arXiv subjects

Valentina Staneva

Publications and source records attributed to Valentina Staneva.

4 recordsLinked to original sources

arXiv2026

An open-source, scalable workflow for organizing echosounder data for machine learning applications

Echosounders, or high-frequency active sonar systems, have become standard tools for quantifying and mapping the distribution of marine organisms in fisheries or ecological surveys. Conventional echosounder data analysis often relies on human annotation of echograms, which are sonar imagery formed by echo intensity. Over the past decade, in parallel with the exponentially growing volume of echosounder data, there has been a corresponding increase in the development of machine learning (ML) methods that operate primarily on echograms as images. However, echograms are not simply images: they are associated with specific spatiotemporal coordinates that are essential for alignment with survey events, human annotations, and other oceanographic datasets. We present a generalizable two-stage workflow for constructing analysis-ready datasets for ML development tailored for echograms from transect-based surveys, in which (1) acoustic data are partitioned according to transect designation, and (2) masks are created from annotations referencing user-defined uniform spatiotemporal echo data grid. Importantly, ancillary information, such as geospatial coordinates and oceanographic measurements, is propagated across processing stages to preserve the essential contextual information for downstream analyses. We demonstrate the scalability of our workflow implementation based on two open-source software libraries, Echopype and Echoregions, using two example fisheries survey datasets. We additionally provide an executable tutorial that guides readers through the computational implementation of this workflow. Together, these elements provide a scalable and generalizable framework for creating analysis-ready echosounder datasets for ML applications.

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arXiv2021

Interoperable and scalable echosounder data processing with Echopype

Echosounders are high-frequency sonar systems used to sense fish and zooplankton underwater. Their deployment on a variety of ocean observing platforms is generating vast amounts of data at an unprecedented speed from the oceans. Efficient and integrative analysis of these data, whether across different echosounder instruments or in combination with other oceanographic datasets, is crucial for understanding marine ecosystem response to the rapidly changing climate. Here we present Echopype, an open-source Python software library designed to address this need. By standardizing data as labeled, multi-dimensional arrays encoded in the widely embraced netCDF data model following a community convention, Echopype enhances the interoperability of echosounder data, making it easier to explore and use. By leveraging scientific Python libraries optimized for distributed computing, Echopype achieves computational scalability, enabling efficient processing in both local and cloud computing environments. Echopype's modularized package structure further provides a unified framework for expanding support for additional instrument raw data formats and incorporating new analysis functionalities. We plan to continue developing Echopype by supporting and collaborating with the echosounder user community, and envision that the growth of this package will catalyze the integration of echosounder data into broader regional and global ocean observation strategies.

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arXiv2020

Compact representation of temporal processes in echosounder time series via matrix decomposition

The recent explosion in the availability of echosounder data from diverse ocean platforms has created unprecedented opportunities to observe the marine ecosystems at broad scales. However, the critical lack of methods capable of automatically discovering and summarizing prominent spatio-temporal echogram structures has limited the effective and wider use of these rich datasets. To address this challenge, we develop a data-driven methodology based on matrix decomposition that builds compact representation of long-term echosounder time series using intrinsic features in the data. In a two-stage approach, we first remove noisy outliers from the data by Principal Component Pursuit, then employ a temporally smooth Nonnegative Matrix Factorization to automatically discover a small number of distinct daily echogram patterns, whose time-varying linear combination (activation) reconstructs the dominant echogram structures. This low-rank representation provides biological information that is more tractable and interpretable than the original data, and is suitable for visualization and systematic analysis with other ocean variables. Unlike existing methods that rely on fixed, handcrafted rules, our unsupervised machine learning approach is well-suited for extracting information from data collected from unfamiliar or rapidly changing ecosystems. This work forms the basis for constructing robust time series analytics for large-scale, acoustics-based biological observation in the ocean.

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arXiv2018

Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery

Rapid damage assessment is of crucial importance to emergency responders during hurricane events, however, the evaluation process is often slow, labor-intensive, costly, and error-prone. New advances in computer vision and remote sensing open possibilities to observe the Earth at a different scale. However, substantial pre-processing work is still required in order to apply state-of-the-art methodology for emergency response. To enable the comparison of methods for automatic detection of damaged buildings from post-hurricane remote sensing imagery taken from both airborne and satellite sensors, this paper presents the development of benchmark datasets from publicly available data. The major contributions of this work include (1) a scalable framework for creating benchmark datasets of hurricane-damaged buildings and (2) public sharing of the resulting benchmark datasets for Greater Houston area after Hurricane Harvey in 2017. The proposed approach can be used to build other hurricane-damaged building datasets on which researchers can train and test object detection models to automatically identify damaged buildings.

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