{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:54:48Z","timestamp":1775199288981,"version":"3.50.1"},"reference-count":175,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T00:00:00Z","timestamp":1560816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005234","name":"Central China Normal University","doi-asserted-by":"publisher","award":["CCNU18QN020"],"award-info":[{"award-number":["CCNU18QN020"]}],"id":[{"id":"10.13039\/501100005234","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in almost every application (e.g., agriculture, forestry, and mining) that needs observed information from the top or oblique views. While they intend to be a general remote sensing (RS) tool, the relevant RS data processing and analysis methods are still largely ad-hoc to applications. Although the obvious advantages of UAV data are their high spatial resolution and flexibility in acquisition and sensor integration, there is in general a lack of systematic analysis on how these characteristics alter solutions for typical RS tasks such as land-cover classification, change detection, and thematic mapping. For instance, the ultra-high-resolution data (less than 10 cm of Ground Sampling Distance (GSD)) bring more unwanted classes of objects (e.g., pedestrian and cars) in land-cover classification; the often available 3D data generated from photogrammetric images call for more advanced techniques for geometric and spectral analysis. In this paper, we perform a critical review on RS tasks that involve UAV data and their derived products as their main sources including raw perspective images, digital surface models, and orthophotos. In particular, we focus on solutions that address the \u201cnew\u201d aspects of the UAV data including (1) ultra-high resolution; (2) availability of coherent geometric and spectral data; and (3) capability of simultaneously using multi-sensor data for fusion. Based on these solutions, we provide a brief summary of existing examples of UAV-based RS in agricultural, environmental, urban, and hazards assessment applications, etc., and by discussing their practical potentials, we share our views in their future research directions and draw conclusive remarks.<\/jats:p>","DOI":"10.3390\/rs11121443","type":"journal-article","created":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T02:42:46Z","timestamp":1560912166000},"page":"1443","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":580,"title":["Unmanned Aerial Vehicle for Remote Sensing Applications\u2014A Review"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5055-4106","authenticated-orcid":false,"given":"Huang","family":"Yao","sequence":"first","affiliation":[{"name":"School of Educational Information Technology, Central China Normal University, Wuhan 430079, China"},{"name":"Department of Civil, Environmental and Geodetic Engineering, The Ohio State University (OSU), Columbus, OH 43210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5896-1379","authenticated-orcid":false,"given":"Rongjun","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Geodetic Engineering, The Ohio State University (OSU), Columbus, OH 43210, USA"},{"name":"Department of Electrical and Computer Engineering, The Ohio State University (OSU), Columbus, OH 43210, USA"}]},{"given":"Xiaoyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Geodetic Engineering, The Ohio State University (OSU), Columbus, OH 43210, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"281","DOI":"10.14358\/PERS.81.4.281","article-title":"Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs)","volume":"81","author":"Pajares","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12518-013-0120-x","article-title":"UAV for 3D mapping applications: A review","volume":"6","author":"Nex","year":"2014","journal-title":"Appl. Geomat."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2015.12.029","article-title":"UAVs as remote sensing platform in glaciology: Present applications and future prospects","volume":"175","author":"Bhardwaj","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1080\/01431161.2016.1252477","article-title":"Forestry applications of UAVs in Europe: A review","volume":"38","author":"Torresan","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Crommelinck, S., Bennett, R., Gerke, M., Nex, F., Yang, M.Y., and Vosselman, G. (2016). Review of automatic feature extraction from high-resolution optical sensor data for UAV-based cadastral mapping. Remote Sens., 8.","DOI":"10.3390\/rs8080689"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2014.11.001","article-title":"Urban land cover classification using airborne LiDAR data: A review","volume":"158","author":"Yan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"16422","DOI":"10.3390\/rs71215840","article-title":"Classification of ultra-high resolution orthophotos combined with DSM using a dual morphological top hat profile","volume":"7","author":"Zhang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6356","DOI":"10.1109\/TGRS.2013.2296351","article-title":"Detecting cars in UAV images with a catalog-based approach","volume":"52","author":"Moranduzzo","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.3390\/rs4041090","article-title":"A real-time method to detect and track moving objects (DATMO) from unmanned aerial vehicles (UAVs) using a single camera","volume":"4","author":"Thomas","year":"2012","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Korchenko, A.G., and Illyash, O.S. (2013, January 15\u201317). The generalized classification of Unmanned Air Vehicles. Proceedings of the 2013 IEEE 2nd International Conference Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD), Kiev, Ukraine.","DOI":"10.1109\/APUAVD.2013.6705275"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Valavanis, K.P., and Vachtsevanos, G.J. (2015). Classification of UAVs. Handbook of Unmanned Aerial Vehicles, Springer.","DOI":"10.1007\/978-90-481-9707-1"},{"key":"ref_13","unstructured":"Department of Defense of USA, and Office of the Secretary of Defense (2010). Army Roadmap for Unmanned Aircraft Systems, 2010\u20132035, U.S. Army UAS Center of Excellence and Fort Rucker."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2950","DOI":"10.1109\/TGRS.2006.876704","article-title":"A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery","volume":"44","author":"Zhang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0034-4257(87)90015-0","article-title":"The factor of scale in remote sensing","volume":"21","author":"Woodcock","year":"1987","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u2018Structure-from-Motion\u2019photogrammetry: A low-cost, effective tool for geoscience applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/TGRS.2013.2265295","article-title":"Direct georeferencing of ultrahigh-resolution UAV imagery","volume":"52","author":"Turner","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.isprsjprs.2014.12.025","article-title":"Automatic registration of UAV-borne sequent images and LiDAR data","volume":"101","author":"Yang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0924-2716(03)00020-0","article-title":"Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data","volume":"58","author":"Segl","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","unstructured":"Eisenbei\u00df, H. (2009). UAV Photogrammetry, ETH Zurich."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Du, S., Zhang, Y., Qin, R., Yang, Z., Zou, Z., Tang, Y., and Fan, C. (2016). Building change detection using old aerial images and new LiDAR data. Remote Sens., 8.","DOI":"10.3390\/rs8121030"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11273-009-9169-z","article-title":"Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review","volume":"18","author":"Adam","year":"2010","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s11119-014-9360-y","article-title":"Detection of downy mildew of opium poppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle","volume":"15","author":"Landa","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kislik, C., Dronova, I., and Kelly, M. (2018). UAVs in Support of Algal Bloom Research: A Review of Current Applications and Future Opportunities. Drones, 2.","DOI":"10.3390\/drones2040035"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11013","DOI":"10.3390\/rs61111013","article-title":"A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles","volume":"6","author":"Suomalainen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/JSEN.2013.2279720","article-title":"A novel UAV-based ultra-light weight spectrometer for field spectroscopy","volume":"14","author":"Burkart","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9007","DOI":"10.1080\/01431161.2010.532172","article-title":"Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats","volume":"32","author":"Psomas","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","first-page":"112","article-title":"Multi-and hyperspectral geologic remote sensing: A review","volume":"14","author":"Hecker","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of hyperspectral data from urban areas based on extended morphological profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rufino, G., and Moccia, A. (2005). Integrated VIS-NIR hyperspectral\/thermal-IR electro-optical payload system for a mini-UAV. Infotech@ Aerospace, AIAA.","DOI":"10.2514\/6.2005-7009"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1002\/rob.21508","article-title":"HyperUAS\u2014Imaging spectroscopy from a multirotor unmanned aircraft system","volume":"31","author":"Lucieer","year":"2014","journal-title":"J. Field Robot."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.isprsjprs.2015.08.002","article-title":"Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance","volume":"108","author":"Aasen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Robles-Kelly, A., and Huynh, C.P. (2012). Imaging Spectroscopy for Scene Analysis, Springer Science & Business Media.","DOI":"10.1007\/978-1-4471-4652-0"},{"key":"ref_36","first-page":"239","article-title":"Thermal remote sensing: Concepts, issues and applications","volume":"33","author":"Prakash","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sheng, H., Chao, H., Coopmans, C., Han, J., McKee, M., and Chen, Y. (2010, January 15\u201317). Low-cost UAV-based thermal infrared remote sensing: Platform, calibration and applications. Proceedings of the 2010 IEEE\/ASME International Conference on Mechatronics and Embedded Systems and Applications (MESA), Qingdao, China.","DOI":"10.1109\/MESA.2010.5552031"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rudol, P., and Doherty, P. (2008, January 1\u20138). Human body detection and geolocalization for UAV search and rescue missions using color and thermal imagery. Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2008.4526559"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"391","DOI":"10.14358\/PERS.69.4.391","article-title":"Demonstrating UAV-acquired real-time thermal data over fires","volume":"69","author":"Ambrosia","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"13560","DOI":"10.3390\/s131013560","article-title":"Thermal tracking in mobile robots for leak inspection activities","volume":"13","author":"Ibarguren","year":"2013","journal-title":"Sensors"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2008.2010457","article-title":"Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle","volume":"47","author":"Berni","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1080\/10106049.2010.534557","article-title":"UAS remote sensing missions for rangeland applications","volume":"26","author":"Laliberte","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jensen, A.M., Neilson, B.T., McKee, M., and Chen, Y. (2012, January 22\u201327). Thermal remote sensing with an autonomous unmanned aerial remote sensing platform for surface stream temperatures. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352476"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2011.10.007","article-title":"Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera","volume":"117","author":"Berni","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"697","DOI":"10.5194\/hess-20-697-2016","article-title":"Estimating evaporation with thermal UAV data and two-source energy balance models","volume":"20","author":"Hoffmann","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"345","DOI":"10.5194\/isprsarchives-XXXIX-B1-345-2012","article-title":"Introducing a low-cost mini-UAV for thermal-and multispectral-imaging","volume":"39","author":"Bendig","year":"2012","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/JSTARS.2009.2037523","article-title":"Analysis on the use of multiple returns LiDAR data for the estimation of tree stems volume","volume":"2","author":"Dalponte","year":"2009","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.3390\/rs4061519","article-title":"Development of a UAV-LiDAR system with application to forest inventory","volume":"4","author":"Wallace","year":"2012","journal-title":"Remote Sens."},{"key":"ref_49","first-page":"119","article-title":"High-Precision Positioning and Real-Time Data Processing of UAV Systems","volume":"38","author":"Rieke","year":"2011","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5547","DOI":"10.1109\/JSTARS.2016.2569162","article-title":"Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest\u2013Part A: 2-D Contest","volume":"9","author":"Gatta","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5560","DOI":"10.1109\/JSTARS.2016.2581843","article-title":"Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest\u2014Part B: 3-D Contest","volume":"9","author":"Vo","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2014.03.009","article-title":"Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites","volume":"103","author":"Belward","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4238","DOI":"10.1109\/TGRS.2015.2393857","article-title":"Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images","volume":"53","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/01431160500297956","article-title":"Urban land cover multi-level region-based classification of VHR data by selecting relevant features","volume":"27","author":"Carleer","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Fry, J., Coan, M., Homer, C., Meyer, D., and Wickham, J. (2009). Completion of the National Land Cover Database (NLCD) 1992\u20132001 Land cover Change Retrofit Product, US Geological Survey.","DOI":"10.3133\/ofr20081379"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.gloplacha.2015.02.009","article-title":"Land use and land cover change impacts on the regional climate of non-Amazonian South America: A review","volume":"128","author":"Salazar","year":"2015","journal-title":"Glob. Planet. Chang."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.07.007","article-title":"Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak","volume":"131","author":"Dash","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2246","DOI":"10.1080\/01431161.2016.1252475","article-title":"Evaluation of UAV imagery for mapping Silybum marianum weed patches","volume":"38","author":"Tamouridou","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"7911","DOI":"10.3390\/rs6097911","article-title":"An object-based hierarchical method for change detection using unmanned aerial vehicle images","volume":"6","author":"Qin","year":"2014","journal-title":"Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.compag.2015.03.019","article-title":"An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops","volume":"114","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2012.08.033","article-title":"Fine-scale remotely-sensed cover mapping of coastal dune and salt marsh ecosystems at Cape Cod National Seashore using Random Forests","volume":"127","author":"Timm","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1080\/2150704X.2014.882526","article-title":"High-resolution landcover classification using Random Forest","volume":"5","author":"Hayes","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_67","unstructured":"Baatz, M., Benz, U., Dehghani, S., Heynen, M., H\u00f6ltje, A., Hofmann, P., Lingenfelder, I., Mimler, M., Sohlbach, M., and Weber, M. (2004). ECognition Professional User Manual 4, Definiens Imaging."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1109\/TGRS.2008.2010708","article-title":"Hierarchical texture-based segmentation of multiresolution remote-sensing images","volume":"47","author":"Gaetano","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.isprsjprs.2007.08.005","article-title":"Using colour, texture, and hierarchial segmentation for high-resolution remote sensing","volume":"63","author":"Stamon","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1109\/TGRS.2003.814627","article-title":"A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas","volume":"41","author":"Shackelford","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1109\/LGRS.2013.2262132","article-title":"Object-based spatial feature for classification of very high resolution remote sensing images","volume":"10","author":"Zhang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2015.04.010","article-title":"Scale parameter selection by spatial statistics for GeOBIA: Using mean-shift based multi-scale segmentation as an example","volume":"106","author":"Ming","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Tiede","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Kim, M., Madden, M., and Warner, T. (2008). Estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery. Object-Based Image Analysis, Springer.","DOI":"10.1007\/978-3-540-77058-9_16"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic object-based image analysis\u2013towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.isprsjprs.2008.03.003","article-title":"Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses","volume":"63","author":"Aguilar","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"819","DOI":"10.14358\/PERS.75.7.819","article-title":"Forest type mapping using object-specific texture measures from multispectral Ikonos imagery","volume":"75","author":"Kim","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.3390\/rs70101074","article-title":"UAV remote sensing for urban vegetation mapping using random forest and texture analysis","volume":"7","author":"Feng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Moranduzzo, T., Mekhalfi, M.L., and Melgani, F. (2015, January 26\u201331). LBP-based multiclass classification method for UAV imagery. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326283"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1109\/36.763282","article-title":"Updating land-cover maps by using texture information from very high-resolution space-borne imagery","volume":"37","author":"Smits","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.patcog.2011.07.017","article-title":"Extraction of complex patterns from multiresolution remote sensing images: A hierarchical top-down methodology","volume":"45","author":"Kurtz","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1109\/TGRS.2008.2009355","article-title":"Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery","volume":"47","author":"Laliberte","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2017.01.017","article-title":"Informal settlement classification using point-cloud and image-based features from UAV data","volume":"125","author":"Gevaert","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.foreco.2015.10.018","article-title":"Use of WorldView-2 stereo imagery and National Forest Inventory data for wall-to-wall mapping of growing stock","volume":"359","author":"Immitzer","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_85","unstructured":"Salehi, B., Zhang, Y., and Zhong, M. (2011, January 1\u20135). Object-based land cover classification of urban areas using VHR imagery and photogrammetrically-derived DSM. Proceedings of the ASPRS 2011 Annual Conference, Milwaukee, WI, USA."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2007.01.001","article-title":"Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction","volume":"62","author":"Sohn","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2313","DOI":"10.1080\/01431161.2017.1295486","article-title":"A UAV\u2013lidar system to map Amazonian rainforest and its ancient landscape transformations","volume":"38","author":"Khan","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.3390\/s90301980","article-title":"LULC classification and topographic correction of Landsat-7 ETM+ imagery in the Yangjia River Watershed: The influence of DEM resolution","volume":"9","author":"Gao","year":"2009","journal-title":"Sensors"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"239","DOI":"10.14358\/PERS.74.2.239","article-title":"Multisource Classification Using Support Vector Machines","volume":"74","author":"Watanachaturaporn","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2010.08.007","article-title":"Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests","volume":"66","author":"Guo","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_91","unstructured":"Pix4D, SA (2018, December 30). Pix4Dmapper: Professional Drone Mapping and Photogrammetry Software | Pix4D. Available online: https:\/\/www.pix4d.com\/."},{"key":"ref_92","unstructured":"Agisoft LLC (2018, December 30). Agisoft Metashape. Available online: https:\/\/www.agisoft.com\/."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1002\/esp.3366","article-title":"Topographic structure from motion: A new development in photogrammetric measurement","volume":"38","author":"Fonstad","year":"2013","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/01431160903439882","article-title":"Information fusion of aerial images and LIDAR data in urban areas: Vector-stacking, re-classification and post-processing approaches","volume":"32","author":"Huang","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.1109\/JSTARS.2014.2357832","article-title":"A mean shift vector-based shape feature for classification of high spatial resolution remotely sensed imagery","volume":"8","author":"Qin","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1109\/83.217222","article-title":"Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms","volume":"2","author":"Vincent","year":"1993","journal-title":"IEEE Trans. Image Process."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.isprsjprs.2017.08.003","article-title":"Toward combining thematic information with hierarchical multiscale segmentations using tree Markov random field model","volume":"131","author":"Zhang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5194\/isprs-annals-IV-2-W3-9-2017","article-title":"SLIC superpixels for object delineation from UAV data","volume":"4","author":"Crommelinck","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Corcoran, P., and Winstanley, A. (2008). Using texture to tackle the problem of scale in land-cover classification. Object-Based Image Analysis, Springer.","DOI":"10.1007\/978-3-540-77058-9_6"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1080\/01431160512331314083","article-title":"Support vector machines for classification in remote sensing","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1109\/36.406684","article-title":"A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification","volume":"33","author":"Paola","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TGRS.2012.2202912","article-title":"An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery","volume":"51","author":"Huang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1109\/JSTARS.2013.2295513","article-title":"Ensemble Learning in Hyperspectral Image Classification: Toward Selecting a Favorable Bias-Variance Tradeoff","volume":"7","author":"Merentitis","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"6361","DOI":"10.1109\/TGRS.2018.2837357","article-title":"Recurrent Multiresolution Convolutional Networks for VHR Image Classification","volume":"56","author":"Bergado","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Mboga, N., Georganos, S., Grippa, T., Lennert, M., Vanhuysse, S., and Wolff, E. (2019). Fully Convolutional Networks and Geographic Object-Based Image Analysis for the Classification of VHR Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050597"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_108","unstructured":"Yu, F., and Koltun, V. (2015). Multi-Scale Context Aggregation by Dilated Convolutions. arXiv."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic labeling in very high resolution images via a self-cascaded convolutional neural network","volume":"145","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","article-title":"Classification with an edge: Improving semantic image segmentation with boundary detection","volume":"135","author":"Marmanis","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2017.11.011","article-title":"Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks","volume":"140","author":"Audebert","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Liu, Y., Piramanayagam, S., Monteiro, S.T., and Saber, E. (2017, January 21\u201326). Dense Semantic Labeling of Very-High-Resolution Aerial Imagery and LiDAR with Fully-Convolutional Neural Networks and Higher-Order CRFs. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.200"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Penatti, O.A.B., Nogueira, K., and Santos, J.A.d. (2015, January 7\u201312). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Wu, Z., Han, X., Lin, Y.-L., Uzunbas, M.G., Goldstein, T., Lim, S.N., and Davis, L.S. (2018, January 8\u201314). DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation. Proceedings of the Computer Vision\u2014ECCV 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01228-1_32"},{"key":"ref_118","unstructured":"LTA (2019, May 29). Maintaining Our Roads and Facilities, Available online: http:\/\/www.lta.gov.sg\/content\/ltaweb\/en\/roads-and-motoring\/road-safety-and-regulations\/maintaining-our-roads-and-facilities.html."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.isprsjprs.2014.01.006","article-title":"3D change detection at street level using mobile laser scanning point clouds and terrestrial images","volume":"90","author":"Qin","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Saur, G., and Kr\u00fcger, W. (2016, January 12\u201319). Change Detection in Uav Video Mosaics Combining a Feature Based Approach and Extended Image Differencing. Proceedings of the 2016 International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Prague, Czech Republic.","DOI":"10.5194\/isprs-archives-XLI-B7-557-2016"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Ma, Y., Wu, X., Yu, G., Xu, Y., and Wang, Y. (2016). Pedestrian detection and tracking from low-resolution unmanned aerial vehicle thermal imagery. Sensors, 16.","DOI":"10.3390\/s16040446"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Gaszczak, A., Breckon, T.P., and Han, J. (2011, January 23). Real-time people and vehicle detection from UAV imagery. Proceedings of the IS&T\/SPIE Electronic Imaging. International Society for Optics and Photonics, San Fransico, CA, USA.","DOI":"10.1117\/12.876663"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Butenuth, M., Burkert, F., Schmidt, F., Hinz, S., Hartmann, D., Kneidl, A., Borrmann, A., and Sirmacek, B. (2011, January 6\u201313). Integrating pedestrian simulation, tracking and event detection for crowd analysis. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130237"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"De Smedt, F., Hulens, D., and Goedem\u00e9, T. (2015, January 7\u201312). On-board real-time tracking of pedestrians on a UAV. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301359"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Triggs, B., McLauchlan, P.F., Hartley, R.I., and Fitzgibbon, A.W. (2000). Bundle adjustment\u2014A modern synthesis. International Workshop on Vision Algorithms, Springer.","DOI":"10.1007\/3-540-44480-7_21"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2003.09.007","article-title":"Object-based classification of remote sensing data for change detection","volume":"58","author":"Walter","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2016.09.013","article-title":"3D change detection\u2013approaches and applications","volume":"122","author":"Qin","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Lefebvre, A., Corpetti, T., and Hubert-Moy, L. (2008, January 7\u201311). Object-oriented approach and texture analysis for change detection in very high resolution images. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779809"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/j.eswa.2007.09.067","article-title":"Object-oriented change detection for the city of Harare, Zimbabwe","volume":"36","author":"Gamanya","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.1016\/j.rse.2010.02.018","article-title":"Updating the 2001 National Land Cover Database impervious surface products to 2006 using Landsat imagery change detection methods","volume":"114","author":"Xian","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"3181","DOI":"10.1016\/j.rse.2008.03.013","article-title":"An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution","volume":"112","author":"Bontemps","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.rse.2017.07.009","article-title":"A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion","volume":"199","author":"Wu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.01.006","article-title":"A critical synthesis of remotely sensed optical image change detection techniques","volume":"160","author":"Tewkesbury","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_134","unstructured":"Frost, S. (2019, June 17). Study Analysing the Current Activities in the Field of UAV. ENTR\/2007\/065. Available online: https:\/\/ec.europa.eu\/home-affairs\/sites\/homeaffairs\/files\/e-library\/documents\/policies\/security\/pdf\/uav_study_element_2_en.pdf."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.geoderma.2018.09.046","article-title":"UAV based soil salinity assessment of cropland","volume":"338","author":"Ivushkin","year":"2019","journal-title":"Geoderma"},{"key":"ref_136","first-page":"3","article-title":"Agricultural Drones: Relatively cheap drones with advanced sensors and imaging capabilities are giving farmers new ways to increase yields and reduce crop damage","volume":"17","author":"Anderson","year":"2014","journal-title":"MIT Technol. Rev"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.biosystemseng.2004.12.011","article-title":"Remote-sensing technology for vegetation monitoring using an unmanned helicopter","volume":"90","author":"Sugiura","year":"2005","journal-title":"Biosyst. Eng."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Wallace, L., Lucieer, A., Malenovsk\u00fd, Z., Turner, D., and Vop\u011bnka, P. (2016). Assessment of forest structure using two UAV techniques: A comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests, 7.","DOI":"10.3390\/f7030062"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"4968","DOI":"10.1109\/JSTARS.2018.2879368","article-title":"Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network","volume":"11","author":"Zhu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.enggeo.2018.11.002","article-title":"Total system error analysis of UAV-CRP technology for monitoring transportation infrastructure assets","volume":"247","author":"Congress","year":"2018","journal-title":"Eng. Geol."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Malihi, S., Zoej, M.J.V., and Hahn, M. (2018). Large-Scale Accurate Reconstruction of Buildings Employing Point Clouds Generated from UAV Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10071148"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"4215","DOI":"10.5194\/hess-19-4215-2015","article-title":"High-quality observation of surface imperviousness for urban runoff modelling using UAV imagery","volume":"19","author":"Tokarczyk","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_143","first-page":"1373","article-title":"Low cost UAV for post-disaster assessment","volume":"37","author":"Bendea","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.5194\/nhess-15-1087-2015","article-title":"UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning","volume":"15","author":"Kerle","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"15717","DOI":"10.3390\/s150715717","article-title":"UAV deployment exercise for mapping purposes: Evaluation of emergency response applications","volume":"15","author":"Boccardo","year":"2015","journal-title":"Sensors"},{"key":"ref_146","unstructured":"Schultjan, M. (2012). Towards the Deployment of UAVs for Fire Surveillance, Hamburg University of Technology."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1177\/0309133313515293","article-title":"Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography","volume":"38","author":"Lucieer","year":"2014","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.3390\/rs70201736","article-title":"Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV)","volume":"7","author":"Turner","year":"2015","journal-title":"Remote Sens."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.1080\/01431161.2016.1275061","article-title":"Kinematic analysis of sea cliff stability using UAV photogrammetry","volume":"38","author":"Barlow","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Sturdivant, E.J., Lentz, E.E., Thieler, E.R., Farris, A.S., Weber, K.M., Remsen, D.P., Miner, S., and Henderson, R.E. (2017). UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9101020"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1023\/A:1009995404447","article-title":"Estimating average and proportional variograms of soil properties and their potential use in precision agriculture","volume":"1","author":"McBratney","year":"1999","journal-title":"Precis. Agric."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.1080\/01431160903023025","article-title":"Potential and constraints of Unmanned Aerial Vehicle technology for the characterization of Mediterranean riparian forest","volume":"30","author":"Dunford","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1111\/j.1755-0238.2003.tb00258.x","article-title":"Temporal stability of an NDVI-LAI relationship in a Napa Valley vineyard","volume":"9","author":"Johnson","year":"2003","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1890\/0012-9658(2006)87[2765:MTEOPV]2.0.CO;2","article-title":"Modeling the effect of photosynthetic vegetation properties on the NDVI\u2013LAI relationship","volume":"87","author":"Steltzer","year":"2006","journal-title":"Ecology"},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2004.10.006","article-title":"On the relationship of NDVI with leaf area index in a deciduous forest site","volume":"94","author":"Wang","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.rse.2004.04.009","article-title":"Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements","volume":"91","author":"Fensholt","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_158","first-page":"152","article-title":"Extraction of vegetation information from visible unmanned aerial vehicle images","volume":"31","author":"Wang","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant remote sensing vegetation indices: A review of developments and applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1127\/1432-8364\/2013\/0200","article-title":"UAV-based imaging for multi-temporal, very high Resolution Crop Surface Models to monitor Crop Growth VariabilityMonitoring des Pflanzenwachstums mit Hilfe multitemporaler und hoch aufl\u00f6sender Oberfl\u00e4chenmodelle von Getreidebest\u00e4nden auf Basis von Bildern aus UAV-Befliegungen","volume":"2013","author":"Bendig","year":"2013","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Dong, J., Burnham, J.G., Boots, B., Rains, G., and Dellaert, F. (June, January 29). 4d crop monitoring: Spatio-temporal reconstruction for agriculture. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, Singapore.","DOI":"10.1109\/ICRA.2017.7989447"},{"key":"ref_162","unstructured":"UN DESA (2019, June 17). World Urbanization Prospects: The 2014 Revision. 2015. Available online: https:\/\/esa.un.org\/unpd\/wup\/Publications\/Files\/WUP2014-Report.pdf."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Branco, L.H.C., and Segantine, P.C.L. (2015). MaNIAC-UAV-a methodology for automatic pavement defects detection using images obtained by Unmanned Aerial Vehicles. Journal of Physics: Conference Series, IOP Publishing.","DOI":"10.1088\/1742-6596\/633\/1\/012122"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"515","DOI":"10.5194\/isprs-archives-XLI-B5-515-2016","article-title":"Photogrammetric techniques for road surface analysis","volume":"41","author":"Knyaz","year":"2016","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Phung, M., Dinh, T., Hoang, V., and Ha, Q. (July, January 28). Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles. Proceedings of the 2017 International Symposium on Automation and Robotics in Construction (ISARC), Taipei, Taiwan.","DOI":"10.22260\/ISARC2017\/0115"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1016\/j.renene.2015.09.042","article-title":"Thermal infrared imaging of geothermal environments and by an unmanned aerial vehicle (UAV): A case study of the Wairakei\u2013Tauhara geothermal field, Taupo, New Zealand","volume":"86","author":"Nishar","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_167","unstructured":"Saari, H., Aallos, V.-V., Akuj\u00e4rvi, A., Antila, T., Holmlund, C., Kantoj\u00e4rvi, U., M\u00e4kynen, J., and Ollila, J. (2018). Novel miniaturized hyperspectral sensor for UAV and space applications. Sensors, Systems, and Next-Generation Satellites XIII, SPIE."},{"key":"ref_168","unstructured":"Herold, M., Roberts, D., Smadi, O., and Noronha, V. (April, January 31). Road condition mapping with hyperspectral remote sensing. Proceedings of the 2004 AVIRIS Workshop, Pasadena, CA, USA."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1177\/0309133309339563","article-title":"A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters","volume":"33","author":"Joyce","year":"2009","journal-title":"Prog. Phys. Geogr."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2013.06.011","article-title":"A comprehensive review of earthquake-induced building damage detection with remote sensing techniques","volume":"84","author":"Dong","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.cageo.2014.04.001","article-title":"Development of an UAS for post-earthquake disaster surveying and its application in Ms7. 0 Lushan Earthquake, Sichuan, China","volume":"68","author":"Xu","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.isprsjprs.2017.03.001","article-title":"Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning","volume":"140","author":"Vetrivel","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1109\/LGRS.2015.2429894","article-title":"Unsupervised detection of earthquake-triggered roof-holes from UAV images using joint color and shape features","volume":"12","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Leprince, S., Ayoub, F., Klinger, Y., and Avouac, J. (2007, January 23\u201328). Co-Registration of Optically Sensed Images and Correlation (COSI-Corr): An operational methodology for ground deformation measurements. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423207"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"4119","DOI":"10.3390\/s8074119","article-title":"A Methodology to Validate the InSAR Derived Displacement Field of the September 7(th), 1999 Athens Earthquake Using Terrestrial Surveying. Improvement of the Assessed Deformation Field by Interferometric Stacking","volume":"8","author":"Kotsis","year":"2008","journal-title":"Sensors (Basel)"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/12\/1443\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:59:15Z","timestamp":1760187555000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/12\/1443"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,18]]},"references-count":175,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11121443"],"URL":"https:\/\/doi.org\/10.3390\/rs11121443","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,18]]}}}