{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113028"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113028","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Terrain characterization for site selection and preparation","abstract":"Terrain characterization is a key component in autonomous base camp site selection and preparation. Aerial terrain characterization allows for large areas of interest to be characterized in a safe and efficient manner. In this work three terrain characteristics, terrain elevation/slope, land cover/land use classes, and soil moisture content were determined using UAV-mounted sensors to inform base camp site selection and preparation decisions. To determine accurate and real-time elevation/slope values, a stale a priori digital elevation model (DEM) was merged with a high-resolution, updated LIDAR DEM using the mblend method. The mblend method achieved better results than the traditional cover method by ensuring fewer height discontinuities along the edge of the two DEMs. To perform land cover/land use mapping, three semantic segmentation models (PSPNet, U-Net, and Segnet) and three base models (VGG, ResNet, and MobileNet) were modified to include multispectral imagery and compared. Seven land cover classes were determined with an accuracy of 82.71% by model ResNet/SegNet. To determine soil moisture content (SMC), ten models were developed to predict soil moisture – two machine learning models, support vector machine (SVM) and extremely randomized trees (ET), were paired with 5 input variables. The results indicated that SMC could be predicted with greater accuracy by reducing the dimensionality of a hyperspectral dataset to resemble a standard multispectral dataset. The ET model produced better estimations of SMC when trained with the reduced dimensionality (RD) input set and concatenated multispectral (CM) set – obtaining an increase of 1.3% (RD) and 5.4% (CM) in R-squared values and a decrease of .13 and .22 in mean absolute error (MAE) when compared to the baseline set. Finally, a process overview and use case is presented to illustrate the terrain characterization process as a whole.","abstract_html":"Terrain characterization is a key component in autonomous base camp site selection and preparation. Aerial terrain characterization allows for large areas of interest to be characterized in a safe and efficient manner. In this work three terrain characteristics, terrain elevation/slope, land cover/land use classes, and soil moisture content were determined using UAV-mounted sensors to inform base camp site selection and preparation decisions. To determine accurate and real-time elevation/slope values, a stale a priori digital elevation model (DEM) was merged with a high-resolution, updated LIDAR DEM using the mblend method. The mblend method achieved better results than the traditional cover method by ensuring fewer height discontinuities along the edge of the two DEMs. To perform land cover/land use mapping, three semantic segmentation models (PSPNet, U-Net, and Segnet) and three base models (VGG, ResNet, and MobileNet) were modified to include multispectral imagery and compared. Seven land cover classes were determined with an accuracy of 82.71% by model ResNet/SegNet. To determine soil moisture content (SMC), ten models were developed to predict soil moisture – two machine learning models, support vector machine (SVM) and extremely randomized trees (ET), were paired with 5 input variables. The results indicated that SMC could be predicted with greater accuracy by reducing the dimensionality of a hyperspectral dataset to resemble a standard multispectral dataset. The ET model produced better estimations of SMC when trained with the reduced dimensionality (RD) input set and concatenated multispectral (CM) set – obtaining an increase of 1.3% (RD) and 5.4% (CM) in R-squared values and a decrease of .13 and .22 in mean absolute error (MAE) when compared to the baseline set. Finally, a process overview and use case is presented to illustrate the terrain characterization process as a whole.","abstract_has_math":false,"creators":["Lobato, Ana Michaela"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Systems & Entrepreneurial Engr","degree_department":null,"school":null,"contributors":["Norris, William R"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T21:45:40Z","date_published":"2022-01-12T21:45:40Z","updated_at":"2026-07-22T22:24:52Z","subjects":["terrain characterization","remote sensing","image processing","machine learning","deep learning"],"languages":["en"],"rights":["Copyright 2021 Ana Lobato"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113028","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Norris, William R"]},{"key":"dc:creator","label":"Author","values":["Lobato, Ana Michaela"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T21:45:40Z","2021-07-14","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Systems & Entrepreneurial Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["terrain characterization","remote sensing","image processing","machine learning","deep learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Ana Lobato"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113028"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Terrain characterization is a key component in autonomous base camp site selection and preparation. Aerial terrain characterization allows for large areas of interest to be characterized in a safe and efficient manner. In this work three terrain characteristics, terrain elevation/slope, land cover/land use classes, and soil moisture content were determined using UAV-mounted sensors to inform base camp site selection and preparation decisions. To determine accurate and real-time elevation/slope values, a stale a priori digital elevation model (DEM) was merged with a high-resolution, updated LIDAR DEM using the mblend method. The mblend method achieved better results than the traditional cover method by ensuring fewer height discontinuities along the edge of the two DEMs. To perform land cover/land use mapping, three semantic segmentation models (PSPNet, U-Net, and Segnet) and three base models (VGG, ResNet, and MobileNet) were modified to include multispectral imagery and compared. Seven land cover classes were determined with an accuracy of 82.71% by model ResNet/SegNet. To determine soil moisture content (SMC), ten models were developed to predict soil moisture – two machine learning models, support vector machine (SVM) and extremely randomized trees (ET), were paired with 5 input variables. The results indicated that SMC could be predicted with greater accuracy by reducing the dimensionality of a hyperspectral dataset to resemble a standard multispectral dataset. The ET model produced better estimations of SMC when trained with the reduced dimensionality (RD) input set and concatenated multispectral (CM) set – obtaining an increase of 1.3% (RD) and 5.4% (CM) in R-squared values and a decrease of .13 and .22 in mean absolute error (MAE) when compared to the baseline set. Finally, a process overview and use case is presented to illustrate the terrain characterization process as a whole.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-01-12 without embargo terms","The student, Ana Lobato, accepted the attached license on 2021-07-12 at 17:02.","The student, Ana Lobato, submitted this Thesis for approval on 2021-07-12 at 17:08.","This Thesis was approved for publication on 2021-07-14 at 13:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16885 on 2022-01-12 at 12:45:05","Made available in DSpace on 2022-01-12T21:45:40Z (GMT). No. of bitstreams: 2 LOBATO-THESIS-2021.pdf: 21372919 bytes, checksum: f66bb2f915a905c9c5d96cd2898de5f6 (MD5) LICENSE.txt: 4212 bytes, checksum: db5c3ad84868a2b38421f0496eae4f3d (MD5) Previous issue date: 2021-07-14"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Terrain characterization for site selection and preparation"]}]}],"canonical_facts":{"dc:contributor":["Norris, William R"],"dc:creator":["Lobato, Ana Michaela"],"dc:date":["2022-01-12T21:45:40Z","2021-07-14","2021-08"],"dc:description":["Terrain characterization is a key component in autonomous base camp site selection and preparation. Aerial terrain characterization allows for large areas of interest to be characterized in a safe and efficient manner. In this work three terrain characteristics, terrain elevation/slope, land cover/land use classes, and soil moisture content were determined using UAV-mounted sensors to inform base camp site selection and preparation decisions. To determine accurate and real-time elevation/slope values, a stale a priori digital elevation model (DEM) was merged with a high-resolution, updated LIDAR DEM using the mblend method. The mblend method achieved better results than the traditional cover method by ensuring fewer height discontinuities along the edge of the two DEMs. To perform land cover/land use mapping, three semantic segmentation models (PSPNet, U-Net, and Segnet) and three base models (VGG, ResNet, and MobileNet) were modified to include multispectral imagery and compared. Seven land cover classes were determined with an accuracy of 82.71% by model ResNet/SegNet. To determine soil moisture content (SMC), ten models were developed to predict soil moisture – two machine learning models, support vector machine (SVM) and extremely randomized trees (ET), were paired with 5 input variables. The results indicated that SMC could be predicted with greater accuracy by reducing the dimensionality of a hyperspectral dataset to resemble a standard multispectral dataset. The ET model produced better estimations of SMC when trained with the reduced dimensionality (RD) input set and concatenated multispectral (CM) set – obtaining an increase of 1.3% (RD) and 5.4% (CM) in R-squared values and a decrease of .13 and .22 in mean absolute error (MAE) when compared to the baseline set. Finally, a process overview and use case is presented to illustrate the terrain characterization process as a whole.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-01-12 without embargo terms","The student, Ana Lobato, accepted the attached license on 2021-07-12 at 17:02.","The student, Ana Lobato, submitted this Thesis for approval on 2021-07-12 at 17:08.","This Thesis was approved for publication on 2021-07-14 at 13:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16885 on 2022-01-12 at 12:45:05","Made available in DSpace on 2022-01-12T21:45:40Z (GMT). No. of bitstreams: 2 LOBATO-THESIS-2021.pdf: 21372919 bytes, checksum: f66bb2f915a905c9c5d96cd2898de5f6 (MD5) LICENSE.txt: 4212 bytes, checksum: db5c3ad84868a2b38421f0496eae4f3d (MD5) Previous issue date: 2021-07-14"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113028"],"dc:language":["en"],"dc:rights":["Copyright 2021 Ana Lobato"],"dc:subject":["terrain characterization","remote sensing","image processing","machine learning","deep learning"],"dc:title":["Terrain characterization for site selection and preparation"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Systems & Entrepreneurial Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:52Z"}