# Enhancing Maritime Situational Awareness through End-to-End Onboard Raw Data Analysis

arXiv: https://arxiv.org/abs/2411.03403 · HTML: https://mlsanity.com/paper/2411.03403/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 78 references that are in the map.
- First posted on arXiv 5 Nov 2024.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020)
- [Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection](https://mlsanity.com/paper/2202.06934/) (2022)
- [Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images](https://mlsanity.com/paper/2205.13764/) (2022)
- [Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark](https://mlsanity.com/paper/2206.13996/) (2022)
- [Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression](https://mlsanity.com/paper/2110.13675/) (2021)
- [Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligence](https://mlsanity.com/paper/2305.11891/) (2023)
- [Residual Networks Behave Like Ensembles of Relatively Shallow Networks](https://mlsanity.com/paper/1605.06431/) (2016)
- [Rethinking Intersection Over Union for Small Object Detection in Few-Shot Regime](https://mlsanity.com/paper/2307.09562/) (2023)

## Built on by (most cited first, 5 of 5)

- [Explaining raw data complexity to improve satellite onboard processing](https://mlsanity.com/paper/2510.06858/) (2025)
- [Rethinking Satellite Image Restoration for Onboard AI: A Lightweight Learning-Based Approach](https://mlsanity.com/paper/2604.12807/) (2026)
- [TriCCOT: Tri-part Convolutional Conformal Transformer for Onboard Space Object Detection](https://mlsanity.com/paper/2609.08659/) (2026)
- [Thermal Anomaly Detection using Physics Aware Neuromorphic Networks: Comparison between Raw and L1C Sentinel-2 Data](https://mlsanity.com/paper/2604.18606/) (2026)
- [Raw Imagery Impacting Your AI: Should You Care?](https://mlsanity.com/paper/2609.38265/) (2026)

## Abstract (arXiv)

Satellite-based onboard data processing is crucial for time-sensitive applications requiring timely and efficient rapid response. Advances in edge artificial intelligence are shifting computational power from ground-based centers to on-orbit platforms, transforming the "sensing-communication-decision-feedback" cycle and reducing latency from acquisition to delivery. The current research presents a framework addressing the strict bandwidth, energy, and latency constraints of small satellites, focusing on maritime monitoring. The study contributes three main innovations. Firstly, it investigates the application of deep learning techniques for direct ship detection and classification from raw satellite imagery. By simplifying the onboard processing chain, our approach facilitates direct analyses without requiring computationally intensive steps such as calibration and ortho-rectification. Secondly, to address the scarcity of raw satellite data, we introduce two novel datasets, VDS2Raw and VDV2Raw, which are derived from raw data from Sentinel-2 and Vegetation and Environment Monitoring New Micro Satellite (VENuS) missions, respectively, and enriched with Automatic Identification System (AIS) records. Thirdly, we characterize the tasks' optimal single and multiple spectral band combinations through statistical and feature-based analyses validated on both datasets. In sum, we demonstrate the feasibility of the proposed method through a proof-of-concept on CubeSat-like hardware, confirming the models' potential for operational satellite-based maritime monitoring.