Front. Mar. Sci. Frontiers in Marine Science Front. Mar. Sci. 2296-7745 Frontiers Media S.A. 10.3389/fmars.2022.1076775 Marine Science Original Research Comparing spatial patterns of marine vessels between vessel-tracking data and satellite imagery Nakayama Shinnosuke 1 * Dong WenXin 1 Correro Richard G. 1 Selig Elizabeth R. 1 Wabnitz Colette C.C. 1 2 Hastie Trevor J. 3 4 Leape Jim 1 Yeung Serena 4 Micheli Fiorenza 1 5 6 1 Stanford Center for Ocean Solutions, Stanford University, Stanford, CA, United States 2 Institute for the Oceans and Fisheries, The University of British Columbia, Vancouver, BC, Canada 3 Department of Statistics, Stanford University, Stanford, CA, United States 4 Department of Biomedical Data Science, Stanford University, Stanford, CA, United States 5 Hopkins Marine Station, Stanford University, Pacific Grove, CA, United States 6 Oceans Department, Stanford University, Stanford, CA, United States

Edited by: Simone Marini, National Research Council (CNR), Italy

Reviewed by: Qiqi Zhu, China University of Geosciences Wuhan, China; Valmik Nikam, Veermata Jijabai Technological Institute, India

*Correspondence: Shinnosuke Nakayama, shinn1@stanford.edu

This article was submitted to Ocean Observation, a section of the journal Frontiers in Marine Science

 20 01 2023 2022 9 1076775 21 10 2022 19 12 2022 Copyright © 2023 Nakayama, Dong, Correro, Selig, Wabnitz, Hastie, Leape, Yeung and Micheli 2023 Nakayama, Dong, Correro, Selig, Wabnitz, Hastie, Leape, Yeung and Micheli

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Monitoring marine use is essential to effective management but is extremely challenging, particularly where capacity and resources are limited. To overcome these limitations, satellite imagery has emerged as a promising tool for monitoring marine vessel activities that are difficult to observe through publicly available vessel-tracking data. However, the broader use of satellite imagery is hindered by the lack of a clear understanding of where and when it would bring novel information to existing vessel-tracking data. Here, we outline an analytical framework to (1) automatically detect marine vessels in optical satellite imagery using deep learning and (2) statistically contrast geospatial distributions of vessels with the vessel-tracking data. As a proof of concept, we applied our framework to the coastal regions of Peru, where vessels without the Automatic Information System (AIS) are prevalent. Quantifying differences in spatial information between disparate datasets—satellite imagery and vessel-tracking data—offers insight into the biases of each dataset and the potential for additional knowledge through data integration. Our study lays the foundation for understanding how satellite imagery can complement existing vessel-tracking data to improve marine oversight and due diligence.

 automatic information system (AIS) automated vessel detection data integration deep learning fisheries management log-Gaussian Cox process satellite imagery spatial statistics Office of Advanced Cyberinfrastructure10.13039/100000105 Introduction

Geospatial information on marine vessels offers powerful insights that can be used for marine surveillance to strengthen national security, at-sea safety, fisheries management, and biological monitoring. A widely available source for such information is satellite-based vessel-tracking data collected through the Automatic Identification System (AIS). Originally designed to avoid vessel collisions, vessels larger than specific sizes (for example, about 20 m in the U.S. and 15 m in the EU) engaging on international voyages are required to transmit their geolocations to AIS (International Maritime Organization, 2015). Thanks to their extensive spatial and temporal coverages, AIS data are successfully applied to various aspects of marine monitoring, such as global fishing activities on the high seas (Kroodsma et al., 2018), anomaly detection of vessel behavior (Zhen et al., 2017), and animal conservation (Guzman et al., 2013; Queiroz et al., 2019; Womersley et al., 2022).

Despite their popularity, AIS data have critical limitations for marine surveillance. First, AIS suffers from drastic signal interference when too many vessels transmit signals in one location (Eriksen et al., 2006), making it impossible to distinguish high-density areas from low-density ones. Second, vessels on domestic voyages or of small sizes are not required to transmit AIS signals, biasing our knowledge of marine use towards large vessels on the high seas. Despite the significant contribution of small-scale fisheries to seafood catches (Mills et al., 2011), their importance to livelihoods and nutrition (Short et al., 2021), and their potential for ecological impacts through bycatch or overfishing (Peckham et al., 2007), the size selectivity of AIS data largely excludes small-scale fisheries, limiting our understanding of their effort at a global scale. Finally, vessels can cheat the system by turning off AIS transmitters onboard (Welch et al., 2022) or by spoofing their positions or identities (Katsilieris et al., 2013), making AIS less suitable for detecting illicit behavior (see Balduzzi et al., 2014 for more details of the threats to AIS). For commercial fishing vessels, many countries mandate the Vessel Monitoring System (VMS) to track geolocations of vessels registered to their countries via satellite, but national governments curate the data and often do not make them publicly available. The limitations of these data suggest that additional data are needed to supplement vessel-tracking data for a more complete understanding of vessel dynamics.

The desire to overcome current data limitations has led to a greater exploration of the possibilities offered by satellite imagery. The most commonly used is Synthetic Aperture Radar (SAR) imagery (Pichel and Clemente-Colón, 2000; Bovenga et al., 2014; Snapir et al., 2019), which uses beam-scanning radar to capture images independent of light and cloud coverage. Optical imagery has been emerging as an alternative to SAR imagery thanks to its higher resolution, more frequent updating, broader geospatial coverage, and easier accessibility compared to SAR data (Kanjir et al., 2018). Although optical satellite imagery could complement existing vessel-tracking data by providing additional information, its use for marine surveillance is challenged by two major limitations. One is the difficulty in classifying vessel types (such as fishing vessels, leisure boats, passenger vessels, cargo ships, and tankers) at typical image resolutions (for example, 3 m per pixel), hindering applications to specific research domains. Another limitation is the inability to track vessel movement and infer their activities given low update rates (for example, once a day).

Limitations of both vessel-tracking data and satellite imagery could be alleviated through data integration. In imagery-based vessel detection, data integration is often used to enhance geospatial coverage and detection accuracy by cross-matching vessels with vessel-tracking data through interpolation (Brekke et al., 2008; Jubelin and Khenchaf, 2014; Mazzarella et al., 2015). This approach contributes to estimating the pervasiveness of “dark” vessels operating without AIS signals (Hsu et al., 2019; Kurekin et al., 2019), or identifying fishing vessels operating illegally within foreign EEZs (Park et al., 2020a). In contrast to the gaining popularity of data integration, however, satellite imagery has not been used to complement geospatial information to include detection of small vessels that do not transmit AIS signals, or to quantify interactions (e.g., spatial overlap on fishing grounds) between industrial fleets and small-scale fisheries, for example. To enable broader use of satellite imagery for marine surveillance, it is important to understand what information satellite imagery could add to existing vessel-tracking data.

In this study, we present an analytical framework to evaluate the possibilities of satellite imagery to complement existing vessel-tracking data. Specifically, we illustrate (1) a deep learning approach for detecting vessels in optical satellite imagery and (2) a statistical method to compare vessel distributions detected in satellite imagery with AIS data towards assessing the added value of satellite imagery. As a proof of concept, we applied this analytical framework to two ports in Peru and explored the new insights generated through data integration.

 Methods Satellite imagery data

Images taken by PlanetScope satellites were sourced from Planet (www.planet.com). We downloaded satellite scenes from more than 40 locations on the Peruvian shoreline from 2016 to 2021. Each scene is an RGB-NIR image at a resolution of 3 m per pixel, having a cloud coverage rate lower than 3%. We processed the data by manually cropping off land area and gridding each scene into a set of 299 × 299 × 3 RGB images. Then, we created our own dataset to train and test the model described below by manually labeling vessel bounding boxes. Although several datasets of vessel images exist in the public domain (Gallego et al., 2018; Kaggle, 2018), the images we worked with were at a lower resolution, less well lit, and contained smaller vessels. Therefore, we opted to create our own dataset to improve the model performance (Quinonero-Candela et al., 2008). We ended up with 308 images containing at least one vessel and 11,538 images containing no vessels. In total, there were 3,046 vessel instances in the dataset.

 Automated vessel detection model

To automatically locate vessels in satellite imagery, we used Faster R-CNN (Ren et al., 2015) with ResNet-50 (He et al., 2016) backbone pretrained on the COCO image dataset (Lin et al., 2014). We opted for Faster R-CNN as it is one of the state-of-the-art deep learning models for object detection. There are other deep learning models with different architectures for object detection, such as U-Net (Ronneberger et al., 2015) and YOLO (Redmon et al., 2016), which could be equally suitable. Our Faster R-CNN model was trained end-to-end using SGD optimizer with a learning rate of 0.001, momentum of 0.9, weight decay of 0.0005, and batch size of 16. The Faster R-CNN model was trained for 20 epochs, as determined by the best validation F-1 score. To improve detection accuracy and speed, we first ran the image through a binary classifier to determine whether there were any vessels present in the image. The classifier model is a ResNet-50 model pretrained on ImageNet1k and fine-tuned on the same dataset as the Faster R-CNN detector model. The classifier model was trained for 4 epochs, as determined by the best validation F-1 score. To address dataset imbalance, we sampled an equal number of images containing vessels and images without vessels for each mini-batch when training both models. To augment our dataset, each training image was first randomly flipped horizontally with a probability of 0.5, then vertically with a probability of 0.5. Validation and test splits are each sourced from a location along the shore not contained in the training dataset. The model was implemented in the Python package torchvision ver. 0.13.0.

The model was evaluated on the hold-out test set consisting of 41 vessels and 269 images without vessels at IoU (Intersection over Union) = 0.1. If one needs a more reliable estimate for model performance, K-fold cross-validation is preferrable to hold-out, although it requires more computational resources and time.

 Comparing satellite imagery with AIS data

To assess whether our approach provides additional information relative to existing vessel-tracking data, we explored the similarity of vessel distributions detected in satellite imagery and the AIS data using a log-Gaussian Cox process (LGCP) model (Møller et al., 1998). The utility of this spatial model lies in the parametric characterization of the underlying process of point patterns for interpretations. Thanks to its flexibility, LGCP is widely used to model spatial point patterns in various fields, including ecology (Conn et al., 2014; Waagepetersen et al., 2016), epidemiology (Li et al., 2012; Giorgi et al., 2016), and seismology (D’Angelo et al., 2022), but has not been applied to marine vessels yet. In this paper, we introduce the LGCP model as a powerful instrument to compare vessel distributions between AIS data and satellite imagery. The model could readily be extended to ask more complex questions by incorporating covariates, such as fish distributions and ocean climate data. If flexibility of the process-based approach is not required, one can compare two point patterns using simpler distance-based methods, such as nearest neighbor distance and Ripley’s K function (Diggle, 2013).

LGCP characterizes spatial point patterns as a Poisson point process that is conditionally dependent on a spatially varying intensity field modeled as a Gaussian process. That is, for a set of locations s in a region of interest D, we represent the Poisson intensity function λ(s) by log λ(s) = z(s), where z(s) is a realization of a Gaussian process for s ∈ D. The univariate LGCP can be naturally extended to model two datasets as point processes over D (Møller et al., 1998; Gelfand and Schliep, 2018). Assuming that each dataset follows an independent Poisson process with intensity λ 1(s) and λ 2(s), respectively, they can be modeled together as LGCP with log intensity z (s) = (z 1(s), z 2(s))T, where z (s) is a vector of Gaussian process with μ = (μ 1, μ 2)T and covariance functions c(s, s′) = cov(z 1(s), z 2(s′)) for s ∈ D and s′ ∈ D (Gelfand and Schliep, 2018).

Using a linear model of coregionalization (Gelfand et al., 2004), we can rewrite the log intensity as z (s) = β + AV(s), where β = (β 1, β 2)T is a vector of intercepts, A is a lower triangular matrix with element aij , and V(s) = (V 1(s), V 2(s))T is a vector of independent Gaussian processes V 1(s) and V 2(s) with mean 0 and variance 1, respectively (Gelfand and Schliep, 2018). Here, coefficient a 21 of the lower triangular matrix A indicates the magnitude of dependence between the two intensity fields, where positive and negative values indicate spatial clustering and inhibition of points of the two datasets, respectively. When there is no local spatial dependence between the intensity fields, we expect a 21 to be close to zero.

This model can also accommodate covariates that may explain vessel distributions. We can easily do so by extending the log intensity as z (s) = X(s) γ + AV(s), where γ is a vector consisting of an intercept and slopes of variables of interest corresponding to a design matrix X(s).

 Applying the framework

As a proof of concept, we applied our framework of detecting vessels in satellite imagery and comparing vessel distributions with vessel-tracking data to areas off the ports of Talara and Paita in northern Peru ( Figure 1 ). We selected these locations because vessels on the images were likely to consist of fishing vessels and differ from the AIS data, considering that both ports are located in the Piura region, where small-scale fisheries are predominant (Alfaro-Shigueto et al., 2010). Although large commercial fishing vessels are not allowed to operate near the coast of Peru, the port of Talara experiences heavy traffic by AIS-carrying vessels (mostly passengers and tankers), whereas the port of Paita is less used by them. For each area, we aggregated vessel locations (10 × 10 km2) from January 1 to December 31, 2020. To explore whether the information of vessel distribution was different from that from the AIS data, we obtained the AIS data pre-processed by Global Fishing Watch (https://globalfishingwatch.org). For each vessel on the days when the satellite images were taken, we subsampled the AIS data by selecting one vessel location around 10 am at a local time (time when the satellite images were taken) within a 1-hour buffer to mirror the frequency of the satellite imagery data.

Study sites and vessel distributions. (A) Green squares near Talara and Paita in Peru indicate the areas where we applied the automated vessel detection model. (B) Locations of vessels from the AIS data and the optical satellite imagery near Talara. (C) Locations of vessels near Paita. AIS data is displayed in red circles, satellite in blue crosses. Data were collected from January 1 to December 31, 2020. For the AIS data, one vessel location was subsampled to mirror the sampling frequency of the satellite imagery data.

To apply the bivariate LGCP model, we divided each area into 10 × 10 grid cells and summed the number of vessels in each cell (1 × 1 km2) for each point type (AIS or satellite imagery). We fitted a bivariate LGCP model to the vessel counts using an exponentiated quadratic kernel as a covariance function of Gaussian process V 1(s) and V 2(s). For priors, we used a uniform distribution between 100 and 5,000 for length scale l of the kernel. For the elements of matrix A, we used an exponential distribution with λ = 1 for a 11 and a 22 and a normal distribution with μ = 0 and σ = 10 for a 21. For the intercepts β 1 and β 2, we used a normal distribution with μ = 0 and σ = 10. We checked the adequateness of the model structure and priors by diagnosing the trace plots and posterior predictive distribution. The posteriors were obtained over 2 chains, with 1,000 draws after 1,000 burn-ins each, using the No-U-Turn Sampler (Hoffman and Gelman 2014). The model was fitted in the Python package PyMC ver. 4.4.0. The script and the model performance checks can be found in the GitHub repository (https://github.com/stanford-cos/lgcp).

 Results

Our framework started with automatically detecting marine vessels in optical satellite imagery. For the performance of the vessel detection model, we report F1 score, precision, recall, and AP (Average Precision) following standard convention. AP measures the area under the precision-recall curve by changing the model confidence score from 0 to 1. Our ResNet-50 classifier and Faster R-CNN detector pipeline performs at F1 = 0.84, precision = 0.87, recall = 0.80, and AP = 0.72. F1, precision, and recall are measured using a classifier confidence threshold of 0.1 and detector confidence threshold of 0.5 (see Figure 2 for example).

Example of vessel detection. Top: original images. Bottom: vessel detection algorithm applied to the corresponding images. Boxes indicate the vessels detected by our algorithm.

The model detected 118 vessels in Talara and 151 vessels in Paita ( Figure 1 ). In Talara, vessels were concentrated in the northeastern part of the focal region near the port entrance, as well as the southeastern part. We observed a similar distribution in the AIS vessels. By contrast, in Paita, the distribution of vessels detected in satellite imagery were different from that of the AIS data. In the satellite imagery, a high concentration of vessels was detected in the southern part of the focal region outside the Port of Paita, whereas AIS vessels were aggregated in the southeastern part of the focal region near the port entrance ( Figure 1 ).

The point pattern analysis showed that differences in vessel distributions between satellite imagery and AIS data depended on locations. In the bivariate LGCP model fitted to the point data in Talara, the positive value of a 21 indicates that the vessel distributions from the AIS data and the satellite imagery partly share the common underlying process ( Table 1 ). That is, the vessel distributions obtained from the satellite imagery and the AIS data show relatively similar patterns. In Paita, by contrast, the small value of a 21 with 94% HDI overlapping zero indicates little dependence between the two distributions ( Table 1 ). That is, the spatial point patterns in the two samples were likely to be underpinned by different processes in this region, which was characterized by a 11 and a 22.

Posterior estimates for the bivariate log-Gaussian Cox process (LGCP) model fitted to vessel distributions in two locations in Peru.

Talara Paita
l 885 (709, 1086) 3150 (2077, 4460)
β 1 −1.307 (−2.242, −0.293) −1.270 (−3.972, 1.599)
β 2 −1.108 (−1.884, −0.317) 0.277 (−2.954, 3.473)
a 11 2.170 (1.615, 2.733) 2.815 (1.347, 4.332)
a 22 1.128 (0.687, 1.622) 3.068 (1.545, 4.894)
a 21 1.164 (0.678, 1.723) −0.266 (−2.765, 2.336)

Values in parentheses are 94% highest density intervals (HDI). The positive value of a 21 in Talara reveals clustering of vessel positions between AIS and satellite imagery, whereas the weak value of a 21 overlapping zero in Paita indicates no dependence.

Visualization of the estimated model parameters illuminated contrasting differences in the processes that underpinned the vessel distributions ( Figure 3 ). When the posterior predictive distributions of the estimated intensities λ 1(s) and λ 2(s) were resampled for the 0.2 × 0.2 km2 cells in Talara, we see dense vessel distributions in the northeast corner of the focal region and relatively low densities elsewhere in both the AIS data and satellite imagery. In Paita, we see a high density of AIS-carrying vessels near the port entry in the southeast corner of the focal region, but vessels detected in the satellite imagery are concentrated outside the port ( Figure 3 ).

Model estimates of vessel distributions. (A) AIS data in Talara, (B) satellite imagery in Talara, (C) AIS data in Paita, and (D) satellite imagery in Paita. Values are the sample means of the posterior predictive distributions corresponding to 0.2 × 0.2 km2 cells. For all images the warmer (yellow) the color the higher the number of vessels detected. Note that the intensity scales vary across the 4 images.

 Discussion

This study demonstrates a potential application of satellite imagery to complement publicly available vessel-tracking data. Specifically, we show that satellite imagery could provide additional information to AIS datasets on vessel distribution. In our proof of concept, we found that the vessel distribution in the satellite imagery was independent of that in the AIS data in Paita. Considering the prevalence of small-scale fisheries in the area (Alfaro-Shigueto et al., 2010), it is possible that the satellite imagery detected small vessels without AIS devices. By contrast, in Talara, we found that vessel distributions were more similar between AIS and satellite imagery. This indicates that vessels detected in satellite imagery would likely be AIS vessels or non-AIS vessels that show a geospatial distribution similar to AIS vessels. The differences in AIS and satellite imagery vessel distributions could depend on many factors, including location, time, and spatial and temporal resolutions of interest, and our framework allows systematic investigation to test such factors. These new insights could then be used to strengthen marine surveillance under limited resources. For example, it could help identify and prioritize areas heavily utilized by small-scale fishing fleets for additional surveillance methods.

The analytical framework we introduced here can also be used as a robust foundation to investigate factors that bring about differences in vessel distributions between data sources. For example, we could learn the dynamics of vessels that do not transmit AIS signals and explore the potential factors that underpin differences in distribution by systematically comparing geospatial patterns of vessels between AIS data and satellite imagery across locations and over time. Such an approach is particularly useful when trying to glean a comprehensive picture of vessel dynamics for a given area. In addition, the LGCP model can easily be extended to include covariates, such as water temperature, chlorophyll-a, bathymetric features, and distance to marine protected areas, to explore factors that drive realized geospatial distributions of marine vessels.

The research field of automated vessel detection has been rapidly advancing, with the improvement of deep learning techniques for object detection (Cheng et al., 2016; Kang et al., 2017; Liu et al., 2021a) and the adoption of novel approaches, such as use of wakes behind moving vessels (Graziano and Renga, 2021; Liu et al., 2021b) and multispectral imagery (Wang et al., 2021). The field of vessel detection will further advance with the increasing accessibility to super high-resolution imagery (Jérôme, 2019), which could allow the classification of vessel types and fishing gears in the near future. These advances in technology and data quality will accelerate the possibilities of the applications of satellite imagery, and this study exemplifies one direction to embrace unprecedented opportunities, in addition to the traditional cross-matching with vessel-tracking data.

Ocean problems are often complex and multifaceted, making it challenging to address with a single data source. Data integration offers a powerful framework that could highlight where due diligence is needed. Quantification of information differences illuminates the biases of each dataset. A clear understanding of such biases supports the effective integration of similar data types, such as VIIRS (Hsu et al., 2019), over-the-horizon radar (Ponsford et al., 2017; Yang et al., 2022), and hyperspectral imagery (Park et al., 2018; Park et al., 2020b). Ultimately, knowing the uniqueness of each data source will help gauge the likelihood of generating new knowledge beyond the original scope of each dataset to address specific questions through data integration. By looking through multiple lenses, we can triangulate the problems, shed light on different aspects, and discover novel insights. At the same time, our study echoes the importance of a multi-data approach and data sharing in marine surveillance (Dupont et al., 2020).

 Data availability statement

The datasets presented in this study can be found in online repositories https://github.com/stanford-cos/lgcp.

 Author contributions

All authors helped design the study. WD and RC led the satellite imagery analysis and performance assessment. SN performed spatial data analysis and drafted the manuscript. SY, TH, CW, ES and FM supervised the analysis. All authors contributed to the article and approved the submitted version.

 Funding

This research was funded by National Science Foundation (OAC #1934578) and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) Seed Grant. RC was financially supported by the Undergraduate Summer Research Program by the Department of Statistics at Stanford University.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2022.1076775/full#supplementary-material

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