped based on their discipline and level of expertise, and the statistical differences were evaluated using the Cochran's Q-test and the annotation accuracy of each group to determine for observation biases. Given the crowdsourced labels, FCNs were trained with majority-vote annotations from each group to check whether observation biases were propagated to FCN performance. Two scenarios were examined: first, a direct comparison of FCNs trained with transcribed in-situ labels and crowdsourced labels from each group was established. Then, transcribed in-situ labels were supplemented with crowdsourced labels to investigate the feasibility of training FCNs with crowdsourced labels in coastal mapping applications. We show that annotations sourced from discipline experts (ecologists and geomorphologists) familiar with the study site were more accurate than experts wi..

Improving image registration using colour transfer methods in remote sensing applications.

Image registration is described as aligning several images into a common image coordinate system. For remote sensing, registration is often a key pre-processing step as aerial imagery can be captured by multiple sensors at different spatial and spectral resolutions. A common approach for registering images from different cameras involves using bilinear interpolation to upsample a lower resolution image and computing robust features to find corresponding points in pairs of images. These correspondences provide the basis to compute geometrical linear transforms that align both images together. However, the main drawback to these methods are that colour information in images is ignored and the multimodal nature of this process can cause sub-par linear transforms to be computed. In this work, we show that multimodal aspect can be circumvented entirely using the Linear Monge-Kantorovitch colour transform and that the subsequent registration is improved

DataSheet_1_Fully convolutional neural networks applied to large-scale marine morphology mapping.zip

In this study we applied for the first time Fully Convolutional Neural Networks (FCNNs) to a marine bathymetric dataset to derive morphological classes over the entire Irish continental shelf. FCNNs are a set of algorithms within Deep Learning that produce pixel-wise classifications in order to create semantically segmented maps. While they have been extensively utilised on imagery for ecological mapping, their application on elevation data is still limited, especially in the marine geomorphology realm. We employed a high-resolution bathymetric dataset to create a set of normalised derivatives commonly utilised in seabed morphology and habitat mapping that include three bathymetric position indexes (BPIs), the vector ruggedness measurement (VRM), the aspect functions and three types of hillshades. The class domains cover ten or twelve semantically distinct surface textures and submarine landforms present on the shelf, with our definitions aiming for simplicity, prevalence and distinctiveness. Sets of 50 or 100 labelled samples for each class were used to train several U-Net architectures with ResNet-50 and VGG-13 encoders. Our results show a maximum model precision of 0.84 and recall of 0.85, with some classes reaching as high as 0.99 in both. A simple majority (modal) voting combining the ten best models produced an excellent map with overall F1 score of 0.96 and class precisions and recalls superior to 0.87. For target classes exhibiting high recall (proportion of positives identified), models also show high precision (proportion of correct identifications) in predictions which confirms that the underlying class boundary has been learnt. Derivative choice plays an important part in the performance of the networks, with hillshades combined with bathymetry providing the best results and aspect functions and VRM leading to an overall deterioration of prediction accuracies. The results show that FCNNs can be successfully applied to the seabed for a morphological exploration of the dataset and as a baseline for more in-depth habitat mapping studies. For example, prediction of semantically distinct classes as “submarine dune” and “bedrock outcrop” can be precise and reliable. Nonetheless, at present state FCNNs are not suitable for tasks that require more refined geomorphological classifications, as for the recognition of detailed morphogenetic processes.</p