Accurate, real-time estimation of barge quantity on inland waterways remains a critical challenge due to the non-self-propelled nature of barges and the limitations of existing monitoring systems. This study introduces a novel method to use Automatic Identification System (AIS) vessel tracking data to predict the number of barges in tow using Machine Learning (ML). To train and test the model, barge instances were manually annotated from satellite scenes across the Lower Mississippi River. Labeled images were matched to AIS vessel tracks using a spatiotemporal matching procedure. A comprehensive set of 30 AIS-derived features capturing vessel geometry, dynamic movement, and trajectory patterns were created and evaluated using Recursive Feature Elimination (RFE) to identify the most predictive variables. Six regression models, including ensemble, kernel-based, and generalized linear approaches, were trained and evaluated. The Poisson Regressor model yielded the best performance, achieving a Mean Absolute Error (MAE) of 1.92 barges using 12 of the 30 features. The feature importance analysis revealed that metrics capturing vessel maneuverability such as course entropy, speed variability and trip length were most predictive of barge count. The proposed approach provides a scalable, readily implementable method for enhancing Maritime Domain Awareness (MDA), with strong potential applications in lock scheduling, port management, and freight planning. Future work will expand the proof of concept presented here to explore model transferability to other inland rivers with differing operational and environmental conditions.

This study presents a machine learning approach to predict the number of barges transported by vessels on inland waterways using tracking data from the Automatic Identification System (AIS). While AIS tracks the location of tug and tow vessels, it does not monitor the presence or number of barges transported by those vessels. Understanding the number and types of barges conveyed along river segments, between ports, and at ports is crucial for estimating the quantities of freight transported on the nation's waterways. This insight is also valuable for waterway management and infrastructure operations impacting areas such as targeted dredging operations, and data-driven resource allocation. Labeled sample data was generated using observations from traffic cameras located along key river segments and matched to AIS data records. A sample of 164 vessels representing up to 42 barge convoys per vessel was used for model development. The methodology involved first predicting barge presence and then predicting barge quantity. Features derived from the AIS data included speed measures, vessel characteristics, turning measures, and interaction terms. For predicting barge presence, the AdaBoost model achieved an F1 score of 0.932. For predicting barge quantity, the Random Forest combined with an AdaBoost ensemble model achieved an F1 score of 0.886. Bayesian optimization was used for hyperparameter tuning. By advancing predictive modeling for inland waterways, this study offers valuable insights for transportation planners and organizations, which require detailed knowledge of traffic volumes, including the flow of commodities, their destinations, and the tonnage moving in and out of ports.

Amid rising tensions in the South China Sea, the People's Liberation Army (PLA) has developed a low-cost fast deployable air defense system using radar reflector balloons to protect military facilities from aerial attacks. In the era of AI technology, the Chinese military is opting for a rather simplistic method to protect critical infrastructure. The PLA demonstrated its latest technique at a joint drill, called the Zhejiang Golden Shield-22, conducted by the Chinese military and local units in November. It involved the use of radar reflector balloons to safeguard military facilities in case of a long-range missile or drone attack, according to a report on The War Zone. It included the deployment of AD air balloons on UAVs avenues of approach&energy assets visual camouflage (Ukraine lesson learnt) pic.twitter.com/m21KrBIVnZ

It's been a tough year for elegant terns in Southern California. A drone crash in June forced an estimated 3,000 of the sleek seabirds with their pointed orange bills to abandon their eggs on Bolsa Chica Ecological Reserve in Orange County. Experts say it's possible that many of the birds set up camp on two commercial barges in nearby Long Beach Harbor. Now droves of the baby birds are falling into the ocean and drowning. "They basically landed on the barge a day or so, and it may have been two or three days, after the incident involving the drones when they left Bolsa Chica," said Tim Daly, spokesman for California Department of Fish and Wildlife.

The scene around the flooded Whitehill Yeo pit in Devon, UK, resembles a lunar landscape. Until it was abandoned just a few years ago, an endless stream of diesel trucks carried china clay out of the mine seven days a week. But don't be fooled by the silence: this is very much an active site. It's just that all the excavation is happening deep beneath the placid waters. This is a test bed, the first, for a new type of mining by underwater robots.

A grainy video uploaded to YouTube appears to show a small object flying over the SpaceX rocket moments before it exploded. SpaceX said an'anomaly' had occurred while the rocket was being loaded with fuel. No was injured in the blast. The rocket's payload, an Israeli-built communications satellite for Facebook due to launch on Saturday, was also destroyed. A video of the blast, posted to the site by Steve Svensson, appears to show a silver spherical object flying close to rocket when the blast happened.

Elon Musk has released video footage of SpaceX's latest landing attempt, in which the aerospace firm lost a Falcon 9 rocket. The Falcon 9 took off from Cape Canaveral in Florida on Wednesday, carrying two communications satellites into orbit. Looks like early liquid oxygen depletion caused engine shutdown just above the deck pic.twitter.com/Sa6uCkpknY Head of SpaceX, Elon Musk, has released video footage showing just how close the first stage of this week's rocket launch came to successfully landing on the floating barge. The Falcon 9 appeared to be on course for an upright landing before it was lost in clouds of smoke.

CAPE CANAVERAL, Fla. -- The first stage of a SpaceX Falcon 9 rocket launched Friday has returned to the Space Coast. SpaceX confirmed the booster's early morning Tuesday arrival at Port Canaveral on its official Twitter and Instagram accounts around 2:30 a.m. In a photo, the charred first stage of the rocket stands tall on the "Of Course I Still Love You" autonomous ship near the port's 273-foot-tall cranes. A crane will lift the 14-story rocket stage off the boat onto a stand, before it is transported to a hangar at Kennedy Space Center or Cape Canaveral Air Force Station. The operation's exact timeframe is unclear, but the stage probably will be at the port for at least Tuesday before it is moved. If the rocket stage is deemed in good enough condition after inspections and multiple test firings of its nine Merlin 1D engines, CEO Elon Musk said SpaceX could try to launch it again as soon as June.