{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T04:53:02Z","timestamp":1780375982129,"version":"3.54.1"},"reference-count":66,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T00:00:00Z","timestamp":1648857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1737563"],"award-info":[{"award-number":["1737563"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As rapid urbanization occurs in cities worldwide, the importance of maintaining updated digital elevation models (DEM) will continue to increase. However, due to the cost of generating high-resolution DEM over large spatial extents, the temporal resolution of DEMs is coarse in many regions. Low-cost unmanned aerial vehicles (UAS) and DEM data fusion provide a partial solution to improving the temporal resolution of DEM but do not identify which areas of a DEM require updates. We present Rapid-DEM, a framework that identifies and prioritizes locations with a high likelihood of an urban topographic change to target UAS data acquisition and fusion to provide up-to-date DEM. The framework uses PlanetScope 3 m satellite imagery, Google Earth Engine, and OpenStreetMap for land cover classification. GRASS GIS generates a contextualized priority queue from the land cover data and outputs polygons for UAS flight planning. Low-cost UAS fly the identified areas, and WebODM generates a DEM from the UAS survey data. The UAS data is fused with an existing DEM and uploaded to a public data repository. To demonstrate Rapid-DEM a case study in the Walnut Creek Watershed in Wake County, North Carolina is presented. Two land cover classification models were generated using random forests with an overall accuracy of 89% (kappa 0.86) and 91% (kappa 0.88). The priority queue identified 109 priority locations representing 1.5% area of the watershed. Large forest clearings were the highest priority locations, followed by newly constructed buildings. The highest priority site was a 0.5 km2 forest clearing that was mapped with UAS, generating a 15 cm DEM. The UAS DEM was resampled to 3 m resolution and fused with USGS NED 1\/9 arc-second DEM data. Surface water flow was simulated over the original and updated DEM to illustrate the impact of the topographic change on flow patterns and highlight the importance of timely DEM updates.<\/jats:p>","DOI":"10.3390\/rs14071718","type":"journal-article","created":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T06:04:01Z","timestamp":1648965841000},"page":"1718","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2903-9924","authenticated-orcid":false,"given":"Corey T.","family":"White","sequence":"first","affiliation":[{"name":"Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0426-6693","authenticated-orcid":false,"given":"William","family":"Reckling","sequence":"additional","affiliation":[{"name":"Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5120-5538","authenticated-orcid":false,"given":"Anna","family":"Petrasova","sequence":"additional","affiliation":[{"name":"Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1247-6212","authenticated-orcid":false,"given":"Ross K.","family":"Meentemeyer","sequence":"additional","affiliation":[{"name":"Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA"},{"name":"Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6906-3398","authenticated-orcid":false,"given":"Helena","family":"Mitasova","sequence":"additional","affiliation":[{"name":"Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA"},{"name":"Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,2]]},"reference":[{"key":"ref_1","unstructured":"UN (2019). Secretary-General. Progress towards the Sustainable Development Goals: Report of the Secretary-General, UN."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Vogler, J.B., and Vukomanovic, J. (2021). Trends in United States Human Footprint Revealed by New Spatial Metrics of Urbanization and Per Capita Land Change. Sustainability, 13.","DOI":"10.3390\/su132212852"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/S2095-6339(15)30002-2","article-title":"Long-Term Hydrological Impacts of Land Use\/Land Cover Change from 1984 to 2010 in the Little River Watershed, Tennessee","volume":"2","author":"Zhu","year":"2014","journal-title":"Int. Soil Water Conserv. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"White, C.T., Mitasova, H., BenDor, T.K., Foy, K., Pala, O., Vukomanovic, J., and Meentemeyer, R.K. (2021). Spatially Explicit Fuzzy Cognitive Mapping for Participatory Modeling of Stormwater Management. Land, 10.","DOI":"10.3390\/land10111114"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1021\/acs.est.6b04267","article-title":"The Potential of Knowing More: A Review of Data-Driven Urban Water Management","volume":"51","author":"Eggimann","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/19479830903561035","article-title":"Multi-source remote sensing data fusion: Status and trends","volume":"1","author":"Zhang","year":"2010","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/014311698215748","article-title":"Review article Multisensor image fusion in remote sensing: Concepts, methods and applications","volume":"19","author":"Pohl","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"You, Y., Cao, J., and Zhou, W. (2020). A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios. Remote Sens., 12.","DOI":"10.3390\/rs12152460"},{"key":"ref_9","unstructured":"Planet Team (2017). Planet Application Program Interface: In Space for Life on Earth, Planet Team."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Frazier, A.E., and Hemingway, B.L. (2021). A Technical Review of Planet Smallsat Data: Practical Considerations for Processing and Using PlanetScope Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13193930"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Luo, N., Wan, T., Hao, H., and Lu, Q. (2019). Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification Over Urban Areas. Remote Sens., 11.","DOI":"10.3390\/rs11010088"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1109\/LGRS.2017.2762466","article-title":"Classification of High-Resolution Remote-Sensing Image Using OpenStreetMap Information","volume":"14","author":"Wan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, W., He, C., Fang, J., Zheng, J., Fu, H., and Yu, L. (2019). Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data. Remote Sens., 11.","DOI":"10.3390\/rs11040403"},{"key":"ref_14","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2020). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Albrecht, C.M., Zhang, R., Cui, X., Freitag, M., Hamann, H.F., Klein, L.J., Finkler, U., Marianno, F., Schmude, J., and Bobroff, N. (2020). Change Detection from Remote Sensing to Guide OpenStreetMap Labeling. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9070427"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Stoker, J., and Miller, B. (2022). The Accuracy and Consistency of 3D Elevation Program Data: A Systematic Analysis. Remote Sens., 14.","DOI":"10.3390\/rs14040940"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"159","DOI":"10.5194\/isprs-annals-III-8-159-2016","article-title":"Overland Flow Analysis Using Time Series Of SUAS-Derived Elevation Models","volume":"III-8","author":"Jeziorska","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40965-017-0019-2","article-title":"Fusion of high-resolution DEMs for water flow modeling","volume":"2","author":"Petrasova","year":"2017","journal-title":"Open Geospat. Data Softw. Stand."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3389\/feart.2015.00085","article-title":"Perspectives on Open Access High Resolution Digital Elevation Models to Produce Global Flood Hazard Layers","volume":"3","author":"Sampson","year":"2016","journal-title":"Front. Earth Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wessels, K., van den Bergh, F., Roy, D., Salmon, B., Steenkamp, K., MacAlister, B., Swanepoel, D., and Jewitt, D. (2016). Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers. Remote Sens., 8.","DOI":"10.3390\/rs8110888"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1080\/22797254.2018.1451782","article-title":"Using Google Earth Engine to detect land cover change: Singapore as a use case","volume":"51","author":"Sidhu","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Reckling, W., Mitasova, H., Wegmann, K., Kauffman, G., and Reid, R. (2021). Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection. Drones, 5.","DOI":"10.3390\/drones5040110"},{"key":"ref_24","unstructured":"(2022, February 11). OpenDroneMap Authors WebODM; WebODM. Available online: https:\/\/www.opendronemap.org\/webodm\/."},{"key":"ref_25","unstructured":"GRASS Development Team (2022, February 28). Geographic Resources Analysis Support System (GRASS GIS) Software, Version 8.0. Available online: https:\/\/grass.osgeo.org."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Krishnan, S., Crosby, C., Nandigam, V., Phan, M., Cowart, C., Baru, C., and Arrowsmith, R. (2011, January 23\u201325). OpenTopography: A services oriented architecture for community access to LIDAR topography. Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications, COM.Geo \u201911, Washington, DC, USA.","DOI":"10.1145\/1999320.1999327"},{"key":"ref_27","unstructured":"Planet Team (2019, February 08). Planet Imagery Product Specifications; Planet Labs Inc. Available online: https:\/\/assets.planet.com\/docs\/."},{"key":"ref_28","unstructured":"Planet Development Team (2022, February 11). Planet API\u2014Python Client\u2014Planet API Client 1.4.6 Documentation. Available online: https:\/\/planetlabs.github.io\/planet-client-python\/index.html."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Barrington-Leigh, C., and Millard-Ball, A. (2017). The world\u2019s user-generated road map is more than 80% complete. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0180698"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1080\/10095020.2017.1371385","article-title":"Open land-use map: A regional land-use mapping strategy for incorporating OpenStreetMap with earth observations","volume":"20","author":"Yang","year":"2017","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Crawford, B., Swanson, E., Schultz-Fellenz, E., Collins, A., Dann, J., Lathrop, E., and Milazzo, D. (2021). A New Method for High Resolution Surface Change Detection: Data Collection and Validation of Measurements from UAS at the Nevada National Security Site, Nevada, USA. Drones, 5.","DOI":"10.3390\/drones5020025"},{"key":"ref_32","unstructured":"Jensen, J.R. (2015). Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall Press. [4th ed.]."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1006\/cviu.2002.0960","article-title":"Thresholding for Change Detection","volume":"86","author":"Rosin","year":"2002","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s101090100076","article-title":"Change detection thresholds for remotely sensed images","volume":"4","author":"Rogerson","year":"2002","journal-title":"J. Geogr. Syst."},{"key":"ref_35","unstructured":"Jamalabad, M., and Abkar, A. (2004, January 12\u201323). Forest Canopy Density Monitoring, Using Satellite Images. Proceedings of the 20th ISPRS Congress, International Society for Photogrammetry and Remote Sensing, Istanbul, Turkey."},{"key":"ref_36","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS, NASA, Goddard Space Flight Center. Technical Presentations. NASA SP-351."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/0034-4257(91)90017-Z","article-title":"Normalized difference vegetation index measurements from the advanced very high resolution radiometer","volume":"35","author":"Goward","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"16024","DOI":"10.3390\/rs71215819","article-title":"Automatic Labelling and Selection of Training Samples for High-Resolution Remote Sensing Image Classification over Urban Areas","volume":"7","author":"Huang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"012042","DOI":"10.1088\/1755-1315\/98\/1\/012042","article-title":"Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine","volume":"98","author":"Farda","year":"2017","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5243","DOI":"10.1080\/01431160903131000","article-title":"Sampling designs for accuracy assessment of land cover","volume":"30","author":"Stehman","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","unstructured":"DroneDeploy Team (2020, September 28). Drone & UAV Mapping Platform; DroneDeploy. Available online: https:\/\/www.dronedeploy.com\/."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Stott, E., Williams, R.D., and Hoey, T.B. (2020). Ground Control Point Distribution for Accurate Kilometre-Scale Topographic Mapping Using an RTK-GNSS Unmanned Aerial Vehicle and SfM Photogrammetry. Drones, 4.","DOI":"10.3390\/drones4030055"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rogers, S.R., Manning, I., and Livingstone, W. (2020). Comparing the Spatial Accuracy of Digital Surface Models from Four Unoccupied Aerial Systems: Photogrammetry versus LiDAR. Remote Sens., 12.","DOI":"10.3390\/rs12172806"},{"key":"ref_47","unstructured":"(2022, February 11). OpenDroneMap. ODM\u2014A Command Line Toolkit to Generate Maps, Point Clouds, 3D Models and DEMs from Drone, Balloon or Kite Images, Available online: https:\/\/github.com\/OpenDroneMap\/ODM."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Pell, T., Li, J.Y.Q., and Joyce, K.E. (2022). Demystifying the Differences between Structure-from-MotionSoftware Packages for Pre-Processing Drone Data. Drones, 6.","DOI":"10.3390\/drones6010024"},{"key":"ref_49","unstructured":"Dewitz, J., and U.S. Geological Survey (2020, July 01). National Land Cover Database (NLCD) 2019 Products (ver. 2.0, July 2020), Available online: https:\/\/doi.org\/10.5066\/P96HHBIE."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.isprsjprs.2020.02.019","article-title":"Conterminous United States land cover change patterns 2001\u20132016 from the 2016 National Land Cover Database","volume":"162","author":"Homer","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","unstructured":"Krautwurst, Z., Petrasova, A., Petras, V., GRASS Development Team 2021, and r.in.usgs (2021, December 17). Geographic Resources Analysis Support System (GRASS) Software, Version 8.0, Available online: https:\/\/grass.osgeo.org\/grass80\/manuals\/addons\/r.in.usgs.html."},{"key":"ref_52","unstructured":"Town of Cary (2022, January 30). Storm Inlets; Wake County Open Data. Available online: https:\/\/maps.townofcary.org\/arcgis\/rest\/services\/Infrastructure\/StormwaterNetwork\/MapServer\/35."},{"key":"ref_53","unstructured":"Gesch, D.B., Evans, G.A., Oimoen, M.J., and Arundel, S. (2018). The National Elevation Dataset. Digital Elevation Model Technologies and Applications, The DEM Users Manual; American Society for Photogrammetry and Remote Sensing. [3rd ed.]."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1016\/S0167-5648(04)80159-X","article-title":"Path sampling method for modeling overland water flow, sediment transport, and short term terrain evolution in Open Source GIS","volume":"Volume 55","author":"Mitasova","year":"2004","journal-title":"Developments in Water Science"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier\u2014The Role of Image Composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Xie, S., Liu, L., Zhang, X., Yang, J., Chen, X., and Gao, Y. (2019). Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11243023"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11004-021-09946-w","article-title":"A Truly Spatial Random Forests Algorithm for Geoscience Data Analysis and Modelling","volume":"54","author":"Talebi","year":"2022","journal-title":"Math. Geosci."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Achanta, R., and S\u00fcsstrunk, S. (2017, January 21\u201326). Superpixels and Polygons Using Simple Non-iterative Clustering. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.520"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2018.12.013","article-title":"Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product","volume":"222","author":"Gray","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Panagiotou, E., Chochlakis, G., Grammatikopoulos, L., and Charou, E. (2020). Generating Elevation Surface from a Single RGB Remotely Sensed Image Using Deep Learning. Remote Sens., 12.","DOI":"10.3390\/rs12122002"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ghuffar, S. (2018). DEM Generation from Multi Satellite PlanetScope Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10091462"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"127361","DOI":"10.1016\/j.jclepro.2021.127361","article-title":"Roles of geospatial technology in eco-industrial park site selection: State\u2013of\u2013the-art review","volume":"309","author":"Nuhu","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_63","unstructured":"U.S. Geological Survey (2022, February 07). USGS NED ned19_n36x00_w078x75_nc_statewide_2003 1\/9 arc-second 2012 15 \u00d7 15 Minute IMG, Available online: https:\/\/www.sciencebase.gov\/catalog\/item\/581d2892e4b08da350d5ff30."},{"key":"ref_64","unstructured":"(2022, February 07). Floodplain Mapping Program, North Carolina Division of Emergency Management, Available online: https:\/\/fris.nc.gov\/fris\/Download.aspx."},{"key":"ref_65","unstructured":"Neteler, M., Gbbert, S., Tawalika, C., Bettge, A., Benelcadi, H., L\u00f6w, F., Adams, T., and Paulsen, H. (2019, January 19\u201321). Actinia: Cloud Based Geoprocessing (Version 1). Proceedings of the 2019 Conference on Big Data from Space (BiDS\u20192019), Munich, Germany."},{"key":"ref_66","unstructured":"White, C.T., Petrasova, A., Reckling, W., and Mitasova, H. (2022, February 12). Rapid-DEM. OSF. 25 March. Available online: http:\/\/doi.org\/10.17605\/OSF.IO\/YG6H8."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1718\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:48:59Z","timestamp":1760136539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1718"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,2]]},"references-count":66,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14071718"],"URL":"https:\/\/doi.org\/10.3390\/rs14071718","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,2]]}}}