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This study presents an unsupervised, scalable framework for extracting transmission corridor structures (including conductors and supporting towers) from U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) lidar point clouds. The workflow begins with height\u2010above\u2010ground filtering and statistical outlier removal to isolate elevated infrastructure candidates. Local Principal Component Analysis (PCA) is then used to compute fine\u2010scale geometric descriptors. To incorporate spatial context and improve structural separability in feature space, we learn point embeddings using a Graph Attention Network (GAT) trained with a structure\u2010consistency triplet objective, yielding representations tailored for downstream density\u2010based grouping rather than semantic classification. To address the spatial non\u2010stationarity of point density and noise across large scenes, we formulate Density\u2010Based Spatial Clustering of Applications with Noise (DBSCAN) parameter selection as a reinforcement learning problem and employ Proximal Policy Optimization (PPO) to adapt  and minPts at the block level based on embedding statistics. The final extraction is produced via block\u2010wise DBSCAN and spatial merging to support efficient processing of multi\u2010million\u2010point clouds. Experiments on USGS 3DEP data over Houston, Texas, show that the proposed method produces spatially coherent candidate corridor structures from unstructured lidar. Quantitative assessment is conducted using OpenStreetMap (OSM)\u2010referenced spatial\u2010agreement metrics under a consistent vector\u2010reference protocol, alongside comparative baselines and ablation studies, to examine the relative contributions of graph embedding learning and RL\u2010based adaptive clustering.<\/jats:p>","DOI":"10.1111\/tgis.70340","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T07:38:01Z","timestamp":1783582681000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Structure\u2010Aware Unsupervised Transmission Corridor Structure Extraction From\n                    <scp>3DEP<\/scp>\n                    Lidar Using Graph Attention Embeddings and Reinforcement\u2010Learned\n                    <scp>DBSCAN<\/scp>"],"prefix":"10.1111","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0409-1179","authenticated-orcid":false,"given":"Nanzhou","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Geogrpahy Texas A&amp;M University  College Station Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Geogrpahy Texas A&amp;M University  College Station Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4863-0138","authenticated-orcid":false,"given":"Samantha T.","family":"Arundel","sequence":"additional","affiliation":[{"name":"Center of Excellence for Geospatial Information Science (CEGIS) U.S.\u00a0Geological Survey (USGS)  Rolla Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ethan","family":"Shavers","sequence":"additional","affiliation":[{"name":"Center of Excellence for Geospatial Information Science (CEGIS) U.S.\u00a0Geological Survey (USGS)  Rolla Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jungkuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Center of Excellence for Geospatial Information Science (CEGIS) U.S.\u00a0Geological Survey (USGS)  Rolla Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"issue":"6","key":"e_1_2_11_2_1","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1109\/LGRS.2013.2262317","article-title":"Geomorphological Change Extraction Using Object\u2010Based Feature Extraction From Multi\u2010Temporal LIDAR Data","volume":"10","author":"Anders N. S.","year":"2013","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"8","key":"e_1_2_11_3_1","doi-asserted-by":"crossref","DOI":"10.3390\/s19081812","article-title":"Lidar\u2010Based Real\u2010Time Detection and Modeling of Power Lines for Unmanned Aerial Vehicles","volume":"19","author":"Azevedo F.","year":"2019","journal-title":"Sensors"},{"issue":"7","key":"e_1_2_11_4_1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0307138","article-title":"Segmentation of LIDAR Point Cloud Data in Urban Areas Using Adaptive Neighborhood Selection Technique","volume":"19","author":"Chakraborty D.","year":"2024","journal-title":"PLoS One"},{"issue":"4","key":"e_1_2_11_5_1","doi-asserted-by":"crossref","DOI":"10.3390\/rs10040613","article-title":"Automatic Clearance Anomaly\u00a0detection for Transmission Line Corridors Utilizing UAV\u2010Borne LIDAR Data","volume":"10","author":"Chen C.","year":"2018","journal-title":"Remote Sensing"},{"key":"e_1_2_11_6_1","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.patcog.2015.07.004","article-title":"Automatic Corridor Extraction From High Resolution Remote Sensing Imagery Based on an Improved Radon Transform","volume":"49","author":"Chen Y.","year":"2016","journal-title":"Pattern Recognition"},{"issue":"1","key":"e_1_2_11_7_1","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/61.974212","article-title":"Power Disturbance Classifier Using a Rule\u2010Based Method and Wavelet Packet\u2010Based Hidden Markov Model","volume":"17","author":"Chung J.","year":"2002","journal-title":"IEEE Transactions on Power Delivery"},{"key":"e_1_2_11_8_1","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s12517-021-06947-1","article-title":"Automatic Extraction of Power Transmission Lines and Risky Object Locations Using UAV Lidar Data","volume":"14","author":"Dihkan M.","year":"2021","journal-title":"Arabian Journal of Geosciences"},{"issue":"34","key":"e_1_2_11_9_1","first-page":"226","article-title":"A Density\u2010Based Algorithm for Discovering Clusters in Large Spatial Databases With Noise","volume":"96","author":"Ester M.","year":"1996","journal-title":"kdd"},{"key":"e_1_2_11_10_1","doi-asserted-by":"crossref","unstructured":"Gargoum S. andK.El\u2010Basyouny.2017.\u201cAutomated Extraction of Road Features Using Lidar Data: A Review of Lidar Applications in Transportation. 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