Results 301 to 310 of about 790,770 (385)

Efficient and Adaptive Autonomous Guidance and Control of Planetary Rover With Improved Traction Controller and Dynamic Cost Map

Journal of Field Robotics, EarlyView.
ABSTRACT Planetary exploration is rapidly gaining importance within the space research community. Autonomous locomotion of rovers requires consideration of several mobility aspects to ensure safety, including avoiding hazardous areas that can cause the robot to become immobilized in soft soil or damaged in sharp terrains.
Alessio De Luca, Luca Muratore, Nikos G. Tsagarakis, Martin Azkarate   +3 more
wiley   +1 more source

Unbiased self supervised learning of kidney histology reveals phenotypic and prognostic insights. [PDF]

Sci Rep
Pandit K, Coudray N, Quiros AC, Surapaneni A, Upadhyay D, Vanguri RS, Hirohama D, Mohandes S, Schlosser P, Thiessen-Philbrook H, Wen Y, Parikh CR, Rhee EP, Waikar SS, Schmidt I, Rosenberg AZ, Palmer MB, Susztak K, Grams ME, Tsirigos A, TRIDENT Study Investigators.   +20 more
europepmc   +1 more source

Perception and Mitigation of Artifacts in a Flat Panel Tiled Display System

, 2014
Stephen Dolph
openalex   +1 more source

Evaluating methods for high‐resolution, national‐scale seagrass mapping in Google Earth Engine

Remote Sensing in Ecology and Conservation, EarlyView.
Marine habitat mapping using satellite imagery can provide baseline and monitoring data across large spatial scales and in remote locations globally. This study evaluates how key methodological choices influence the accuracy of open‐source (for non‐commercial use), cloud‐based satellite mapping workflows for seagrass meadows in the Maldives.
Matthew Floyd, Holly K. East, Andrew J. Suggitt   +2 more
wiley   +1 more source

Deep learning finds convergent melanocytic morphology despite noisy archival slides. [PDF]

Cell Rep Methods
Tada M, Gaskins G, Ghandian S, Mew N, Keiser MJ, Keiser ES.   +5 more
europepmc   +1 more source

Wall‐to‐wall Amazon forest height mapping with Planet NICFI, Aerial LiDAR, and a U‐Net regression model

Remote Sensing in Ecology and Conservation, EarlyView.
Tree canopy height is a key indicator of forest biomass and structure, yet accurate mapping across the Amazon remains challenging. Here, we generated a canopy height map of the Amazon forest at ~4.8 m resolution using Planet NICFI imagery and a deep learning U‐Net model trained with airborne LiDAR data.
Fabien H. Wagner, Ricardo Dalagnol, Griffin Carter, Mayumi C. M. Hirye, Shivraj Gill, Le Bienfaiteur Sagang Takougoum, Samuel Favrichon, Michael Keller, Jean P. H. B. Ometto, Lorena Alves, Cynthia Creze, Stephanie P. George‐Chacon, Shuang Li, Zhihua Liu, Adugna Mullissa, Yan Yang, Erone G. Santos, Sarah R. Worden, Martin Brandt, Philippe Ciais, Stephen C. Hagen, Sassan Saatchi   +21 more
wiley   +1 more source

Improving forest age estimation to understand subtropical forest regrowth dynamics using deep learning image segmentation of time‐series historical aerial photographs

Remote Sensing in Ecology and Conservation, EarlyView.
Accurately estimating forest age is key to understanding how forests recover and evaluating restoration success. We developed a two‐step deep learning approach using historical greyscale aerial photographs to map forest age at fine spatial scales. By combining a pre‐trained model with localized fine‐tuning, our U‐Net + ResNet50 architecture achieved ...
Ying Ki Law, Yi‐Fei Gu, Shuwen Liu, Guangqin Song, Aland H. Y. Chan, Cham Man Tse, Zhonghua Liu, Martha J. Ledger, Billy C. H. Hau, Sawaid Abbas, Jin Wu   +10 more
wiley   +1 more source

Depth-variant deconvolution applied to widefield microscopy for rapid large-volume tissue imaging. [PDF]

Commun Biol
Lee DD, Telfer KA, Koenis MAJ, Lee YK, Namink KW, Saunders BT, Lee H, Kelley H, Ruiz HS, Gaut JP, Randolph GJ, Zinselmeyer BH.   +11 more
europepmc   +1 more source

Deep learning‐based ecological analysis of camera trap images is impacted by training data quality and quantity

Remote Sensing in Ecology and Conservation, EarlyView.
Machine learning image classifiers are increasingly being used to automate camera trap image labelling, but we don't know how much ML model accuracy matters for downstream ecological analyses. Using two large data sets from an African savannah and an Asian dry forest ecosystem, we compared human labelled data with predictions from deep‐learning models ...
Peggy A. Bevan, Omiros Pantazis, Holly A.I. Pringle, Guilherme Braga Ferreira, Daniel J. Ingram, Emily K. Madsen, Liam Thomas, Dol Raj Thanet, Thakur Silwal, Santosh Rayamajhi, Gabriel J. Brostow, Oisin Mac Aodha, Kate E. Jones   +12 more
wiley   +1 more source