{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:53:01Z","timestamp":1778082781002,"version":"3.51.4"},"reference-count":87,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,9]],"date-time":"2019-11-09T00:00:00Z","timestamp":1573257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund","doi-asserted-by":"publisher","award":["DK W 1237-N23"],"award-info":[{"award-number":["DK W 1237-N23"]}],"id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models\u2019 training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model\u2019s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.<\/jats:p>","DOI":"10.3390\/s19224893","type":"journal-article","created":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T04:07:07Z","timestamp":1573531627000},"page":"4893","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3275-8436","authenticated-orcid":false,"given":"Hejar","family":"Shahabi","sequence":"first","affiliation":[{"name":"Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1728-0008","authenticated-orcid":false,"given":"Ben","family":"Jarihani","sequence":"additional","affiliation":[{"name":"Mountain Societies Research Institute, University of Central Asia, Khorog 736000, Tajikistan"},{"name":"Sustainability Research Centre, University of the Sunshine Coast, Sunshine Coast, QLD 4556, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sepideh","family":"Tavakkoli Piralilou","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics-Z _GIS, University of Salzburg, 5020 Salzburg, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Chittleborough","sequence":"additional","affiliation":[{"name":"Mountain Societies Research Institute, University of Central Asia, Khorog 736000, Tajikistan"},{"name":"School of Physical Sciences, University of Adelaide, Adelaide 5005, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7196-5051","authenticated-orcid":false,"given":"Mohammadtaghi","family":"Avand","sequence":"additional","affiliation":[{"name":"Faculty of Natural Resources and Marine Sciences, Tarbiat Modares Unviversity (TMU), Tehran 46414-356, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9664-8770","authenticated-orcid":false,"given":"Omid","family":"Ghorbanzadeh","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics-Z _GIS, University of Salzburg, 5020 Salzburg, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1002\/ldr.2629","article-title":"Reducing sediment connectivity through man-made and natural sediment sinks in the Minizr catchment, Northwest Ethiopia","volume":"28","author":"Mekonnen","year":"2017","journal-title":"Land Degrad. Dev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tien Bui, D., Shirzadi, A., Shahabi, H., Chapi, K., Omidavr, E., Pham, B.T., Talebpour Asl, D., Khaledian, H., Pradhan, B., and Panahi, M. (2019). A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran). Sensors, 19.","DOI":"10.3390\/s19112444"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.catena.2005.06.001","article-title":"Gully erosion: Impacts, factors and control","volume":"63","author":"Valentin","year":"2005","journal-title":"Catena"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1002\/esp.404","article-title":"Impact of road building on gully erosion risk: A case study from the northern Ethiopian highlands","volume":"27","author":"Nyssen","year":"2002","journal-title":"Earth Surf. Process. Landf. J. Br. Geomorphol. Res. Group"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/S0341-8162(02)00143-1","article-title":"Gully erosion and environmental change: Importance and research needs","volume":"50","author":"Poesen","year":"2003","journal-title":"Catena"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.still.2015.07.018","article-title":"Evaluating ephemeral gully erosion impact on Zea mays L. yield and economics using AnnAGNPS","volume":"155","author":"Li","year":"2016","journal-title":"Soil Tillage Res."},{"key":"ref_7","unstructured":"Desta, L., and Adunga, B. (2012). A Field Guide on Gully Prevention and Control Nile Basin Initiative, Eastern Nile Subsidiary Action Program (ENSAP), Eastern Nile, Technical Regional Office (ENTRO). Eastern Nile Watershed Management Project."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/S0341-8162(02)00131-5","article-title":"Geomorphological investigation on gully erosion in the Rift Valley and the northern highlands of Ethiopia","volume":"50","author":"Billi","year":"2003","journal-title":"Catena"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.catena.2014.10.022","article-title":"The impact of permanent gullies on present-day land use and agriculture in loess areas (E. Poland)","volume":"126","author":"Gawrysiak","year":"2015","journal-title":"Catena"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.catena.2008.07.001","article-title":"Thresholds for channel initiation at road drain outlets","volume":"75","author":"Takken","year":"2008","journal-title":"Catena"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ionita, I., Fullen, M.A., Zg\u0142obicki, W., and Poesen, J. (2015). Gully Erosion as a Natural and Human-Induced Hazard, Springer.","DOI":"10.1007\/s11069-015-1935-z"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.geomorph.2012.05.030","article-title":"Gully erosion in sub-tropical south-east Queensland, Australia","volume":"173","author":"Saxton","year":"2012","journal-title":"Geomorphology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.matcom.2005.02.022","article-title":"Modelling sources of sediment at sub-catchment scale: An example from the Burdekin Catchment, North Queensland, Australia","volume":"69","author":"Post","year":"2005","journal-title":"Math. Comput. Simul."},{"key":"ref_14","unstructured":"Brodie, J., Waterhouse, J., Schaffelke, B., Kroon, F., Thorburn, P., Rolfe, J., Johnson, J., Fabricius, K., Lewis, S., and Devlin, M. (2013). Land use impacts on Great Barrier Reef water quality and ecosystem condition, Reef Water Quality Protection Plan Secretariat."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.marpolbul.2004.11.028","article-title":"Effects of terrestrial runoff on the ecology of corals and coral reefs: Review and synthesis","volume":"50","author":"Fabricius","year":"2005","journal-title":"Mar. Pollut. Bull."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1890\/08-2023.1","article-title":"Water quality as a regional driver of coral biodiversity and macroalgae on the Great Barrier Reef","volume":"20","author":"Fabricius","year":"2010","journal-title":"Ecol. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.marpolbul.2011.10.018","article-title":"River loads of suspended solids, nitrogen, phosphorus and herbicides delivered to the Great Barrier Reef lagoon","volume":"65","author":"Kroon","year":"2012","journal-title":"Mar. Pollut. Bull."},{"key":"ref_18","unstructured":"Bainbridge, Z.T. (2015). Tracing the Sources, Transport and Dispersal of Suspended Sediment from the Burdekin River Catchment into the Great Barrier Reef Lagoon, James Cook University."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.agee.2012.02.002","article-title":"Using sediment tracing to assess processes and spatial patterns of erosion in grazed rangelands, Burdekin River basin, Australia","volume":"180","author":"Wilkinson","year":"2013","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.geomorph.2013.08.021","article-title":"Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy)","volume":"204","author":"Conoscenti","year":"2014","journal-title":"Geomorphology"},{"key":"ref_21","first-page":"85","article-title":"Assessment of landslide susceptibility, semi-quantitative risk and management in the Ilam dam basin, Ilam, Iran","volume":"3","author":"Kornejady","year":"2015","journal-title":"Environ. Resour. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.pce.2014.02.002","article-title":"Potential of weight of evidence modelling for gully erosion hazard assessment in Mbire District\u2013Zimbabwe","volume":"67","author":"Dube","year":"2014","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1007\/s11069-016-2239-7","article-title":"Gully erosion susceptibility mapping: The role of GIS-based bivariate statistical models and their comparison","volume":"82","author":"Rahmati","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s11069-015-1703-0","article-title":"Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: Advantages and limitations","volume":"79","author":"Conoscenti","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1002\/env.999","article-title":"Incorporating uncertainty in gully erosion calculations using the random forests modelling approach","volume":"21","author":"Kuhnert","year":"2010","journal-title":"Environmetrics"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.scitotenv.2017.07.198","article-title":"Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling","volume":"609","author":"Pourghasemi","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_27","unstructured":"Ghorbanzadeh, O., and Blaschke, T. (2019, November 08). Optimizing Sample Patches Selection of CNN to Improve the mIOU on Landslide Detection. Available online: https:\/\/pdfs.semanticscholar.org\/022f\/b2150b1a0bbf2051b48a9eacf104423d3400.pdf?_ga=2.190701539.919042809.1573265293-1274004429.1559794368."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s11069-018-3449-y","article-title":"A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping","volume":"94","author":"Ghorbanzadeh","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree","volume":"13","author":"Bui","year":"2016","journal-title":"Landslides"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1080\/10106049.2018.1425738","article-title":"A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment","volume":"33","author":"Chen","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Moslem, S., Blaschke, T., and Duleba, S. (2018). Sustainable urban transport planning considering different stakeholder groups by an interval-AHP decision support model. Sustainability, 11.","DOI":"10.3390\/su11010009"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1080\/19475705.2017.1413012","article-title":"Multi-criteria risk evaluation by integrating an analytical network process approach into GIS-based sensitivity and uncertainty analyses","volume":"9","author":"Ghorbanzadeh","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_33","first-page":"203","article-title":"Assessing flood hazard using GIS based multi-criteria decision making approach; study area: East-Azerbaijan province (Kaleybar Chay Basin)","volume":"8","author":"Pirnazar","year":"2017","journal-title":"J. Flood Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.1080\/01431160600857469","article-title":"Automatic identification of erosion gullies with ASTER imagery in the Brazilian Cerrados","volume":"28","author":"Vrieling","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101","DOI":"10.2113\/gseegeosci.21.2.101","article-title":"Gully erosion mapping using object-based and pixel-based image classification methods","volume":"21","author":"Karami","year":"2015","journal-title":"Environ. Eng. Geosci."},{"key":"ref_36","first-page":"109","article-title":"Gully features extraction using remote sensing techniques","volume":"1","author":"Mararakanye","year":"2012","journal-title":"S. Afr. J. Geomat."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8287","DOI":"10.3390\/rs6098287","article-title":"Detection of gully-affected areas by applying object-based image analysis (OBIA) in the region of Taroudannt, Morocco","volume":"6","author":"Marzolff","year":"2014","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.catena.2014.01.010","article-title":"Quantifying temporal changes in gully erosion areas with object oriented analysis","volume":"128","author":"Shruthi","year":"2015","journal-title":"Catena"},{"key":"ref_39","unstructured":"Francipane, A., Mussom\u00e8, F., Cipolla, G., and Noto, L. (2017, January 23\u201328). Object-based image analysis technique for gully mapping using topographic data at very high resolution (VHR). Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Abdi, O. (2019). Climate-Triggered Insect Defoliators and Forest Fires Using Multitemporal Landsat and TerraClimate Data in NE Iran: An Application of GEOBIA TreeNet and Panel Data Analysis. Sensors, 19.","DOI":"10.3390\/s19183965"},{"key":"ref_41","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_42","unstructured":"Blaschke, T., and Piralilo, S.T. (2018). The Near-Decomposability Paradigm Re-Interpreted for Place-Based GIS, Konferenzbeitrag."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1023\/A:1012460413855","article-title":"Saliency, scale and image description","volume":"45","author":"Kadir","year":"2001","journal-title":"Int. J. Comput. Vis."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2013.11.018","article-title":"Automated parameterisation for multi-scale image segmentation on multiple layers","volume":"88","author":"Csillik","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","first-page":"218","article-title":"SegOptim\u2014A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data","volume":"76","author":"Marcos","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","article-title":"Ensemble-based classifiers","volume":"33","author":"Rokach","year":"2010","journal-title":"Artif. Intell. Rev."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.catena.2017.10.010","article-title":"Spatial modelling of gully erosion in Mazandaran Province, northern Iran","volume":"161","author":"Zabihi","year":"2018","journal-title":"Catena"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1007\/s12665-012-2205-y","article-title":"A GIS-based approach for gully erosion susceptibility modelling: A test in Sicily, Italy","volume":"70","author":"Conoscenti","year":"2013","journal-title":"Environ. Earth Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.geomorph.2018.04.011","article-title":"Large-scale mapping of gully-affected areas: An approach integrating Google Earth images and terrain skeleton information","volume":"314","author":"Liu","year":"2018","journal-title":"Geomorphology"},{"key":"ref_51","unstructured":"Geyik, M. (1986). FAO Watershed Management Field Manual: Gully Control, Food and Agriculture Organization of the United Nations."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Pradhan, B., Pourghasemi, H., Rezaei, K., and Kerle, N. (2018). Spatial modelling of gully erosion using GIS and R programing: A comparison among three data mining algorithms. Appl. Sci., 8.","DOI":"10.3390\/app8081369"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.geomorph.2017.09.006","article-title":"Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion","volume":"298","author":"Rahmati","year":"2017","journal-title":"Geomorphology"},{"key":"ref_54","first-page":"417325","article-title":"A quantitative study of gully erosion based on object-oriented analysis techniques: A case study in Beiyanzikou catchment of Qixia, Shandong, China","volume":"2014","author":"Wang","year":"2014","journal-title":"Sci. World J."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Veress, M., N\u00e9meth, I., and Schl\u00e4ffer, R. (2013). The effects of flash floods on gully erosion and alluvial fan accumulation in the K\u0151szeg Mountains. Geomorphological Impacts of Extreme Weather, Springer.","DOI":"10.1007\/978-94-007-6301-2_19"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/JSTARS.2014.2350036","article-title":"Object-based image analysis and digital terrain analysis for locating landslides in the Urmia Lake Basin, Iran","volume":"7","author":"Blaschke","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Tavakkoli Piralilou, S., Shahabi, H., Jarihani, B., Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., and Aryal, J. (2019). Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas. Remote Sens., 11.","DOI":"10.3390\/rs11212575"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., and Aryal, J. (2019). Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1061\/(ASCE)0887-3801(1994)8:2(129)","article-title":"Where and Why Artificial Neural Networks Are Applicable in Civil Engineering","volume":"8","author":"Garrett","year":"1994","journal-title":"J. Comput. Civil Eng."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1002\/esp.593","article-title":"Landslide susceptibility analysis using GIS and artificial neural network","volume":"28","author":"Lee","year":"2003","journal-title":"Earth Surf. Process. Landf. J. Br. Geomorphol. Res. Group"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Aryal, J., and Gholaminia, K. (2018). A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. J. Spat. Sci., 1\u201317.","DOI":"10.1080\/14498596.2018.1505564"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.1007\/s12517-012-0610-x","article-title":"Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: A comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms","volume":"6","author":"Zare","year":"2013","journal-title":"Arab. J. Geosci."},{"key":"ref_63","first-page":"1","article-title":"Landslide risk analysis using artificial neural network model focussing on different training sites","volume":"4","author":"Pradhan","year":"2009","journal-title":"Int. J. Phys. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1007\/s10706-017-0236-6","article-title":"Evaluation of MLP and RBF methods for hazard zonation of landslides triggered by the Twin Ahar-Varzeghan earthquakes","volume":"35","author":"Bagheri","year":"2017","journal-title":"Geotech. Geol. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1080\/19475705.2017.1407368","article-title":"Assessment of the effects of training data selection on the landslide susceptibility mapping: A comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)","volume":"9","author":"Kalantar","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1016\/j.rse.2011.05.013","article-title":"Object-oriented mapping of landslides using Random Forests","volume":"115","author":"Stumpf","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1016\/j.jenvman.2018.11.110","article-title":"Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS","volume":"232","author":"Arabameri","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.geomorph.2014.04.006","article-title":"Object-based gully system prediction from medium resolution imagery using Random Forests","volume":"216","author":"Shruthi","year":"2014","journal-title":"Geomorphology"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_70","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_71","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"853","DOI":"10.5194\/nhess-5-853-2005","article-title":"Spatial prediction models for landslide hazards: Review, comparison and evaluation","volume":"5","author":"Brenning","year":"2005","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s11004-011-9379-9","article-title":"Support vector machines for landslide susceptibility mapping: The Staffora River Basin case study, Italy","volume":"44","author":"Ballabio","year":"2012","journal-title":"Math. Geosci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s10346-013-0391-7","article-title":"Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression","volume":"11","author":"Kavzoglu","year":"2014","journal-title":"Landslides"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Van der Laan, M.J., Polley, E.C., and Hubbard, A.E. (2007). Super learner. Stat. Appl. Genet. Mol. Biol., 6.","DOI":"10.2202\/1544-6115.1309"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Lee, S., and Oh, H.-J. (2012). Ensemble-based landslide susceptibility maps in Jinbu area, Korea. Terrigenous Mass Movements, Springer.","DOI":"10.1007\/978-3-642-25495-6_7"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1080\/01431161.2012.747018","article-title":"GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments","volume":"34","author":"Aguilar","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/22797254.2017.1419441","article-title":"The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method","volume":"51","author":"Labib","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/22797254.2017.1297540","article-title":"Object-based water body extraction model using Sentinel-2 satellite imagery","volume":"50","author":"Kaplan","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"5409","DOI":"10.1080\/01431161.2013.790574","article-title":"Identifying potential areas of Cannabis sativa plantations using object-based image analysis of SPOT-5 satellite data","volume":"34","author":"Lisita","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.5194\/nhess-11-2715-2011","article-title":"Landslide mapping with multi-scale object-based image analysis\u2014A case study in the Baichi watershed, Taiwan","volume":"11","author":"Lahousse","year":"2011","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.geomorph.2013.09.012","article-title":"Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method","volume":"204","author":"Moosavi","year":"2014","journal-title":"Geomorphology"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"4318","DOI":"10.3390\/rs70404318","article-title":"Automatic case-based reasoning approach for landslide detection: Integration of object-oriented image analysis and a genetic algorithm","volume":"7","author":"Dou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Pradhan, B., Seeni, M.I., and Nampak, H. (2017). Integration of LiDAR and QuickBird data for automatic landslide detection using object-based analysis and random forests. Laser Scanning Applications in Landslide Assessment, Springer.","DOI":"10.1007\/978-3-319-55342-9_4"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Bunn, M.D., Leshchinsky, B.A., Olsen, M.J., and Booth, A. (2019). A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives. Remote Sens., 11.","DOI":"10.3390\/rs11030303"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"105264","DOI":"10.1016\/j.enggeo.2019.105264","article-title":"Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data","volume":"260","author":"Comert","year":"2019","journal-title":"Eng. Geol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4893\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:33:13Z","timestamp":1760189593000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4893"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,9]]},"references-count":87,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19224893"],"URL":"https:\/\/doi.org\/10.3390\/s19224893","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,9]]}}}