Machine learning
Researchers at Federal University Paraiba have developed a machine learning model that estimates daily precipitation in Brazil with high accuracy, without relying on ground-based data. The IMERG BraMaL-D model uses satellite-based precipitation data and 53 re-analysis variables from MERRA-2 to produce accurate daily precipitation estimations. The model outperformed other global
Technical institutes
A study published in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences has introduced a web-based geographic information system (WebGIS) platform to enhance glacier monitoring in the Italian Alps. The platform, developed by researchers at Polytechnic University Milan, integrates heterogeneous datasets and provides a low-cost, open-source
Technical institutes
Researchers at East China University of Technology have conducted a study on the ecological fragility and climate sensitivity of the Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) in China. The study aimed to investigate land use dynamics and ecological benefit changes within this region, which is
Technical institutes
Investigators at Technical University of Darmstadt, Germany, have made a significant breakthrough in the field of information science, developing a novel approach for accurate tree-crown segmentation in forest monitoring. The research is crucial for forest management and biodiversity conservation, as accurate monitoring of tree crowns is essential for understanding forest
Technical institutes
Researchers from North Carolina Agricultural and Technical State University have made significant strides in developing an accurate method for classifying coastal wetland areas using high-resolution multi-spectral imagery and LiDAR remote sensing data. By leveraging the Random Forest algorithm, the team was able to achieve impressive accuracy rates in different scenarios,
Technical institutes
Researchers from the Tianjin University of Technology have made significant strides in the field of remote sensing with the introduction of DCANet, a novel dual-branch cross-scale feature aggregation network. The study, published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, aims to improve the
Machine learning
Researchers from the City College of New York have proposed an innovative framework for enhancing the accuracy and resilience of vehicle classification systems using LiDAR sensors. The framework integrates inductive loop sensors and LiDAR technology to address the limitations of legacy signature-based models, which may become obsolete with the introduction
Sensors
Researchers at Taiyuan University of Technology have made a significant breakthrough in identifying landslide hazards in the Loess Plateau, a region prone to frequent landslides due to extreme climatic conditions. By employing an integrated remote-sensing identification approach, the team was able to detect potential landslide hazards with high accuracy, providing
Sensors
A groundbreaking patent application has been filed by inventors Clifton and Kadam, presenting a revolutionary remote sensing device with a duplex optical element for improved crop analysis in agriculture. The device aims to overcome the current limitations of manual estimation and inaccurate forecasting, enabling farmers to make informed decisions for
Machine learning
Researchers from the Department of Energy and Pacific Northwest National Laboratory have shed new light on the complex world of coastal terrestrial-aquatic interfaces (TAIs), revealing the intricacies of gas flux in these dynamic ecosystems. Studying the Chesapeake Bay area, the team employed cutting-edge techniques to investigate the spatial variability of
Sensors
WAYMO LLC, a leader in autonomous vehicle technology, has been assigned a patent for its innovative Lidar-based trailer tracking technology. This cutting-edge technology is designed to enhance the safety and efficiency of autonomous vehicles by accurately tracking the pose of trailers, including their orientation relative to the tractor unit. By
Machine learning
Scientists at Beijing Key Laboratory have developed a method to accurately estimate nitrogen use efficiency in maize using a combination of multi-spectral and LiDAR data acquired by a multi-sensor UAV platform. The technique, which utilizes machine learning algorithms to analyze the data, has shown significant improvements in accuracy compared to
