Arzu Erener

Performance evaluation of building detection and digital surface model extraction algorithms: Outcomes of the PRRS 2008 Algorithm Performance Contest

This paper presents the initial results of the algorithm performance contest that was organized as part of the 5th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2008). The focus of the 2008 contest was automatic building detection and digital surface model (DSM) extraction. A QuickBird data set with manual ground truth was used for building detection evaluation, and a stereo Ikonos data set with a highly accurate reference DSM was used for DSM extraction evaluation. Nine submissions were received for the building detection task, and three submissions were received for the DSM extraction task. We provide an overview of the data sets, the summaries of the methods used for the submissions, the details of the evaluation criteria, and the results of the initial evaluation.

An approach for detection of buildings and changes in buildings using orthophotos and point clouds: A case study of Van Erriş earthquake

Abstract This paper presents an image analysis of the Van Erciş earthquake, and demonstrates how efficiently the orthophoto images and point clouds from stereo matching data can be used for automatic detection of buildings and changes in buildings. The proposed method contains three basic steps. The first step is to classify the high-resolution pre and post event Red- Green-Blue (RGB) orthophoto images (orthoRGB) using Support Vector Machine (SVM) classification procedure to extract the building areas. In the second step, normalized Digital Surface Model (nDSM) band derived from point clouds and Digital Terrain Model (DTM) is integrated with the SVM classification (nDSM+orthoRGB). In the last step, building damage assessment is performed through a comparison between two independent classification results from pre-and post-event data. It was observed that using the nDSM band in the classification process as additional bands the accuracy of classification increases significantly.

Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1s and 0s to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of 1s and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the models performance. The shortest computation time is achieved by the LR for all sampling strategies.LR-PRS, FLR-PRS and BLR-Whole Data set-ups, yield the best fits with respect to COD values.PRS has a better overall performance when compared to the other adopted sampling strategies.Avoidance of spatial correlation in the data set is critical for the QLSM models performance.

GIS Based Urban Area Spatiotemporal Change Evaluation Using Landsat and Night Time Temporal Satellite Data

AbstractnThis study aims at determining the spatiotemporal change in urban areas by using multi-temporal satellite images with geographic information systems integration. In this study, the city of Erzincan was selected as the sample case. The analyses of change were conducted by using the optical satellite images from LANSAT TM dated 1987 and the LANDSAT ETM+ dated 2006, besides the night images from 1998, 2006 and 2010. Spatial change maps were created for the qualitative analysis, and change matrixes were formed for the quantitative assessment of these changes. The outcomes of these change analyses were then evaluated and interpreted in the light of the demographics of the population living in the area. The results obtained from the Landsat satellite images indicate that the area of the city expanded at the annual average rate of 1.65% in 1987–2006. Night images indicate that the city area grew at an annual average rate of 4.04% in 1998–2006, while this rate was 20.28% in the period of 2006–2010. The results of the study demonstrate that the usability and contribution of satellite images is quite significant in tracking and monitoring temporal and spatial change in the area.

Examining Urbanization Dynamics in Turkey Using DMSP–OLS and Socio-Economic Data

AbstractnThe present study tried to determine the spatial expansion of urban areas in all the cities in Turkey and to examine the relationship between this spatial expansion with the related demographic, employment, educational, industrial and other indicators using the Geographical Information Systems. The present study was made up of three parts. In the first part, the urban areas in Turkey were determined using the pixel-based image classification methods. In the second part, the development levels of the cities, one of socio-economic indicators, were determined using the Principle Components Analysis. In the last part, statistical analyses were conducted to examine the relationship between the development levels of cities and urban areas. The results obtained revealed that the cities with larger urban area were more in the western part of the country and while those with less urban area were in the eastern part of the country. A similar distribution was also true for the socio-economic development order. The Pearson correlation coefficient of 0.711 between these variables demonstrated that there was a linear positive correlation in between. According to the results of Moran’s I spatial auto-correlation analysis, the distribution of both urban area and socio-economic development throughout the country had a relationship with place. According to geographically weighted regression analysis, the demographic, education and health indicators had the biggest influence on urban area.

Barajların Çevresel Etkilerinin Zamansal Ve Mekansal Olarak Uzaktan Algılama İle Değerlendirilmesi: Atatürk Barajı Örneği

Barajlar ulkenin enerji uretiminin en dogal ve en ucuz yoludur. Barajlar insa etmek, ucuz enerji uretimi, rekrasyon olanaklarini artirmasi, tarimsal arazilerin sulanmasi, sehir sebekeleri icin gerekli olan suyu saglamasi ve taskin kontrolu acisindan buyuk onem tasirlar. Bununla birlikte, alansal olarak cok buyuk barajlar, havadaki nem oranini artirarak bulundugu bolgenin iklimini ve ekolojik dengesini degistirmektedirler. Bu calismada Ataturk Baraj golunun 1992 ve 2016 yillari arasinda bolge uzerindeki cevresel etkileri uydu goruntuleri ve uzaktan algilama teknikleri kullanilarak ortaya konulmaya calisilmistir. xa0Calismada 1992, 1998, 2006 ve 2016 yillarina ait Landsat 4 ,5 TM ve Landsat 8 OLI -TIRS uydular ina ait goruntuler kullanilmistir. Calisma alanina ait bitki alanlari, topraga gore ayarlanmis bitki ortusu indeksi (SAVI) ve yuzey nemliligi normallestirilmis nem fark indeksi (NDMI) kullanilarak , yillara ait yuzey sicaklik dagilimlari (YSD) ise Landsat 4, 5 TM, ve Landsat 8 OLI-TIRS uydularinin termal kizil otesi (TIR) bandlari kullanilarak elde edilmistir. 1992, 1998, 2006 ve 2016 yillarinda meydana gelen bitki alanlari, yuzey nemliligi ve yuzey sicaklik dagilimlari arasindaki iliski rastlantisal olarak secilmis 500 noktada esit aralikli ve oran olcekte Pearson korelasyon katsayisi kullanilarak test edilmistir.