Results 221 to 230 of about 14,618,314 (310)

Deep learning‐based super‐resolution reconstruction and improved YOLOv9 for efficient benthos detection: a case study at Lake Hamana, Japan

Remote Sensing in Ecology and Conservation, EarlyView.
This study presents a UAV‐based framework that integrates deep learning‐based super‐resolution reconstruction and an enhanced YOLO detector to improve centimetre‐scale benthic organism monitoring. Using hermit crabs in Lake Hamana, a coastal lagoon in Japan, as a case study, the method substantially enhanced small‐object detection performance ...
Fan Zhao, Bangzhang Ma, Dianhan Xi, Jiaqi Wang, Yijia Chen, Yongying Liu, Xinlei Shao, Mowen Zhang, Guocheng Zhang, Jundong Chen, Katsunori Mizuno   +10 more
wiley   +1 more source

Surveillance camera image dataset for visual classification in criminal investigation research (CCTV cameras). [PDF]

Data Brief
Rangelov D, Rangelov N, Miltchev R, Genchev E.   +3 more
europepmc   +1 more source

Time‐series digital camera photos combined with machine learning algorithms can realize accurate observation of flowering phenology

Remote Sensing in Ecology and Conservation, EarlyView.
Intelligent approaches are required to extract valuable phenological information from time‐series digital camera photos. In this research, we employed YOLO‐based object detection and semantic segmentation models to identify flowers and flower pixels, acquire flower count and flower cover data, and extract phenophases such as first, peak, and end ...
Chuangye Song, Yuan Jia, Lin Zhang, Dongxiu Wu   +3 more
wiley   +1 more source

Understanding Human Motion from Depth Sensors: Activity Recognition and Age Group Recognition Using Skeleton Data. [PDF]

Sensors (Basel)
Paul RE, Tanman AG, Hartmann Y, Behrendt J, Liu H, Schultz T.   +5 more
europepmc   +1 more source

Classification of tree species and standing dead trees in Boreal forests using UAV‐based RGB, multispectral, and LiDAR point clouds

Remote Sensing in Ecology and Conservation, EarlyView.
We evaluated single‐ and multi‐sensor UAV approaches for classifying tree species and standing dead trees in boreal forests, focusing on key biodiversity indicators such as European aspen. Using spectral and structural features extracted from RGB, multispectral (MSP), and LiDAR point clouds for 1,205 field‐measured trees, we compared classification ...
Anton Kuzmin, Lauri Korhonen, Topi Tanhuanpää, Mikko Kukkonen, Matti Maltamo, Timo Kumpula   +5 more
wiley   +1 more source

Real-Time Vision-Based Fall Detection Systems for the Elderly: A Systematic Review. [PDF]

Sensors (Basel)
Nabizade M, Yahiaoui R, Lajoie I, Nacer N, Auber F, Fayad M.   +5 more
europepmc   +1 more source

Retrospective image analysis for long‐term demography using Google Earth imagery

Remote Sensing in Ecology and Conservation, EarlyView.
We demonstrate that high‐resolution Google Earth imagery, combined with minimal field validation, enables retrospective tracking of individual invasive plants. The image shows one of the monitored individuals of Opuntia sp. in Greece. Our approach reveals long‐term demographic patterns, recruitment dynamics, and spatial expansion without continuous ...
Erola Fenollosa, Garrett Cohen, Roberto Salguero‐Gómez   +2 more
wiley   +1 more source

3D Pose Estimation Using Virtual Projection Based on 3D Reconstructed Model. [PDF]

Sensors (Basel)
Kim JW, Lee S, Park BS, Lee HB, Kang DH, Seo YH.   +5 more
europepmc   +1 more source

Ground‐based robotic remote sensing for standardized biodiversity monitoring in coastal habitats

Remote Sensing in Ecology and Conservation, EarlyView.
Illustrated workflow of the proposed citizen‐to‐robot monitoring pipeline: (i) expert‐validated citizen observations are translated into AI models, (ii) deployed on a ground‐based robotic platform for proximal sensing of coastal dune habitats, (iii) enabling standardized detection of ecological targets (e.g., Pancratium maritimum & Brithys crini), and (
Giovanni Di Lorenzo, Simonetta Bagella, Micaela del Valle Rasino, Maria Laura Carranza, Manolo Garabini, Franco Angelini   +5 more
wiley   +1 more source

A Survey of Environmental Perception for Unmanned Ground Agricultural Machinery in Field Environments. [PDF]

Sensors (Basel)
Zhang Q, Wu W, Liu M, Xu L, Zhao Z, Liang S.   +5 more
europepmc   +1 more source