H. Sebnem Duzgun

A Conceptual List of Indicators for Urban Planning and Management Based on Earth Observation

Sustainable development is a key component in urban studies. Earth Observation (EO) can play a valuable role in sustainable urban development and planning, since it represents a powerful data source with the potential to provide a number of relevant urban sustainability indicators. To this end, in this paper we propose a conceptual list of EO-based indicators capable of supporting urban planning and management. Three cities with different typologies, namely Basel, Switzerland; Tel Aviv, Israel; and Tyumen, Russia were selected as case studies. The EO-based indicators are defined to effectively record the physical properties of the urban environment in a diverse range of environmental sectors such as energy efficiency, air pollution and public health, water, transportation and vulnerability to hazards. The results assess the potential of EO to support the development of a set of urban environmental indicators towards sustainable urban planning and management.

A GIS-based software for lifeline reliability analysis under seismic hazard

Lifelines are vital networks, and it is important that those networks are still functional after major natural disasters such as earthquakes. Assessing reliability of lifelines requires spatial analysis of lifelines with respect to a given earthquake hazard map. In this paper, a GIS-based software for the spatial assessment of lifeline reliability which is developed by using GeoTools environment is presented. The developed GIS-based software imports seismic hazard and lifeline network layers and then creates a gridded network structure. Finally, it adopts a network reliability algorithm to calculate the upper and lower bounds for system reliability of the lifeline under seismic hazard. The software enables user visualizing the reliability values in graphical form as well as thematic lifeline reliability map with colors indicating reliability level along with the link and the overall network. It also provides functions for saving the analysis results in shape file format. The software is tested and validated for an application taken from literature which is a part of water distribution system of Bursa in Turkey. The developed GIS-based software module that creates GIS-based reliability map of the lifelines under seismic hazard is user friendly, modifiable, fast in execution time, illustrative and validated for the existing literature studies.

Towards EO-based sustainable urban planning and management

The increasing availability of Earth Observation (EO) technologies has provided new opportunities for a wide range of urban applications, such as mapping and monitoring of the urban environment, socio-economic estimations, characterization of urban climate, analysis of regional and global impacts and urban security and emergency preparedness. However, a gap exists between the research-focused results offered by the urban EO community and the application of these data and products by urban planners and decision makers. Hence, the main objective of the GEOURBAN (ExploitinG Earth Observation in sUstainable uRBan plAnning & maNagement) project is to bridge this gap by demonstrating the ability of current and future EO systems to depict parameters of urban structure and urban environmental quality at detailed level. This paper introduces the conceptual approach towards the integration of EO-based geo-information into urban and spatial planning and presents and discusses first results of the indicators developed and evaluated by using remote sensing data.

A Framework for Defining a 3D Model in Support of Risk Management

A conceptual framework is proposed that defines the most appropriate 3D visualisations for different types of natural disasters in urban environments. Based on the disaster type, the needed level of detail for a 3D model is derived, which is then linked to the time needed to process the data and obtain this level of detail. The levels of detail are compliant with the 3D international standard CityGML. The framework is designed to serve risk managers and to help them make a better selection of 3D model representations to perform their tasks. After a brief introduction on the relations between types of disasters, data needed to manage the disasters and different users involved in the risk management process, the chapter elaborates on the parameters according to which types of hazards are classified. The framework is demonstrated for an earthquake case in Eskisehir, Turkey. The paper concludes with a discussion of the advantages and disadvantages of the given framework, as well as an outline of future research.

GIS-based stochastic modeling of physical accessibility using GPS-based floating car data and Monte Carlo simulation

The term physical accessibility has long been used by geographers, economists, and urban planners and reflects the relative ease of access to/from several urban/rural services by considering the traveling costs. Numerous accessibility measures, ranging from simple to sophisticated, can be observed in the geographical information systems (GIS)-based accessibility modeling literature. However, these measures are generally calculated from a constant catchment boundary (a most likely or average catchment boundary) based on constant deterministic transportation costs. This is one of the fundamental shortcomings of the current GIS-based accessibility modeling and creates uncertainty about the accuracy and reliability of the accessibility measures, especially when highly variable speeds in road segments are considered. The development of a new stochastic approach by using global positioning system (GPS)-based floating car data and Monte Carlo simulation (MCS) technique could enable handling the variations in transportation costs in a probabilistic manner and help to consider all possible catchment boundaries, instead of one average catchment boundary, in accessibility modeling process. Therefore, this article proposes a stochastic methodology for GIS-based accessibility modeling by using GPS-based floating car data and MCS technique. The proposed methodology is illustrated with a case study on medical emergency service accessibility in Eskisehir, Turkey. Moreover, deterministic and stochastic accessibility models are compared to demonstrate the differences between the models. The proposed model could provide better decision support for the decision-makers who are supposed to deal with accessibility, location/allocation, and service/catchment area related issues.

Integration of stress testing with graph theory to assess the resilience of urban road networks under seismic hazards

Transportation networks daily provide accessibility and crucial services to societies. However, they must also maintain an acceptable level of service to critical infrastructures in the case of disruptions, especially during natural disasters. We have developed a method for assessing the resilience of transportation network topology when exposed to environmental hazards. This approach integrates graph theory with stress testing methodology and involves five basic steps: (1) establishment of a scenario set that covers a range of seismic damage potential in the network, (2) assessment of resilience using various graph-based metrics, (3) topology-based simulations, (4) evaluation of changes in graph-based metrics, and (5) examination of resilience in terms of spatial distribution of critical nodes and the entire network topology. Our case study was from the city of Kathmandu in Nepal, where the earthquake on April 25, 2015, followed by a major aftershock on May 12, 2015, led to numerous casualties and caused significant damage. Therefore, it is a good example for demonstrating and validating the developed methodology. The results presented here indicate that the proposed approach is quite efficient and accurate in assisting stakeholders when evaluating the resilience of transportation networks based on their topology.

Circular target detection algorithm on satellite images based on radial transformation

Remote sensing is used in a spreading manner by many governmental and industrial institutions worldwide in recent years. Target detection has an important place among the applications developed using satellite imagery. In this paper, an original circular target detection algorithm has been proposed based on a radial transformation. The algorithm consists of three stages such as pre-processing, target detection, and postprocessing. In the pre-processing stage, bilateral noise reduction filtering and vegetation detection operations are completed which they are required by target detection step. The target detection stage finds the circular target by a radial transformation algorithm and variables obtained from the training, and postprocessing stage carries out the elimination of falsely detected targets by utilizing the vegetation information. The Petroleum Oil Lubricants (POL) depots in the industrial areas and harbors have been chosen as an application area of the proposed algorithm. The algorithm has been trained and tested on a data set which includes 4-band images with Near-Infrared band. Proposed algorithm is able to detect many circular targets with different types and sizes as a consequence of using a full radial transformation search as well as it gives rewarding results on industrial areas and harbors in the experiments conducted.

Urban area and building detection on high resolution multispectral satellite images using spatial statistics

With the increase in the resolution and the amount of satellite images, automatic extraction of urban areas and buildings became more important in the past decade. Extracting such information manually is tedious and needs a lot of expert effort. In this work, a system for detecting the urban areas, then finding the buildings inside these areas is proposed. LISA analysis is used for detection of urban areas. After the urban area is detected, a mean-shift based segmentation is applied; then each segment is decided as building or not by using segment-test on spectral features and Local Morans I value. Classification of buildings is done by KNN (K-Nearest Neighbor) classifier and Parzen classifiers. Input images to be used are 3 band multispectral images.

A local extrema based method on 2D brightness temperature maps for detection of archaeological artifacts

Archaeological studies using computer vision based analysis methods on thermal imageries mainly lack an important stage of pointwise detection of artifact positions, which is needed for the automation of the system in a generic application. In this paper, we propose a pointwise detection method working in the thermal range of hyperspectral band for archaeological artifacts. The proposed method first optimally converts a given 3D hyperspectral image of the searched scene into a 2D brightness-temperature map by minimizing the mean square error (MSE) between the spectral radiance of a pixel and the Planck curves generated at different temperatures. The local maxima and minima are then found on the resulting 2D map as the candidate points. Finally, a score assignment is performed on the candidate points by using their temperature difference with respect to their neighborhood. The results on the thermal images taken from a test scene have indicated a good correlation between the extrema points and artifact positions.

Signature based vegetation detection on hyperspectral images

In this study, the contribution of utilizing hyperspectral unmixing algorithms on signature based target detection algorithms is studied. Spectral Angle Mapper (SAM), Spectral Matched Filter (SMF) and Adaptive Cosine Estimator (ACE) algorithms are selected as target detection methods and the performance change related to the target spectral acquisition is evaluated. The spectral signature of the desired target, corn, is acquired from ASD hyperspectral library as well as from the hypespectral unmixing endmembers with a minimum angular distance to ASD signature. It is seen that the performance of the corn detection has increased significantly with the utilization of the closest endmember extracted from the hyperspectral data cube. Among all methods, SAM has been designated as the most successful method based on the Receiver Operating Characteristics (ROC) curves.