Matches in SemOpenAlex for { <https://semopenalex.org/work/W4380366819> ?p ?o ?g. }
- W4380366819 endingPage "5505" @default.
- W4380366819 startingPage "5505" @default.
- W4380366819 abstract "Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission’s success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning (multi-layer perceptron (MLP) and convolutional neural network (CNN)) are used to perform a comparative study to estimate the soil properties, such as the soil moisture and terrain strength, used to generate prediction maps of these terrain characteristics. This study found that deep learning outperformed machine learning. Specifically, a multi-layer perceptron performed the best for predicting the percent moisture content (R2/RMSE = 0.97/1.55) and the soil strength (in PSI), as measured by a cone penetrometer for the averaged 0–6” (CP06) (R2/RMSE = 0.95/67) and 0–12” depth (CP12) (R2/RMSE = 0.92/94). A Polaris MRZR vehicle was used to test the application of these prediction maps for mobility purposes, and correlations were observed between the CP06 and the rear wheel slip and the CP12 and the vehicle speed. Thus, this study demonstrates the potential of a more rapid, cost-efficient, and safer approach to predict terrain properties for mobility mapping using remote sensing data with machine and deep learning algorithms." @default.
- W4380366819 created "2023-06-13" @default.
- W4380366819 creator A5003517068 @default.
- W4380366819 creator A5004819534 @default.
- W4380366819 creator A5006970579 @default.
- W4380366819 creator A5032716539 @default.
- W4380366819 creator A5045974793 @default.
- W4380366819 creator A5055823288 @default.
- W4380366819 creator A5066918469 @default.
- W4380366819 creator A5076004234 @default.
- W4380366819 creator A5080968711 @default.
- W4380366819 date "2023-06-11" @default.
- W4380366819 modified "2023-10-01" @default.
- W4380366819 title "Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing" @default.
- W4380366819 cites W1058055990 @default.
- W4380366819 cites W1563088657 @default.
- W4380366819 cites W1967138014 @default.
- W4380366819 cites W1968487307 @default.
- W4380366819 cites W1977177161 @default.
- W4380366819 cites W1983262663 @default.
- W4380366819 cites W1999232283 @default.
- W4380366819 cites W2055353410 @default.
- W4380366819 cites W2071021959 @default.
- W4380366819 cites W2077678091 @default.
- W4380366819 cites W2084777389 @default.
- W4380366819 cites W2104520867 @default.
- W4380366819 cites W2105782107 @default.
- W4380366819 cites W2136251662 @default.
- W4380366819 cites W2139212933 @default.
- W4380366819 cites W2158863190 @default.
- W4380366819 cites W2334113041 @default.
- W4380366819 cites W2337492962 @default.
- W4380366819 cites W2475766724 @default.
- W4380366819 cites W2509917403 @default.
- W4380366819 cites W2519746072 @default.
- W4380366819 cites W2538244214 @default.
- W4380366819 cites W2555779650 @default.
- W4380366819 cites W2581004703 @default.
- W4380366819 cites W2612291637 @default.
- W4380366819 cites W2620197950 @default.
- W4380366819 cites W2623490820 @default.
- W4380366819 cites W2740144340 @default.
- W4380366819 cites W2753823977 @default.
- W4380366819 cites W2770987943 @default.
- W4380366819 cites W2956919865 @default.
- W4380366819 cites W2968752509 @default.
- W4380366819 cites W2995150843 @default.
- W4380366819 cites W2995180237 @default.
- W4380366819 cites W2997203354 @default.
- W4380366819 cites W3001018125 @default.
- W4380366819 cites W3003931509 @default.
- W4380366819 cites W3008439211 @default.
- W4380366819 cites W3010716126 @default.
- W4380366819 cites W3096910885 @default.
- W4380366819 cites W3124539583 @default.
- W4380366819 cites W3172827911 @default.
- W4380366819 cites W3201470660 @default.
- W4380366819 cites W4234698323 @default.
- W4380366819 cites W4242582809 @default.
- W4380366819 cites W4252387515 @default.
- W4380366819 doi "https://doi.org/10.3390/s23125505" @default.
- W4380366819 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37420672" @default.
- W4380366819 hasPublicationYear "2023" @default.
- W4380366819 type Work @default.
- W4380366819 citedByCount "0" @default.
- W4380366819 crossrefType "journal-article" @default.
- W4380366819 hasAuthorship W4380366819A5003517068 @default.
- W4380366819 hasAuthorship W4380366819A5004819534 @default.
- W4380366819 hasAuthorship W4380366819A5006970579 @default.
- W4380366819 hasAuthorship W4380366819A5032716539 @default.
- W4380366819 hasAuthorship W4380366819A5045974793 @default.
- W4380366819 hasAuthorship W4380366819A5055823288 @default.
- W4380366819 hasAuthorship W4380366819A5066918469 @default.
- W4380366819 hasAuthorship W4380366819A5076004234 @default.
- W4380366819 hasAuthorship W4380366819A5080968711 @default.
- W4380366819 hasBestOaLocation W43803668191 @default.
- W4380366819 hasConcept C105795698 @default.
- W4380366819 hasConcept C108583219 @default.
- W4380366819 hasConcept C119857082 @default.
- W4380366819 hasConcept C12267149 @default.
- W4380366819 hasConcept C127313418 @default.
- W4380366819 hasConcept C139945424 @default.
- W4380366819 hasConcept C154945302 @default.
- W4380366819 hasConcept C159078339 @default.
- W4380366819 hasConcept C159390177 @default.
- W4380366819 hasConcept C159750122 @default.
- W4380366819 hasConcept C161840515 @default.
- W4380366819 hasConcept C179717631 @default.
- W4380366819 hasConcept C205649164 @default.
- W4380366819 hasConcept C2780830269 @default.
- W4380366819 hasConcept C33923547 @default.
- W4380366819 hasConcept C39432304 @default.
- W4380366819 hasConcept C41008148 @default.
- W4380366819 hasConcept C50644808 @default.
- W4380366819 hasConcept C58640448 @default.
- W4380366819 hasConcept C60908668 @default.
- W4380366819 hasConcept C62649853 @default.
- W4380366819 hasConcept C81363708 @default.