Towards Robotic Lake Maintenance: Integrating SONAR and Satellite Data to Assist Human Operators
- URL: http://arxiv.org/abs/2603.06266v1
- Date: Fri, 06 Mar 2026 13:30:50 GMT
- Title: Towards Robotic Lake Maintenance: Integrating SONAR and Satellite Data to Assist Human Operators
- Abstract summary: This paper proposes a two-step approach to support targeted weed harvesting for the maintenance of artificial lakes.<n>An Unmanned Surface Vehicle (USV) equipped with multibeam SOund NAvigation and Ranging (SONAR) performs high-resolution bathymetric mapping to locate and quantify aquatic vegetation precisely.<n>Preliminary results demonstrate the feasibility of integrating satellite imagery and underwater acoustic sensing to improve vegetation management in artificial lakes.
- Score: 0.13048920509133805
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Artificial Water Bodies (AWBs) are human-made systems that require continuous monitoring due to their artificial biological processes. These systems demand regular maintenance to manage their ecosystems effectively. As a result of these artificial conditions, underwater vegetation can grow rapidly and must be harvested to preserve the ecological balance. This paper proposes a two-step approach to support targeted weed harvesting for the maintenance of artificial lakes. The first step is the initial detection of Submerged Aquatic Vegetation (SAV), also referred to in this paper as areas of interest, is performed using satellite-derived indices, specifically the Aquatic Plants and Algae (APA) index, which highlights submerged vegetation in water bodies. Subsequently, an Unmanned Surface Vehicle (USV) equipped with multibeam SOund NAvigation and Ranging (SONAR) performs high-resolution bathymetric mapping to locate and quantify aquatic vegetation precisely. This two-stage approach offers an effective human-robot collaboration, where satellite data guides the USV missions and boat skippers leverage detailed SONAR maps for targeted harvesting. This setup narrows the search space and reduces manual workload from human operators, making the harvesting process less labour-intensive for operators. Preliminary results demonstrate the feasibility of integrating satellite imagery and underwater acoustic sensing to improve vegetation management in artificial lakes.
Related papers
- STEMbot: A Compliant Robot for Under-Canopy Plant Navigation [22.4174880970008]
We present STEMbot, a miniature climbing robot system designed for autonomous navigation under plant canopies.<n>Unlike existing climbing platforms that lack on-board perception, STEMbot integrates a fully geometric PIN-SLAM pipeline with a semantic OcTree to achieve robust localization and mapping.
arXiv Detail & Related papers (2026-07-08T19:17:40Z) - Semantically-Aware Diver Activity Recognition Framework for Effective Underwater Multi-Human-Robot Collaboration [9.010955965403669]
We introduce DAR-Net, a framework that analyzes complex underwater scenes to classify diver activities.<n>Our contribution lies in a semantically guided learning formulation that couples transformer-based temporal reasoning with pixel-level scene supervision.<n> DAR-Net achieves promising accuracy in recognizing six distinct diver activities, outperforming state-of-the-art models.
arXiv Detail & Related papers (2026-06-10T17:40:19Z) - OceanGym: A Benchmark Environment for Underwater Embodied Agents [69.56465775825275]
OceanGym is the first comprehensive benchmark for ocean underwater embodied agents.<n>It is designed to advance AI in one of the most demanding real-world environments.<n>By providing a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for developing robust embodied AI.
arXiv Detail & Related papers (2025-09-30T17:09:32Z) - Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments [57.59857784298534]
We propose an integrated pipeline that combines Visual Place Recognition (VPR), feature matching, and image segmentation on video-derived images.<n>This method enables robust identification of revisited areas, estimation of rigid transformations, and downstream analysis of ecosystem changes.
arXiv Detail & Related papers (2025-03-06T05:13:19Z) - Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring [68.41400824104953]
This paper presents a vehicle prototype that addresses the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring.
The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth.
By means of a stereo-camera, it also can detect and locate macro-plastics in real environments.
arXiv Detail & Related papers (2024-10-08T10:35:32Z) - AI techniques for near real-time monitoring of contaminants in coastal waters on board future Phisat-2 mission [3.0049721990828084]
This article describes the opportunities and issues for the contaminants monitoring on the Phisat-2 mission.
The specific characteristics of this mission, with the tools made available, will be presented.
Preliminary promising results are discussed and in progress and future work introduced.
arXiv Detail & Related papers (2024-04-30T14:25:32Z) - Autonomous Underwater Robotic System for Aquaculture Applications [0.2499907423888049]
This work aims to develop a robotic-based automatic net defect detection system for aquaculture net pens oriented to on- ROV processing and real-time detection of different aqua-net defects such as biofouling, vegetation, net holes, and plastic.
The proposed system integrates both deep learning-based methods for aqua-net defect detection and feedback control law for the vehicle movement around the aqua-net to obtain a clear sequence of net images and inspect the status of the net via performing the inspection tasks.
arXiv Detail & Related papers (2023-08-26T10:45:39Z) - DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water
Extent with SAR Images using Knowledge Distillation [44.99833362998488]
We present DeepAqua, a self-supervised deep learning model that eliminates the need for manual annotations during the training phase.
We exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces.
Experimental results show that DeepAqua outperforms other unsupervised methods by improving accuracy by 7%, Intersection Over Union by 27%, and F1 score by 14%.
arXiv Detail & Related papers (2023-05-02T18:06:21Z) - In-situ Water quality monitoring in Oil and Gas operations [1.9857559596234144]
Many existing satellite-based monitoring studies utilize index-based methods to monitor large water bodies such as rivers and oceans.
We propose a new Water Quality Enhanced Index (WQEI) Model, which is designed to enable users to determine contamination levels in water bodies with weak reflectance patterns.
Our results show that 1) WQEI is a good indicator of water turbidity validated with 1200 water samples measured in the laboratory, and 2) by applying our method to commonly available satellite data (e.g. LandSat8), one can achieve high accuracy water quality monitoring efficiently in large regions.
arXiv Detail & Related papers (2023-01-20T20:56:52Z) - Towards Generating Large Synthetic Phytoplankton Datasets for Efficient
Monitoring of Harmful Algal Blooms [77.25251419910205]
Harmful algal blooms (HABs) cause significant fish deaths in aquaculture farms.
Currently, the standard method to enumerate harmful algae and other phytoplankton is to manually observe and count them under a microscope.
We employ Generative Adversarial Networks (GANs) to generate synthetic images.
arXiv Detail & Related papers (2022-08-03T20:15:55Z) - Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
A fully-autonomous aerial robot for high-speed object grasping has been proposed.
As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface.
Our approach has been validated in a challenging international competition and has shown outstanding results.
arXiv Detail & Related papers (2021-12-10T11:49:51Z) - Water Level Estimation Using Sentinel-1 Synthetic Aperture Radar Imagery
And Digital Elevation Models [0.0]
We propose a novel water level extracting approach, which employs Sentinel-1 Synthetic Aperture Radar imagery and Digital Elevation Model data sets.
Experiments show that the algorithm achieved a low average error of 0.93 meters over three reservoirs globally.
arXiv Detail & Related papers (2020-12-11T18:42:15Z) - Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using
Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity.
Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites.
This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
arXiv Detail & Related papers (2020-12-07T16:23:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.