# xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery

arXiv: https://arxiv.org/abs/2206.00897 · HTML: https://mlsanity.com/paper/2206.00897/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 58 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [Target detection in synthetic aperture radar imagery: a state-of-the-art survey](https://mlsanity.com/paper/1804.04719/) (2018)

## Built on by (most cited first, 25 of 25)

- [Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning](https://mlsanity.com/paper/2212.14532/) (2022)
- [SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2211.15660/) (2022)
- [TerraMind: Large-Scale Generative Multimodality for Earth Observation](https://mlsanity.com/paper/2504.11171/) (2025)
- [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024)
- [Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data](https://mlsanity.com/paper/2401.17600/) (2024)
- [Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation](https://mlsanity.com/paper/2503.10845/) (2025)
- [GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots](https://mlsanity.com/paper/2404.15500/) (2024)
- [Multi-Agent Geospatial Copilots for Remote Sensing Workflows](https://mlsanity.com/paper/2501.16254/) (2025)
- [Evaluating Tool-Augmented Agents in Remote Sensing Platforms](https://mlsanity.com/paper/2405.00709/) (2024)
- [WildSAT: Learning Satellite Image Representations from Wildlife Observations](https://mlsanity.com/paper/2412.14428/) (2024)
- [xT: Nested Tokenization for Larger Context in Large Images](https://mlsanity.com/paper/2403.01915/) (2024)
- [Learning De-Biased Representations for Remote-Sensing Imagery](https://mlsanity.com/paper/2410.04546/) (2024)
- [Satellite Imagery and AI: A New Era in Ocean Conservation, from Research to Deployment and Impact (Version. 2.0)](https://mlsanity.com/paper/2312.03207/) (2023)
- [Non-Visible Light Data Synthesis and Application: A Case Study for Synthetic Aperture Radar Imagery](https://mlsanity.com/paper/2311.17486/) (2023)
- [Improving satellite imagery segmentation using multiple Sentinel-2 revisits](https://mlsanity.com/paper/2409.17363/) (2024)
- [A comprehensive GeoAI review: Progress, Challenges and Outlooks](https://mlsanity.com/paper/2412.11643/) (2024)
- [AGI for the Earth, the path, possibilities and how to evaluate intelligence of models that work with Earth Observation Data?](https://mlsanity.com/paper/2508.06057/) (2025)
- [NASTaR: NovaSAR Automated Ship Target Recognition Dataset](https://mlsanity.com/paper/2512.18503/) (2025)
- [FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method](https://mlsanity.com/paper/2404.18245/) (2024)
- [Efficient SAR Vessel Detection for FPGA-Based On-Satellite Sensing](https://mlsanity.com/paper/2507.04842/) (2025)
- [Maritime object classification with SAR imagery using quantum kernel methods](https://mlsanity.com/paper/2512.11367/) (2025)
- [DarkVesselNet: Multi-Modal Remote Sensing and Trajectory Reasoning for Dark Vessel Detection](https://mlsanity.com/paper/2606.00445/) (2026)
- [Changing Modalities: Adapting Remote Sensing Models to New Satellites and Sensors](https://mlsanity.com/paper/2606.23356/) (2026)
- [Diagnosing Aerial-View Object Detectors with Foundational Image Generative Models](https://mlsanity.com/paper/2607.02718/) (2026)
- [SAR Vessel Detection and Gross Tonnage Estimation from Heterogeneous Datasets for Dark Vessel Identification](https://mlsanity.com/paper/2607.18051/) (2026)

## Abstract (arXiv)

Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that do not show up in conventional monitoring systems -- known as ``dark vessels'' -- is key to managing and securing the health of marine environments. With the rise of satellite-based synthetic aperture radar (SAR) imaging and modern machine learning (ML), it is now possible to automate detection of dark vessels day or night, under all-weather conditions. SAR images, however, require a domain-specific treatment and are not widely accessible to the ML community. Maritime objects (vessels and offshore infrastructure) are relatively small and sparse, challenging traditional computer vision approaches. We present the largest labeled dataset for training ML models to detect and characterize vessels and ocean structures in SAR imagery. xView3-SAR consists of nearly 1,000 analysis-ready SAR images from the Sentinel-1 mission that are, on average, 29,400-by-24,400 pixels each. The images are annotated using a combination of automated and manual analysis. Co-located bathymetry and wind state rasters accompany every SAR image. We also provide an overview of the xView3 Computer Vision Challenge, an international competition using xView3-SAR for ship detection and characterization at large scale. We release the data (hrefhttps://iuu.xview.us/https://iuu.xview.us/) and code (hrefhttps://github.com/DIUx-xViewhttps://github.com/DIUx-xView) to support ongoing development and evaluation of ML approaches for this important application.