In this study, the post-earthquake aerial photographs were digitally processed and analysed to detect collapsed buildings caused by the Izmit, Turkey earthquake of 17 August 1999. The selected area of study encloses part of the city of Golcuk, which is one of the urban areas most strongly hit by the earthquake. The analysis relies on the idea that if a building is collapsed, then it will not have corresponding shadows. The boundaries of the buildings were available and stored in a Geographical Information System (GIS) as vector polygons. The vector building polygons were used to match the shadow casting edges of the buildings with their corresponding shadows and to perform analyses in a building-specific manner. The shadow edges of the buildings were detected through a Prewitt edge detection algorithm. For each building, the agreement was then measured between the shadow producing edges of the building polygons and the thresholded edge image based on the percentage of shadow edge pixels. If the computed percentage value was below a preset threshold then the building being assessed was declared as collapsed. Of the 80 collapsed buildings, 74 were detected correctly, providing 92.50% producer's accuracy. The overall accuracy was computed as 96.15%. The results show that the detection of the collapsed buildings through digital analysis of post-earthquake aerial photographs based on shadow information is quite encouraging. It is also demonstrated that determining the optimum threshold value for separating the collapsed from uncollapsed buildings is important.

Digital elevation models (DEM) are the indispensable quantitative environmental variable in most of the research studies in remote sensing. The improvement of sensor and satellite imaging technologies enabled the researchers to generate DEM using remotely sensed data. These data can be started to use as not only the two-dimensional (2-D) but also three-dimensional (3-D) information sources with usage of the DEM. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is one of the sensor systems capable of DEM generation and during the study, ASTER level 1A (L1A) data were used. Due to presence of many geological features and different landcover types, the test site is selected as the watershed of Asarsuyu River, located in north-western Anatolia in between Duzce and Bolu plains. The aim of this study is to check the best effort of 15 m spatial resolution DEM generation from ASTER L1A data by collecting different numbers of ground control points (GCP) (30, 45, and 60) and tie points (TP). During the study, three different techniques—spatial correlation, image differencing and profiling—were used for both planimetric and vertical accuracy assessment. The obtained results from both of the techniques show that the accuracy of the DEM increases by increasing the number of GCP. However, there is an only slight difference between the result of 45 GCPs and 60 GCPs.