GEOBIA 2016 : Solutions and Synergies

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GEOBIA 2016 : Solutions and Synergies., 14 September 2016 - 16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC)

Short Title:GEOBIA 2016
Organisers:
Kerle, N.
Gerke, M.
Lefevre, S.
Description:During the last 10 years the GEOBIA community has grown from a niche discipline to a recognized and vibrant branch of geoinformation science, and methods developed by the growing community have helped to tackle problems in virtually all domains where geographic data are used. The growing importance of image processing, be it of traditional airborne or satellite data, but also complex hyperspectral data stacks, videos, or image data used by other communities (e.g., bio-medical and pharmaceutical), has resulted in a multitude of methodological approaches. Segmentation-based approaches have turned out to be an excellent way to incorporate process and feature knowledge, in addition to providing an effective way of dealing with multi-scale data. As a consequence hundreds of scientific publications have greatly enriched the geoinformation science domain over the past decade.
Item Type:Event (Conference)
Link to this item:http://purl.utwente.nl/proceedings/362
Conference URL:https://www.geobia2016.com/

Proceedings

Kerle, N. and Gerke, M. and Lefevre, S., eds. (2016) GEOBIA 2016 : Solutions and Synergies. University of Twente Faculty of Geo-Information and Earth Observation (ITC). ISBN 9789036542012

Contributions

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Paper

Kulessa, K. and Lang, S. (2016) 3D object-based feature extraction from 3-stereo DSM in urban context.

Blomley, R. de and Jutzi, B. and Weinmann, M. (2016) 3D semantic labeling of ALS point clouds by exploiting multi-scale, multi-type neighborhoods for feature extraction.

Herold, H. and Meinel, G. (2016) Adaptive morphological segmentation-concepts and python implementations.

Jarzabek-Rychard, M. and Maas, H-G. (2016) Aerial image based geometric refinement of building models derived from airborne lidar data.

Hofmann, Peter and Andrejchenko, Vera and Lettmayer, Paul and Schmitzberger, Manuel and Gruber, Michael and Ozan, Izzet and Belgiu, Mariana and Graf, Roland and Lampoltshammer, Thomas Josef and Wegenkittl, Stefan and Blaschke, Thomas (2016) Agent based image analysis (ABIA) - preliminary research results from an implemented framework.

Diesing, M. (2016) Application of geobia to map the seafloor.

Prado, D.F.C. and Carvalho, L.M.T. (2016) Application of object-based accuracy assessment for land cover classification using rapideye images in Southeastern Brazil.

Chauhan, A. and Denis, M.D. and Kumar, M. (2016) Assessing downscaling limits of spatial resolution for awifs and landsat 8 data as compared to liss IV.

Meyer, H.P. and Niekerk, A. van (2016) Assessing edge and area metrics for image segmentation parameter tuning and evaluation.

Feizizadeh, B. and Blaschke, T. (2016) Assessing uncertainties associated with digital elevation models for object based landslide delination.

Rollan, T.A.M. and Blanco, A.C. (2016) Assessment of point cloud analysis in improving object-based agricultural land cover classification using discrete lidar data in Cabadbaran, Agusan del Norte, Phillippines.

Kamps, M. and Seijmonsbergen, A.C. and Rutzinger, M. and Zieher, T. (2016) Assessment of the interaction of land-cover change on shallow landslide occurrence using an automated object-based approach.

Hadjimitsis, D.G. and Agapjou, A. and Themistocleous, K. and Cuca, B. and Nisantzi, A. and Lasaponara, R. and Nole, G. and Tucci, B. and Masini, N. and Krauss, T. and Cerra, D. and Gessner, U. and Schreier, G. (2016) Athena: center of excellence in Cyprus in the field of remote sensing for cultural heritage in the areas of archaeology and cultural heritage.

Baraldi, A. de and Tiede, Dirk and Sudmanss, Martin and Belgiu, Mariana and Lang, Stefan (2016) Automated near real-time earth observation level 2 product generation for semantic querying.

Bock, S. and Immitzer, M. and Atzberger, C. (2016) Automated segmentation parameter selection and classification of urban scenes using open-source software.

Hui, Z. and Hu, Y. and Ziggah, Y.Y. (2016) Automatic building extraction from airborne lidar point cloud based on shift segmentation.

Veena, V.S. and Sai, Subrahmanyam Gorthi and Tapas, Ranjan Martha and Deepak, Mishra and Rama, Rao Nidamanuri (2016) Automatic detection of landslides in object-based environment using open source tools.

Ribeiro, S.R. and Hamulak, T.M. (2016) Characterization of the land-cover and land-use by shape descritors in two areas in Ponta Grossa, PR, BR.

Kanjir, U. and Gregoric Bon, N. (2016) Coastal changes and movements in the wider Vlora (Albania) area.

Cui, Y. and Lefevre, S. and Chapel, L. and Puissant, A. (2016) Combining multiple resolutions into hierarchical representations for kernel-based image classification.

Qian, Yuguo and Zhou, Weiqi and Yan, Jingli and Li, Weifeng and Han, Lijian (2016) Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery.

Volker, A. and Gerschwitz, A. and Bicsan, A. and Fischer, M. and Klink, A. and Lucas, C. and Muller, S. and Muterthies, A. and Schmidt, C. and Stock, G. and Strunck, S. (2016) DLM-update: integration of earth observation technologies in IT-structures of the national mapping authorities in an use case: update of the ATKIS®DLM of the state bureau of surveying and geoinformation Schleswig-Holstein.

Ramirez, S. and Lizarazo, I. (2016) Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images.

Gonzalo-Martin, C. and Garcia-Pedrero, A. and Lillo-Saavedra, M. and Menasalvas, E. (2016) Deep learning for superpixel-based classification of remote sensing images.

Girolamo Neto, C.D. and Pessoa, A.C.M. and Korting, T.S. and Fonseca, L.M.G. (2016) Detecting atlantic forest patches applying geobia and data mining techniques.

Puttemans, Steven and Ranst, Wiebe van and Goedeme, Toon (2016) Detection of photovoltaic installations in RGB aerial imaging: a comparative study.

Weinmann, M. and Mallet, C. and Bredif, M. (2016) Detection, segmentation and localization of individual trees from mms point cloud data.

Visser, Fleur and Buis, Kerst and Verschoren, Veerle and Schoelynck, Jonas (2016) Development of a knowledge driven rule set for classification of submerged aquatic vegetation (SAV) in a clear water stream: where do you draw the boundaries...?

Yang, Naisen and Tang, Hong and Sun, Hongquan and Yang, Xin (2016) Dropband: a convolutional neural network with data augmentation for scene classification of VHR satellite images.

Addink, E.A. and Douma, H. and Duindam, Y.T. and Kleinhans, M.G. (2016) Dynamic objects: unraveling vegetation patterns in a highly dynamic fluvial environment.

Knoth, C. and Nust, D. (2016) Enabling reproducible OBIA with open-source software in docker containers.

Jasvilis, G. and Weise, C. and Zenger-Landolt, B. (2016) Finding complex patterns using template matching.

Soares, A.R. and Korting, T.S. and Fonseca, L.M.G. (2016) First experiments using the image foresting transform (IFT) algorithm for segmentation of remote sensing imagery.

Gao, Yan and Gonzalez, Ignacio and Lopez-Sanchez, Jairo Gabriel and Skutsch, Margaret and Paneque-Galvez, Jaime and Mas, Jean Francois (2016) Forest cover change analysis by object based method using spot and rapideye images.

Höhle, Joachim (2016) From classification results to topographic maps.

Liao, W. and Van Coillie, F. and Zhang, H. and Gautama, S. and Philips, W. (2016) Fusion of optical and lidar images for urban objects recognition.

Ahles, N. and MacFaden, S. and O'Neil-Dunne, J. and Royar, A. and Engel, T. (2016) GEOBIA systems for massive data processing.

Valozic, L. (2016) Getting the act together: segmentation-based land cover classification using rapideye imagery and open street map ancillary data.

Tedesco, A. and Antunes, A.F.B. and Ribeiro, S.R.A. (2016) Gully erosion mapping with high resolution imagery and als data by using tree decision, hierarchical classification and obia.

Körting, T.S. and Namikawa, L.M. and Fonseca, L.M.G. and Felgueiras, C.A. (2016) How to effectively obtain metadata from remote sensing big data?

Atzberger, C. and Immitzer, M. and Bock, S. and Schultz, B. and Vuolo, F. (2016) Identifying suitable segmentation parameters for an object-based image classification.

Csilik, O. and Lang, S. (2016) Improving the speed of multiresolution segmentation using slic superpixels.

Antumes, R.R. and Bias, E.S. and Brites, R.S. and Costa, G.A.O.P. (2016) Integration of open-source tools for object-based monitoring of urban targets.

Happ, P.N. and Ferreira, R.S. and Costa, G.A.O.P. and Feitosa, R.Q. and Bentes, C. and Farias, R. and Achanccaray, P.M. (2016) Interseg: a distributed image segmentation tool.

Prichoa, C.E. and Ribeiro, S.R.A. and Holgado, P.M. (2016) Land cover and land use characterization with geobia in the Pitangui River Basin area, Parana-Brazil.

Verdonck, M.L. and Van Coillie, F. (2016) Local climate zone mapping: a case study in Belgium.

Campomanes, F. and Pada, A.V. and Silapan, J. (2016) Mangrove classification using support vector machines and random forest algorithm: a comparative study.

Wang, Y. and Oude Elberink, S.J. (2016) Map based segmentation of airborne laser scanner data.

Radoux, J. and Bogaert, P. (2016) Map legend and response design: how do they affect accuracy of geobia results.

Mitri, G.H. and Karam, J. (2016) Mapping greenhouse gas emissions and removals from the land use, land use change, and forestry sector at the local level.

Korzeniowska, K. and Korup, O. (2016) Mapping lakes on the Tibetan Plateau with landsat imagery and object-based image analyis.

Mitri, G. and Antoun, E. and Saba, S. and McWethy, D. (2016) Modelling forest fire occurrence in Lebanon using socio-economic and biophysical variables in object-based image analysis.

Valero, J.A. and Lizarazo, I. and Arbelaez, P.A. (2016) Multispectral image segmentation based on Cartesian complexes and their associated oriented matroids.

Tompoulidou, M. and Stefanidou, A. and Grigoriadis, D. and Dragozi, E. and Stavrakoudis, D. and Gitas, I.Z. (2016) National fuel type mapping methodology using geographic object based image analysis and landsat 8 oli imagery.

Kamps, M. and Seijmonsbergen, A.C. and Bouten, W. (2016) Object-based Integrated Landscape Change Analysis: synergy of multi-temporal LiDAR and very high resolution orthophotos.

Djerriri, K. and Safia, A. and Cheriguene, R.S. and Rahli, H.S. and Karoui, M.S. (2016) Object-based VHSR image classification using multiband compact texture unit descriptor.

Wang, M. and Wang, J. (2016) Object-based image analysis based on a region-line primitive association framework.

Louw, G.J. and Niekerk, A. van and Rozanov, A. (2016) Object-based symmetric difference for land surface segmentation scale parameter optimisation.

Zhai, Liang and Sang, Huiyong and Qiao, Qinghua (2016) Object-oriented land cover mapping in China national geographical conditions census.

Audebert, Nicolas and Saux, Bertrand Le and Lefevre, Sebastien (2016) On the usability of deep networks for object-based image analysis.

Arundel, S.T. (2016) Pairing semantics and object-based image analysis for national terrain mapping - a first-case scenario of cirques.

Aryal, J. and Louvet, R. (2016) Quantifying bushfire mapping uncertainty using single and multi-scale approach: a case study from Tasmania, Australia.

Bas, T.P. le (2016) RSOBIA - A new OBIA Toolbar and Toolbox in ArcMap 10.x for Segmentation and Classification.

Kok, R. de and Wezyk, P. and Hejmanowska, B. and Ksiazek, J. (2016) Replacing the use of texture and sealed area in urban fabric classifications by integrating volume and object based distance calculations.

Iersel, W.K. van and Addink, E.A. and Straatsma, M.W. and Middelkoop, H. (2016) River floodplain vegetation classification using multi-temporal high-resolution colour infrared UAV imagery.

Whiteside, T.G. and Bartolo, R.E. (2016) Robust and repeatable ruleset development for hierarchical object-based monitoring of revegetation using high spatial and temporal resolution UAS data.

Naorem, V. de and Kuffer, M. and Verplanke, J. and Kohli, D. (2016) Robustness of rule sets using VHR imagery to detect informal settlements - a case of Mumbai, India.

Simoes, M. and Ferraz, R.P.D. and Begue, A. and Bellon, B. and Freitas, P.L. and Machado, P.L.O.A. and Neves, M.L. and Skorupa, L. and Manzatto, C. (2016) Satellite based multi-scale methods to support the governance of Brazilian low-carbon agriculture (abc plan).

Nex, F. and Dall Mura, M. (2016) Scene classification of urban areas exploiting multi-view high resolution aerial images.

Liu, Y. and Zhang, Y.-D. and Huang, Z. and Wang, M-M. and Yang, D. and Ma, H-M. and Zhang, Y-X. and Li, Y-F. and Li, H-W. and Hu, X-G. (2016) Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis.

Du, S. de and Zhang, F. and Zhang, X. (2016) Semantic classification of urban buildings combining VHR images and GIS data.

Schemala, D. de and Schlesinger, D. and Winkler, P. and Herold, H. and Meinel, G. (2016) Semantic segmentation of settlement patterns in gray-scale map images using RF and CRF within an HPC environment.

d'Oleire-Oltmanns, S. and Gerasch, S. and Tiede, D. and Lang, S. (2016) Small scale landform mapping by integrated optical (2D) and terrain (3D) UAV data.

Csillik, O. (2016) Superpixels: the end of pixels in OBIA. A comparison of stat-of-the-art superpixel methods for remote sensing data.

Cruz, C.B.M. and Almeida, P.M.M. and Barros, R.S. and Vicens, R.S. and Souza, E.M.F.R. and Caris, E.P.A. and Fernandes, M.C. and Menezes, P.M.L. (2016) Supported mapping with multi sensor images through strategy focused on customization and integration of generalized classes by GEOBIA.

Passo, D.P. and Bias, E.S. and Brites, R.S. and Costa, G.A.O.P. and Antunes, R.R. (2016) Susceptibility mapping of linear erosion processes using object-based analysis of VHR images.

Mohammed Badawy, Hani and Moussa, Adel and El-Sheimy, Naser (2016) Synergy between aerial imagery and low density point cloud for automated image classification and point cloud densification.

Krafft, P. and Tiede, D. and Fureder, P. (2016) Template matching to support earth observation based refugee camp analysis in obia workflows - creation and evaluation of a dwelling template library for improving dwelling extraction within an object-based framework.

Souza, E. de and Beuchle, R. and Grecchi, R.C. and Achard, F. (2016) Three-year assessment of the space-time dynamics of burned forest in the Brazilian Amazon, State of Mato Grosso.

Guttler, F.N. and Puissant, A. and Gancarski, P. (2016) Towards a typology of land-cover/land-use evolutions using high resolution satellite image time series: application to the metropolitan area of Strasbourg (France).

Vetrivel, A. and Kerle, N. and Gerke, M. and Nex, F. and Vosselman, G. (2016) Towards automated satellite image segmentation and classification for assessing disaster damage using data-specific features with incremental learning.

Pratomo, J. and Kuffer, M. and Martinez, J. and Kohli, D. (2016) Uncertainties in analyzing the transferability of the generic slum ontology.

Radoux, J. and Defourny, P. (2016) Using lidar and aerial photography to build a geographic object database tuned for ecological model.

Costa, H. and Foody, G.M. and Boyd, D.S. (2016) Using pure and mixed objects in the training of object-based image classifications.

Tanada, E.L.M. and Blanco, A.C. (2016) Using spatial point pattern analysis as supplement for object-based image classification of tree clusters.

Gilbertson, J.K. and Niekerk, A. van (2016) Value of feature reduction for crop differentiation using multi-temporal imagery, machine learning, and object-based image analysis.

Alves, A.M. and Amaro, V.E. (2016) applying geobia method to analyze climate changes associated to energy generation - analysis about oil exploration onshore at potiguar basin.

Lizarazo, I. and Ramirez, S. (2016) A deep learning approach for urban land cover classification from high-spatial resolution imagery and geomorphometric variables.

Mao, T. and Tang, H. and Shu, Y. and Yang, N. (2016) An improved Bayesian nonparametric mixture model to fusing both panchromatic and multispectral images for classification.

Wozniak, E. and Aleksandrowicz, S. (2016) An object-based burnt area detection method based on landsat images - a step forward for automatic global high-resolution mapping.

Antunes, R.R. and Happ, P.N. and Bias, E.S. and Brites, R.S. and Costa, G.A.O.P. and Feitosa, R.Q. (2016) An object-based image interpretation application on cloud computing infrastructure.

Costa, G.A.O.P. and Hofmann, P. and Happ, P.N. and Feitosa, R.Q. (2016) An object-based meta knowledge model for a distributed image interpretation system.

Gu, H.Y. and Li, H.T. and Yan, L. and Blaschke, T. (2016) An object-based semantic classification method of high resolution satellite imagery using ontology.

Grippa, T. and Lennert, M. and Beaumont, B. and Vanhuysse, S. and Stephenne, N. and Wolff, E. (2016) An open-source semi-automated processing chain for urban obia classification.

Vogels, M.F.A. de and Jong, S.M. de and Sterk, G. and Addink, E.A. (2016) A semi-automatic cropland mapping approach using GEOBIA and random forests on black-and-white aerial photography.

Garcia-Pedrero, A. and Gonzalo-Martin, C. and Lillo-Saavedra, M. and Ortiz-Toto, C. (2016) A web-based platform for remote sensing image annotation.

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