Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Aaron E. Maxwell"'
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 122, Iss , Pp 103435- (2023)
The Landsat multispectral time series is a valuable source of moderate spatial resolution data to support forest mapping and monitoring tasks. Using United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) plo
Externí odkaz:
https://doaj.org/article/7da8bcae638f4a0bb191cd69981f8abb
Publikováno v:
Remote Sensing, Vol 16, Iss 3, p 533 (2024)
Evaluating classification accuracy is a key component of the training and validation stages of thematic map production, and the choice of metric has profound implications for both the success of the training process and the reliability of the final a
Externí odkaz:
https://doaj.org/article/4cf1271302604d54a04ebc7bb9e0b29b
Autor:
Aaron E. Maxwell, William E. Odom, Charles M. Shobe, Daniel H. Doctor, Michelle S. Bester, Tobi Ore
Publikováno v:
Earth and Space Science, Vol 10, Iss 5, Pp n/a-n/a (2023)
Abstract Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective
Externí odkaz:
https://doaj.org/article/4d0feb0f39a141d5a13fd29a5d89436d
Autor:
Michelle S. Bester, Aaron E. Maxwell, Isaac Nealey, Michael R. Gallagher, Nicholas S. Skowronski, Brenden E. McNeil
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4407 (2023)
The challenges inherent in field validation data, and real-world light detection and ranging (lidar) collections make it difficult to assess the best algorithms for using lidar to characterize forest stand volume. Here, we demonstrate the use of synt
Externí odkaz:
https://doaj.org/article/e02d5ea5b47f441a8be6342bdc19f615
Autor:
Aaron E. Maxwell, Michael R. Gallagher, Natale Minicuci, Michelle S. Bester, E. Louise Loudermilk, Scott M. Pokswinski, Nicholas S. Skowronski
Publikováno v:
Fire, Vol 6, Iss 3, p 98 (2023)
Terrestrial laser scanning (TLS) data can offer a means to estimate subcanopy fuel characteristics to support site characterization, quantification of treatment or fire effects, and inform fire modeling. Using field and TLS data within the New Jersey
Externí odkaz:
https://doaj.org/article/d0eef6d58a86486aaad2be8bab6da91e
Publikováno v:
Remote Sensing, Vol 14, Iss 22, p 5760 (2022)
Many issues can reduce the reproducibility and replicability of deep learning (DL) research and application in remote sensing, including the complexity and customizability of architectures, variable model training and assessment processes and practic
Externí odkaz:
https://doaj.org/article/cc1a9532fd304f2295a00d7dee890304
Publikováno v:
Remote Sensing, Vol 13, Iss 24, p 4991 (2021)
Machine learning (ML) methods, such as artificial neural networks (ANN), k-nearest neighbors (kNN), random forests (RF), support vector machines (SVM), and boosted decision trees (DTs), may offer stronger predictive performance than more traditional,
Externí odkaz:
https://doaj.org/article/063c0826ed424b96ada1379f3ec22b03
Autor:
Michael R. Gallagher, Aaron E. Maxwell, Luis Andrés Guillén, Alexis Everland, E. Louise Loudermilk, Nicholas S. Skowronski
Publikováno v:
Remote Sensing, Vol 13, Iss 20, p 4168 (2021)
Monitoring wildland fire burn severity is important for assessing ecological outcomes of fire and their spatial patterning as well as guiding efforts to mitigate or restore areas where ecological outcomes are negative. Burn severity mapping products
Externí odkaz:
https://doaj.org/article/bc9212245fb141f39b5b46d60e82b016
Publikováno v:
Land, Vol 10, Iss 7, p 748 (2021)
This study analyzes land-cover transitions in the headwaters of the Big Coal River in the Central Appalachian Region of the US, from 1976 to 2016, where surface mining was found as the major driver of landscape change. The land-change analysis combin
Externí odkaz:
https://doaj.org/article/e2fb430c7f434527a25710995a88e8b6
Publikováno v:
Remote Sensing, Vol 13, Iss 13, p 2591 (2021)
Convolutional neural network (CNN)-based deep learning (DL) has a wide variety of applications in the geospatial and remote sensing (RS) sciences, and consequently has been a focus of many recent studies. However, a review of accuracy assessment meth
Externí odkaz:
https://doaj.org/article/749660c4eb03475eb5dad2f9ccde740f