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Elisabeth Schoepfer
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany

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Preprint content
Published: 04 March 2021
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Knowledge on the key structural characteristics of exposed buildings is crucial for accurate risk modeling with regard to natural hazards. In risk assessment this information is used to interlink exposed buildings with specific representative vulnerability models and is thus a prerequisite to implement sound risk models. The acquisition of such data by conventional building surveys is usually highly expensive in terms of labor, time, and money. Institutional data bases such as census or tax assessor data provide alternative sources of information. Such data, however, are often inappropriate, out-of-date, or not available. Today, the large-area availability of systematically collected street-level data due to global initiatives such as Google Street View, among others, offers new possibilities for the collection of in-situ data. At the same time, developments in machine learning and computer vision – in deep learning in particular – show high accuracy in solving perceptual tasks in the image domain. Thereon, we explore the potential of an automatized and thus efficient collection of vulnerability related building characteristics. To this end, we elaborated a workflow where the inference of building characteristics (e.g., the seismic building structural type, the material of the lateral load resisting system or the building height) from geotagged street-level imagery is tasked to a custom-trained Deep Convolutional Neural Network. The approach is applied and evaluated for the earthquake-prone Chilean capital Santiago de Chile. Experimental results are presented and show high accuracy in the derivation of addressed target variables. This emphasizes the potential of the proposed methodology to contribute to large-area collection of in-situ information on exposed buildings.

ACS Style

Patrick Aravena Pelizari; Christian Geiß; Elisabeth Schoepfer; Torsten Riedlinger; Paula Aguirre; Hernán Santa María; Yvonne Merino Peña; Juan Camilo Gómez Zapata; Massimiliano Pittore; Hannes Taubenböck. Street-Level Imagery and Deep Learning for Characterization of Exposed Buildings. 2021, 1 .

AMA Style

Patrick Aravena Pelizari, Christian Geiß, Elisabeth Schoepfer, Torsten Riedlinger, Paula Aguirre, Hernán Santa María, Yvonne Merino Peña, Juan Camilo Gómez Zapata, Massimiliano Pittore, Hannes Taubenböck. Street-Level Imagery and Deep Learning for Characterization of Exposed Buildings. . 2021; ():1.

Chicago/Turabian Style

Patrick Aravena Pelizari; Christian Geiß; Elisabeth Schoepfer; Torsten Riedlinger; Paula Aguirre; Hernán Santa María; Yvonne Merino Peña; Juan Camilo Gómez Zapata; Massimiliano Pittore; Hannes Taubenböck. 2021. "Street-Level Imagery and Deep Learning for Characterization of Exposed Buildings." , no. : 1.

Preprint content
Published: 04 March 2021
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Exposure describes elements which are imperiled by natural hazards and susceptible to damage. The affiliated vulnerability characterizes the likelihood to experience damage regarding a given level of hazard intensity. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) in risk assessment. Existing data sets and models often describe exposure in an aggregated manner, e.g., by relying on statistical/census data for given administrative entities. Nowadays, earth observation techniques allow to collect spatially continuous information for large geographic areas while enabling a high geometric and temporal resolution. In parallel, modern data interpretation tools based on Artificial Intelligence concepts enable the extraction of thematic information from such data with a high accuracy and detail. Consequently, we exploit measurements from the earth observation missions TanDEM-X and Sentinel-2, which collect data on a global scale, to characterize the built environment in terms of fundamental morphologic properties, namely built-up density and height. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we compare different methods for disaggregation and evaluate how different resolution properties of the earth observation data affect the risk assessment result. Results are presented for the city of Santiago de Chile, Chile, which is prone to natural hazards such as earthquakes. We present loss estimations and corresponding sensivity with respect to the resolution properties of the exposure data used in the model. Thereby, it can be noted how loss estimations vary substantially and that aggregated exposure information underestimates losses in our scenarios. As such, this study underlines the benefits of deploying modern earth observation technologies for refined exposure estimation and related loss estimation.

ACS Style

Christian Geiß; Patrick Aravena Pelizari; Peter Priesmeier; Angélica Rocio Soto Calderon; Elisabeth Schoepfer; Michael Langbein; Torsten Riedlinger; Hernán Santa María; Juan Camilo Gómez Zapata; Massimiliano Pittore; Hannes Taubenböck. Earth Observation Techniques for Spatial Disaggregation of Exposure Data . 2021, 1 .

AMA Style

Christian Geiß, Patrick Aravena Pelizari, Peter Priesmeier, Angélica Rocio Soto Calderon, Elisabeth Schoepfer, Michael Langbein, Torsten Riedlinger, Hernán Santa María, Juan Camilo Gómez Zapata, Massimiliano Pittore, Hannes Taubenböck. Earth Observation Techniques for Spatial Disaggregation of Exposure Data . . 2021; ():1.

Chicago/Turabian Style

Christian Geiß; Patrick Aravena Pelizari; Peter Priesmeier; Angélica Rocio Soto Calderon; Elisabeth Schoepfer; Michael Langbein; Torsten Riedlinger; Hernán Santa María; Juan Camilo Gómez Zapata; Massimiliano Pittore; Hannes Taubenböck. 2021. "Earth Observation Techniques for Spatial Disaggregation of Exposure Data ." , no. : 1.

Journal article
Published: 05 February 2021 in Sustainability
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The inhabitants of Latacunga living in the surrounding of the Cotopaxi volcano (Ecuador) are exposed to several hazards and related disasters. After the last 2015 volcanic eruption, it became evident once again how important it is for the exposed population to understand their own social, physical, and systemic vulnerability. Effective risk communication is essential before the occurrence of a volcanic crisis. This study integrates quantitative risk and semi-quantitative social risk perceptions, aiming for risk-informed communities. We present the use of the RIESGOS demonstrator for interactive exploration and visualisation of risk scenarios. The development of this demonstrator through an iterative process with the local experts and potential end-users increases both the quality of the technical tool as well as its practical applicability. Moreover, the community risk perception in a focused area was investigated through online and field surveys. Geo-located interviews are used to map the social perception of volcanic risk factors. Scenario-based outcomes from quantitative risk assessment obtained by the RIESGOS demonstrator are compared with the semi-quantitative risk perceptions. We have found that further efforts are required to provide the exposed communities with a better understanding of the concepts of hazard scenario and intensity.

ACS Style

Juan Gomez-Zapata; Cristhian Parrado; Theresa Frimberger; Fernando Barragán-Ochoa; Fabio Brill; Kerstin Büche; Michael Krautblatter; Michael Langbein; Massimiliano Pittore; Hugo Rosero-Velásquez; Elisabeth Schoepfer; Harald Spahn; Camilo Zapata-Tapia. Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador. Sustainability 2021, 13, 1714 .

AMA Style

Juan Gomez-Zapata, Cristhian Parrado, Theresa Frimberger, Fernando Barragán-Ochoa, Fabio Brill, Kerstin Büche, Michael Krautblatter, Michael Langbein, Massimiliano Pittore, Hugo Rosero-Velásquez, Elisabeth Schoepfer, Harald Spahn, Camilo Zapata-Tapia. Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador. Sustainability. 2021; 13 (4):1714.

Chicago/Turabian Style

Juan Gomez-Zapata; Cristhian Parrado; Theresa Frimberger; Fernando Barragán-Ochoa; Fabio Brill; Kerstin Büche; Michael Krautblatter; Michael Langbein; Massimiliano Pittore; Hugo Rosero-Velásquez; Elisabeth Schoepfer; Harald Spahn; Camilo Zapata-Tapia. 2021. "Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador." Sustainability 13, no. 4: 1714.

Preprint content
Published: 23 March 2020
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A significant percentage of disasters qualify as complex, multi-hazard events. Either when extreme events trigger additional phenomena (for instance in the case of particularly strong earthquakes generating tsunamis and landslides), or when different compounded hazards significantly amplify their joint impact (e.g., if an earthquake would occur during a typhoon). Further cascading effects can also occur due to systemic interdependency in the exposed infrastructure, for example water or power distribution lines. The quantitative estimation of the consequences associated to such multi-hazard scenarios is referred to as multi-risk estimation and can be relevant in supporting civil protection authorities and decision makers to plan medium and long-term disaster risk reduction (DRR) and prevention measures. 

Exploring the multi-risk associated to a complex event is challenging, partly due to the inherent model complexity, partly because is a strongly interdisciplinary matter, where skills and expertise from heterogeneous scientific and technical areas have to converge, and they rarely can be found in a single institutions nor managed by single-domain experts. In order to streamline this process, and at the same time unleash the potential of different institutions to bridge the gap between science and practice, an innovative conceptual and operational framework for multi-risk scenario assessment has been developed within the project RIESGOS (https://www.riesgos.de). The proposed solution is based on a dynamic, multi-hazard exposure and vulnerability model, which provides the geography-aware structural description of different types of assets (e.g. residential buildings) compatible with vulnerability models related to different hazards.  

A novel methodology for describing inter- and intra-hazard damage accumulation also allows the modelling of scenarios composed by sequences of hazardous events. The processing framework is based on processing modules that are implemented as distinct web-processing-services (WPS), possibly hosted remotely by different institutions. Each WPS is fully complying with the OGC WPS directives, and implemented in a flexible and scalable architecture based on Docker containers. The interoperability among the different services is ensured by a careful harmonization of input and output format and the use of on-the-fly converters. Standard and de-facto standards (e.g., community standards) are supported. Specific WPS provide the simulation of intensity maps for the considered hazards, either on the fly (e.g., for the earthquake shake-map generation) or by querying portfolios of pre-simulated events (e.g., for tsunami inundation maps). 

The proposed framework can be used to explore the direct damage and loss to assets as a result of a sequence of consecutive events, and also includes a specific processing module for the analysis and simulation of cascading effects on extended infrastructure such as power lines. A graph-based topological model of the network along with the physics-inspired modelling of the load- shedding allows the estimation of potential outages caused by non-linear cascading effects triggered by damage accumulation during the events sequence. 

The approach has been exemplified in several study areas in South America, considering a wide range of natural hazards including earthquakes, tsunamis and volcanic phenomena (lahar, ash-fall). The cases of Gran Valparaiso (Chile) and Cotopaxi region (Ecuador) are shown and discussed.  

ACS Style

Massimiliano Pittore; Juan Camilo Gómez Zapata; Nils Brinckmann; Graeme Weatherill; Andrey Babeyko; Sven Harig; Alireza Mahdavi; Benjamin Proß; Hugo Fernando Rosero Velasquez; Daniel Straub; Michael Krautblatter; Theresa Frimberger; Michael Langbein; Christian Geiß; Elisabeth Schoepfer. Towards an integrated framework for distributed, modular multi-risk scenario assessment. 2020, 1 .

AMA Style

Massimiliano Pittore, Juan Camilo Gómez Zapata, Nils Brinckmann, Graeme Weatherill, Andrey Babeyko, Sven Harig, Alireza Mahdavi, Benjamin Proß, Hugo Fernando Rosero Velasquez, Daniel Straub, Michael Krautblatter, Theresa Frimberger, Michael Langbein, Christian Geiß, Elisabeth Schoepfer. Towards an integrated framework for distributed, modular multi-risk scenario assessment. . 2020; ():1.

Chicago/Turabian Style

Massimiliano Pittore; Juan Camilo Gómez Zapata; Nils Brinckmann; Graeme Weatherill; Andrey Babeyko; Sven Harig; Alireza Mahdavi; Benjamin Proß; Hugo Fernando Rosero Velasquez; Daniel Straub; Michael Krautblatter; Theresa Frimberger; Michael Langbein; Christian Geiß; Elisabeth Schoepfer. 2020. "Towards an integrated framework for distributed, modular multi-risk scenario assessment." , no. : 1.

Preprint content
Published: 23 March 2020
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In order to assess the building portfolio composition for a particular natural hazard risk assessment application, it is necessary to classify the built environment into schemas containing building classes. The building classes should also address the attributes which may control their vulnerability towards the different hazards associated with their failure mechanisms, which along with their respective fragility functions are representative of a particular study area. In the case of volcanic risk, former efforts have been carried out in developing volcanic related fragility functions, this has been done mostly for European, Atlantic islands and South Asian building types (SEDIMER, MIA VITA, VOLDIES, EXPLORIS, SAFELAND projects). However, in other parts of the globe, particular construction practices, materials, and even occupancies may describe very diverse building types with different degrees of vulnerability which may or not be compatible with the existing schemas and fragility functions (Spence et al. 2005, Zuccaro et al. 2013, Mavrouli et al. 2013, Jenkins et al. 2014, Torres-Corredor et al. 2017).

As highlighted by Zuccaro et al. 2018, since in the case of volcanic active areas, the built environment will not only be exposed to a single hazard but to several compound or cascading hazards (e.g. tephra fall, pyroclastic flows, lahars), with different time intervals between them, a dynamic vulnerability with cumulated damage on the physical assets would be the baseline upon a multi-risk- volcanic framework should be described. In this similar context, single- hazard but still multi-state fragility functions have been very recently used in order to set up damage descriptions independently on the reference building schema. We propose to generalize this novel approach and further extend it in the volcanic risk assessment context. To do so, the very first step was to generate a multi-hazard- building- taxonomy containing a set of exhaustive mutually exclusive building attributes. Upon that framework, a probabilistic mapping across single- hazards- building- schemas and damage states has been achieved.

This methodological approach has been tested under the RIESGOS project over a selected study area of the Latin American Andes Region. In this region, cities close to active volcanos have been experienced a non-structured grow, which is translated into a significantly vulnerable population living in non- engineering buildings that are highly exposed to volcanic hazards. The Cotopaxi region in Ecuador has been chosen in order to explore the ash falls and lahars damage contributions with several scenarios in terms of volcanic explosivity index (VEI). Local lahars simulations have been obtained at different resolutions. Moreover, probabilistic ash- fall maps have been recently obtained after exhaustive ash fall and wind direction measurements. Lahar flow- velocity and ash- fall load pressure were respectively used as intensity measures. Furthermore, local and foreign building schemas that define the building exposure models have been constrained through ancillary data, cadastral information, and remote individual building inspections, to then been associated with a multi-state fragility function. These ingredients have been integrated into this novel methodological scenario-based- multi-risk- volcanic assessment.

ACS Style

Michael Langbein; Juan Camilo Gomez- Zapata; Theresa Frimberger; Nils Brinckmann; Roberto Torres- Corredor; Daniel Andrade; Camilo Zapata- Tapia; Massimiliano Pittore; Elisabeth Schoepfer. Scenario- based multi- risk assessment on exposed buildings to volcanic cascading hazards. 2020, 1 .

AMA Style

Michael Langbein, Juan Camilo Gomez- Zapata, Theresa Frimberger, Nils Brinckmann, Roberto Torres- Corredor, Daniel Andrade, Camilo Zapata- Tapia, Massimiliano Pittore, Elisabeth Schoepfer. Scenario- based multi- risk assessment on exposed buildings to volcanic cascading hazards. . 2020; ():1.

Chicago/Turabian Style

Michael Langbein; Juan Camilo Gomez- Zapata; Theresa Frimberger; Nils Brinckmann; Roberto Torres- Corredor; Daniel Andrade; Camilo Zapata- Tapia; Massimiliano Pittore; Elisabeth Schoepfer. 2020. "Scenario- based multi- risk assessment on exposed buildings to volcanic cascading hazards." , no. : 1.

Journal article
Published: 30 August 2018 in Remote Sensing
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Villarrica Volcano is one of the most active volcanoes in the South Andes Volcanic Zone. This article presents the results of a monitoring of the time before and after the 3 March 2015 eruption by analyzing nine satellite images acquired by the Technology Experiment Carrier-1 (TET-1), a small experimental German Aerospace Center (DLR) satellite. An atmospheric correction of the TET-1 data is presented, based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Database (GDEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor data with the shortest temporal baseline to the TET-1 acquisitions. Next, the temperature, area coverage, and radiant power of the detected thermal hotspots were derived at subpixel level and compared with observations derived from MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) data. Thermal anomalies were detected nine days before the eruption. After the decrease of the radiant power following the 3 March 2015 eruption, a stronger increase of the radiant power was observed on 25 April 2015. In addition, we show that the eruption-related ash coverage of the glacier at Villarrica Volcano could clearly be detected in TET-1 imagery. Landsat-8 imagery was analyzed for comparison. The information extracted from the TET-1 thermal data is thought be used in future to support and complement ground-based observations of active volcanoes.

ACS Style

Simon Plank; Michael Nolde; Rudolf Richter; Christian Fischer; Sandro Martinis; Torsten Riedlinger; Elisabeth Schoepfer; Doris Klein. Monitoring of the 2015 Villarrica Volcano Eruption by Means of DLR’s Experimental TET-1 Satellite. Remote Sensing 2018, 10, 1379 .

AMA Style

Simon Plank, Michael Nolde, Rudolf Richter, Christian Fischer, Sandro Martinis, Torsten Riedlinger, Elisabeth Schoepfer, Doris Klein. Monitoring of the 2015 Villarrica Volcano Eruption by Means of DLR’s Experimental TET-1 Satellite. Remote Sensing. 2018; 10 (9):1379.

Chicago/Turabian Style

Simon Plank; Michael Nolde; Rudolf Richter; Christian Fischer; Sandro Martinis; Torsten Riedlinger; Elisabeth Schoepfer; Doris Klein. 2018. "Monitoring of the 2015 Villarrica Volcano Eruption by Means of DLR’s Experimental TET-1 Satellite." Remote Sensing 10, no. 9: 1379.

Journal article
Published: 22 June 2016 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In this study object-based image analysis (OBIA) techniques were applied to assess land cover changes related to mineral extraction in a conflict-affected area of the eastern Democratic Republic of the Congo (DRC) over a period of five years based on very high resolution (VHR) satellite data of different sensors. Object-based approaches explicitly consider spatio-temporal aspects which allow extracting important information to document mining activities. The use of remote sensing data as an independent, up-to-date and reliable data source provided hints on the general development of the mining sector in relation to socio-economic and political decisions. While in early 2010, the situation was still characterised by an intensification of mineral extraction, a mining ban between autumn 2010 and spring 2011 marked the starting point for a continuous decrease of mining activities. The latter can be substantiated through a decrease in the extend of the mining area as well as of the number of dwellings in the nearby settlement. A following demilitarisation and the mentioned need for accountability with respect to the origin of certain minerals led to organised, more industrialized exploitation. This development is likewise visible on satellite imagery as typical clearings within forested areas. The results of the continuous monitoring in turn facilitate non-governmental organisations (NGOs) to further foster the mentioned establishment of responsible supply chains by the mining industry throughout the entire period of investigation.

ACS Style

Olaf Kranz; Elisabeth Schoepfer; Kristin Spröhnle; Stefan Lang. Object-based image analysis for the assessment of mineral extraction in conflict regions – a case study in the Democratic Republic of the Congo. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B7, 891 -896.

AMA Style

Olaf Kranz, Elisabeth Schoepfer, Kristin Spröhnle, Stefan Lang. Object-based image analysis for the assessment of mineral extraction in conflict regions – a case study in the Democratic Republic of the Congo. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B7 ():891-896.

Chicago/Turabian Style

Olaf Kranz; Elisabeth Schoepfer; Kristin Spröhnle; Stefan Lang. 2016. "Object-based image analysis for the assessment of mineral extraction in conflict regions – a case study in the Democratic Republic of the Congo." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7, no. : 891-896.

Original articles
Published: 07 June 2016 in Geocarto International
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The exploitation of resources, if not properly managed, can lead to spoiling natural habitats as well as to threatening people’s health, livelihoods and security. The paper discusses a multi-scale Earth observation-based approach to provide independent information related to exploitation activities of natural resources for countries which are experiencing armed conflict. The analyses are based on medium to very high spatial resolution optical satellite data. Object-based image analysis is used for information extraction at these different scales. On a subnational level, conflict-related land cover changes as an indication of potential hot spots for exploitation activities are classified. The regional assessment provides information about potential activity areas of resource exploitation, whereas on a local scale, a site-specific assessment of exploitation areas is performed. The study demonstrates the potential of remote sensing for supporting the monitoring and documentation of natural resource exploitation in conflict regions.

ACS Style

Elisabeth Schoepfer; Kristin Spröhnle; Olaf Kranz; Xavier Blaes; Jan Kolomaznik; Filip Hilgert; Tomas Bartalos; Thomas Kemper. Towards a multi-scale approach for an Earth observation-based assessment of natural resource exploitation in conflict regions. Geocarto International 2016, 32, 1139 -1158.

AMA Style

Elisabeth Schoepfer, Kristin Spröhnle, Olaf Kranz, Xavier Blaes, Jan Kolomaznik, Filip Hilgert, Tomas Bartalos, Thomas Kemper. Towards a multi-scale approach for an Earth observation-based assessment of natural resource exploitation in conflict regions. Geocarto International. 2016; 32 (10):1139-1158.

Chicago/Turabian Style

Elisabeth Schoepfer; Kristin Spröhnle; Olaf Kranz; Xavier Blaes; Jan Kolomaznik; Filip Hilgert; Tomas Bartalos; Thomas Kemper. 2016. "Towards a multi-scale approach for an Earth observation-based assessment of natural resource exploitation in conflict regions." Geocarto International 32, no. 10: 1139-1158.

Book chapter
Published: 30 January 2015 in Applied Geoinformatics for Sustainable Integrated Land and Water Resources Management (ILWRM) in the Brahmaputra River basin
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Land use/land cover (LULC) information is one of the most important spatial input for environmental modelling and a crucial indicator to identify and quantify natural and socioeconomic impacts triggered by LULC changes. Such impacts are related to glacier, snow cover, and permafrost melting, the forming of GLOFs, erosion by land sides, discharge and sediment transport dynamics of alpine rivers, and the socioeconomic regional urban and rural development to name some of them.

ACS Style

Rajesh Thapa; Stefan Lang; Elisabeth Schoepfer; Stefan Kienberger; Petra Füreder; Peter Zeil. Land Use/Land Cover Classification of the Natural Environment. Applied Geoinformatics for Sustainable Integrated Land and Water Resources Management (ILWRM) in the Brahmaputra River basin 2015, 17 -23.

AMA Style

Rajesh Thapa, Stefan Lang, Elisabeth Schoepfer, Stefan Kienberger, Petra Füreder, Peter Zeil. Land Use/Land Cover Classification of the Natural Environment. Applied Geoinformatics for Sustainable Integrated Land and Water Resources Management (ILWRM) in the Brahmaputra River basin. 2015; ():17-23.

Chicago/Turabian Style

Rajesh Thapa; Stefan Lang; Elisabeth Schoepfer; Stefan Kienberger; Petra Füreder; Peter Zeil. 2015. "Land Use/Land Cover Classification of the Natural Environment." Applied Geoinformatics for Sustainable Integrated Land and Water Resources Management (ILWRM) in the Brahmaputra River basin , no. : 17-23.

Journal article
Published: 02 December 2014 in Remote Sensing
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In developing countries, there is a high correlation between the dependence of oil exports and violent conflicts. Furthermore, even in countries which experienced a peaceful development of their oil industry, land use and environmental issues occur. Therefore, independent monitoring of oil field infrastructure may support problem solving. Earth observation data enables fast monitoring of large areas which allows comparing the real amount of land used by the oil exploitation and the companies’ contractual obligations. The target feature of this monitoring is the infrastructure of the oil exploitation, oil well pads—rectangular features of bare land covering an area of approximately 50–60 m × 100 m. This article presents an automated feature extraction procedure based on the combination of a pixel-based unsupervised classification of polarimetric synthetic aperture radar data (PolSAR) and an object-based post-classification. The method is developed and tested using dual-polarimetric TerraSAR-X imagery acquired over the Doba basin in south Chad. The advantages of PolSAR are independence of the cloud coverage (vs. optical imagery) and the possibility of detailed land use classification (vs. single-pol SAR). The PolSAR classification uses the polarimetric Wishart probability density function based on the anisotropy/entropy/alpha decomposition. The object-based post-classification refinement, based on properties of the feature targets such as shape and area, increases the user’s accuracy of the methodology by an order of a magnitude. The final achieved user’s and producer’s accuracy is 59%–71% in each case (area based accuracy assessment). Considering only the numbers of correctly/falsely detected oil well pads, the user’s and producer’s accuracies increase to even 74%–89%. In an iterative training procedure the best suited polarimetric speckle filter and processing parameters of the developed feature extraction procedure are determined. The high transferability of the methodology is proved by an application to a second SAR acquisition.

ACS Style

Simon Plank; Alexander Mager; Elisabeth Schoepfer. Monitoring of Oil Exploitation Infrastructure by Combining Unsupervised Pixel-Based Classification of Polarimetric SAR and Object-Based Image Analysis. Remote Sensing 2014, 6, 11977 -12004.

AMA Style

Simon Plank, Alexander Mager, Elisabeth Schoepfer. Monitoring of Oil Exploitation Infrastructure by Combining Unsupervised Pixel-Based Classification of Polarimetric SAR and Object-Based Image Analysis. Remote Sensing. 2014; 6 (12):11977-12004.

Chicago/Turabian Style

Simon Plank; Alexander Mager; Elisabeth Schoepfer. 2014. "Monitoring of Oil Exploitation Infrastructure by Combining Unsupervised Pixel-Based Classification of Polarimetric SAR and Object-Based Image Analysis." Remote Sensing 6, no. 12: 11977-12004.

Journal article
Published: 28 November 2014 in Remote Sensing
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Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA). Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

ACS Style

Christoff Fourie; Elisabeth Schoepfer. Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation. Remote Sensing 2014, 6, 11852 -11882.

AMA Style

Christoff Fourie, Elisabeth Schoepfer. Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation. Remote Sensing. 2014; 6 (12):11852-11882.

Chicago/Turabian Style

Christoff Fourie; Elisabeth Schoepfer. 2014. "Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation." Remote Sensing 6, no. 12: 11852-11882.

Journal article
Published: 29 September 2014 in Remote Sensing
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For effective management of refugee camps or camps for internally displaced persons (IDPs) relief organizations need up-to-date information on the camp situation. In cases where detailed field assessments are not available, Earth observation (EO) data can provide important information to get a better overview about the general situation on the ground. In this study, different approaches for dwelling detection were tested using the example of a highly complex camp site in Somalia. On the basis of GeoEye-1 imagery, semi-automatic object-based and manual image analysis approaches were applied, compared and evaluated regarding their analysis results (absolute numbers, population estimation, spatial pattern), statistical correlations and production time. Although even the results of the visual image interpretation vary considerably between the interpreters, there is a similar pattern resulting from all methods, which shows same tendencies for dense and sparse populated areas. The statistical analyses revealed that all approaches have problems in the more complex areas, whereas there is a higher variance in manual interpretations with increasing complexity. The application of advanced rule sets in an object-based environment allowed a more consistent feature extraction in the area under investigation that can be obtained at a fraction of the time compared to visual image interpretation if large areas have to be observed.

ACS Style

Kristin Spröhnle; Dirk Tiede; Elisabeth Schoepfer; Petra Füreder; Anna Svanberg; Torbjörn Rost. Earth Observation-Based Dwelling Detection Approaches in a Highly Complex Refugee Camp Environment — A Comparative Study. Remote Sensing 2014, 6, 9277 -9297.

AMA Style

Kristin Spröhnle, Dirk Tiede, Elisabeth Schoepfer, Petra Füreder, Anna Svanberg, Torbjörn Rost. Earth Observation-Based Dwelling Detection Approaches in a Highly Complex Refugee Camp Environment — A Comparative Study. Remote Sensing. 2014; 6 (10):9277-9297.

Chicago/Turabian Style

Kristin Spröhnle; Dirk Tiede; Elisabeth Schoepfer; Petra Füreder; Anna Svanberg; Torbjörn Rost. 2014. "Earth Observation-Based Dwelling Detection Approaches in a Highly Complex Refugee Camp Environment — A Comparative Study." Remote Sensing 6, no. 10: 9277-9297.

Journal article
Published: 21 July 2014 in Remote Sensing
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Earth observation is an important source of information in areas that are too remote, too insecure or even both for traditional field surveys. A multi-scale analysis approach is developed to monitor the Kivu provinces in the Democratic Republic of the Congo (DRC) to identify hot spots of mining activities and provide reliable information about the situation in and around two selected mining sites, Mumba-Bibatama and Bisie. The first is the test case for the approach and the detection of unknown mining sites, whereas the second acts as reference case since it is the largest and most well-known location for cassiterite extraction in eastern Congo. Thus it plays a key-role within the context of the conflicts in this region. Detailed multi-temporal analyses of very high-resolution (VHR) satellite data demonstrates the capabilities of Geographic Object-Based Image Analysis (GEOBIA) techniques for providing information about the situation during a mining ban announced by the Congolese President between September 2010 and March 2011. Although the opening of new surface patches can serve as an indication for activities in the area, the pure change between the two satellite images does not in itself produce confirming evidence. However, in combination with observations on the ground, it becomes evident that mining activities continued in Bisie during the ban, even though the production volume went down considerably.

ACS Style

Fritjof Luethje; Olaf Kranz; Elisabeth Schoepfer. Geographic Object-Based Image Analysis Using Optical Satellite Imagery and GIS Data for the Detection of Mining Sites in the Democratic Republic of the Congo. Remote Sensing 2014, 6, 6636 -6661.

AMA Style

Fritjof Luethje, Olaf Kranz, Elisabeth Schoepfer. Geographic Object-Based Image Analysis Using Optical Satellite Imagery and GIS Data for the Detection of Mining Sites in the Democratic Republic of the Congo. Remote Sensing. 2014; 6 (7):6636-6661.

Chicago/Turabian Style

Fritjof Luethje; Olaf Kranz; Elisabeth Schoepfer. 2014. "Geographic Object-Based Image Analysis Using Optical Satellite Imagery and GIS Data for the Detection of Mining Sites in the Democratic Republic of the Congo." Remote Sensing 6, no. 7: 6636-6661.

Journal article
Published: 28 April 2014 in Remote Sensing
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Sample supervised image analysis, in particular sample supervised segment generation, shows promise as a methodological avenue applicable within Geographic Object-Based Image Analysis (GEOBIA). Segmentation is acknowledged as a constituent component within typically expansive image analysis processes. A general extension to the basic formulation of an empirical discrepancy measure directed segmentation algorithm parameter tuning approach is proposed. An expanded search landscape is defined, consisting not only of the segmentation algorithm parameters, but also of low-level, parameterized image processing functions. Such higher dimensional search landscapes potentially allow for achieving better segmentation accuracies. The proposed method is tested with a range of low-level image transformation functions and two segmentation algorithms. The general effectiveness of such an approach is demonstrated compared to a variant only optimising segmentation algorithm parameters. Further, it is shown that the resultant search landscapes obtained from combining mid- and low-level image processing parameter domains, in our problem contexts, are sufficiently complex to warrant the use of population based stochastic search methods. Interdependencies of these two parameter domains are also demonstrated, necessitating simultaneous optimization.

ACS Style

Christoff Fourie; Elisabeth Schoepfer. Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation. Remote Sensing 2014, 6, 3791 -3821.

AMA Style

Christoff Fourie, Elisabeth Schoepfer. Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation. Remote Sensing. 2014; 6 (5):3791-3821.

Chicago/Turabian Style

Christoff Fourie; Elisabeth Schoepfer. 2014. "Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation." Remote Sensing 6, no. 5: 3791-3821.

Book chapter
Published: 08 May 2010 in Remote Sensing and Digital Image Processing
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This chapter focuses on segmentation of remotely sensed image data and object-based image analysis. It discusses the differences between pixel-based and object-based image analysis; the potential of the object-based approach; and, the application of eCognition software for performing image segmentation and classification at different levels of detail.

ACS Style

Elisabeth Schöpfer; Stefan Lang; Josef Strobl. Segmentation and Object-Based Image Analysis. Remote Sensing and Digital Image Processing 2010, 181 -192.

AMA Style

Elisabeth Schöpfer, Stefan Lang, Josef Strobl. Segmentation and Object-Based Image Analysis. Remote Sensing and Digital Image Processing. 2010; ():181-192.

Chicago/Turabian Style

Elisabeth Schöpfer; Stefan Lang; Josef Strobl. 2010. "Segmentation and Object-Based Image Analysis." Remote Sensing and Digital Image Processing , no. : 181-192.

Articles
Published: 17 March 2009 in Geocarto International
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Recent technical advances in remote sensing data capture and spatial resolution lead to a widening gap between increasing data availability on the one hand and insufficient methodology for semi-automated image data processing and interpretation on the other hand. At the interface of GIS and remote sensing, object-based image analysis methodologies are one possible approach to close this gap. With this, methods from either side are integrated to use both the capabilities of information extraction from image data and the power to perform spatial analysis on derived polygon data. However, dealing with image objects from various sources and in different scales implies combining data with inconsistent boundaries. A landscape interpretation support tool (LIST) is introduced which seeks to investigate and quantify spatial relationships among image objects stemming from different sources by using the concept of spatial coincidence. Moreover, considering different categories of object fate, LIST enables a change categorization for each polygon of a time series of classifications. The application of LIST is illustrated by two case-studies, using Landsat TM and ETM as well as CIR aerial photographs: the first showing how the tool is used to perform object quantification and change analysis; the latter demonstrating how superior aggregation capabilities of the human brain can be combined with the fine spatial segmentation and classification. Possible fields of application are identified and limitations of the approach are discussed.

ACS Style

Stefan Lang; Elisabeth Schopfer; Tobias Langanke. Combined object-based classification and manual interpretation–synergies for a quantitative assessment of parcels and biotopes. Geocarto International 2009, 24, 99 -114.

AMA Style

Stefan Lang, Elisabeth Schopfer, Tobias Langanke. Combined object-based classification and manual interpretation–synergies for a quantitative assessment of parcels and biotopes. Geocarto International. 2009; 24 (2):99-114.

Chicago/Turabian Style

Stefan Lang; Elisabeth Schopfer; Tobias Langanke. 2009. "Combined object-based classification and manual interpretation–synergies for a quantitative assessment of parcels and biotopes." Geocarto International 24, no. 2: 99-114.

Book chapter
Published: 23 October 2007 in Green Defense Technology
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Sustainable urban planning in growing urban agglomerations encompasses the active development of urban green spaces. The loss of urban green not only threatens urban climate and ecosystems, but may also affect a city’s image and the residential satisfaction in general. Quantifiable information about green structures and the amount and distribution of green spaces is essential for sustainable planning. Monitoring tools for outlining differences in urban green space are required, which – more than merely measuring the overall percentage of green – may reflect the different importance of green areas in specific environments. This implies both spatial explicit characterizations of green areas and the consideration of relative importance of certain green structures from a citizen’s perspective. In this chapter we will present two approaches for advanced urban green mapping in the Phoenix Metropolitan Area, Arizona, USA (PHX-US), and Salzburg, Austria, Europe (SBG-AT). The approaches discussed were designed for monitoring urban green development in a repeatable and transferable manner by using (1) proxies derived from remotely sensed data, (2) spatial concepts and spatially explicit measures for spatial characterization, and (3) subjective, social science data reflecting the perception of urban green and therefore, the quality of some aspects of urban life and residential satisfaction.

ACS Style

Stefan Lang; Elisabeth Schopfer; Daniel Hölbling; Thomas Blaschke; Matthias Moeller; Thomas Jekel; Elisabeth Kloyber. Quantifying and Qualifying Urban Green by Integrating Remote Sensing, GIS, and Social Science Method. Green Defense Technology 2007, 93 -105.

AMA Style

Stefan Lang, Elisabeth Schopfer, Daniel Hölbling, Thomas Blaschke, Matthias Moeller, Thomas Jekel, Elisabeth Kloyber. Quantifying and Qualifying Urban Green by Integrating Remote Sensing, GIS, and Social Science Method. Green Defense Technology. 2007; ():93-105.

Chicago/Turabian Style

Stefan Lang; Elisabeth Schopfer; Daniel Hölbling; Thomas Blaschke; Matthias Moeller; Thomas Jekel; Elisabeth Kloyber. 2007. "Quantifying and Qualifying Urban Green by Integrating Remote Sensing, GIS, and Social Science Method." Green Defense Technology , no. : 93-105.