The location of natural gas emergency response team centers (NGERTCs) is critical in terms of addressing natural gas notifications that require a timely emergency response. The selection of NGERTCs in Istanbul has an important place in terms of providing better service, due to the necessity of responding to emergency natural gas notifications within 15 min, in addition to the over 200,000 natural gas notifications per year and heavy traffic conditions. Therefore, this study proposes a solution based on GIS and FAHP to determine suitable NGERTC locations in Istanbul Metropolitan Area. In the first stage of the study, the required 15-min coverage areas for emergency calls for 36 existing NGERTCs in Istanbul were extracted and the adequacy of their locations was analyzed. In the second stage of the study, the weights of seven criteria determined for new NGERTC site selection were calculated by the FAHP method. With spatial analysis made, 12 new NGERTC locations were proposed. Finally, re-coverage analysis was performed for proposed and existing NGERTCs, and changes in coverage area within a 15 min response time were analyzed. Natural gas network coverage increased from 70.04% to 83.86%, and natural gas subscriber coverage increased from 91.03% to 96.27%. The results show that GIS and FAHP are worth using in selecting suitable NGERTC locations.

The Sustainable Development Goals (SDGs) have addressed environmental and social issues in cities, such as insecure land tenure, climate change, and vulnerability to natural disasters. SDGs have motivated authorities to adopt urban land policies that support the quality and safety of urban life. Reliable, accurate, and up-to-date building information should be provided to develop effective land policies to solve the challenges of urbanization. Creating comprehensive and effective systems for land management in urban areas requires a significant long-term effort. However, some procedures should be undertaken immediately to mitigate the potential negative impacts of urban problems on human life. In developing countries, public records may not reflect the current status of buildings. Thus, implementing an automated and rapid building monitoring system using the potential of high-spatial-resolution satellite images and street views may be ideal for urban areas. This study proposed a two-step automated building stock monitoring mechanism. Our proposed method can identify critical building features, such as the building footprint and the number of floors. In the first step, buildings were automatically detected by using the object-based image analysis (OBIA) method on high-resolution spatial satellite images. In the second step, vertical images of the buildings were collected. Then, the number of the building floors was determined automatically using Google Street View Images (GSVI) via the YOLOv5 algorithm and the kernel density estimation method. The first step of the experiment was applied to the high-resolution images of the Pleiades satellite, which covers three different urban areas in Istanbul. The average accuracy metrics of the OBIA experiment for Area 1, Area 2, and Area 3 were 92.74%, 92.23%, and 92.92%, respectively. The second step of the experiment was applied to the image dataset containing the GSVIs of several buildings in different Istanbul streets. The perspective effect, the presence of more than one building in the photograph, some obstacles around the buildings, and different window sizes caused errors in the floor estimations. For this reason, the operator’s manual interpretation when obtaining SVIs increases the floor estimation accuracy. The proposed algorithm estimates the number of floors at a rate of 79.2% accuracy for the SVIs collected by operator interpretation. Consequently, our methodology can easily be used to monitor and document the critical features of the existing buildings. This approach can support an immediate emergency action plan to reduce the possible losses caused by urban problems. In addition, this method can be utilized to analyze the previous conditions after damage or losses occur.

Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre-fire risk assessments to inform mitigation efforts. This study aimed to investigate the relationship between predictions from pre-fire risk assessments and outcomes observed through post-fire burn severity analyses. In this study, forest fire risk was assessed through the Fuzzy Analytical Hierarchy Process (FAHP), in which fire-oriented factors were used as input. The degree of burn was determined by the Random Forest method using 11,519 training points and 400 test points on Sentinel-2 satellite images under three different classes. According to the results obtained from 266 selected test points located within the forest, all primary factors put forth increased high burn severity. Climate, in particular, emerged as the most significant factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, accounting for 67%. This was followed by topography with 50% accuracy at a high fire intensity. In the risk assessment based on the FAHP method, climate was assigned the highest weight value among the other factors (32.2%), followed by topography (27%). To evaluate the results more comprehensively, both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in the Case 1 area, moderate burn severity had the highest in the Case 2 area. During the days of the fire, the direction of spreading was obtained from the MODIS images. In this way, the fire severity was also interpreted depending on the direction of fire progression. Through an analysis of various case studies and literature, this research underlines both the inherent strengths and limitations of predicting forest fire behavior-based pre-fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.

Buildings that have cultural and historical significance are very important elements of our living spaces and they must be protected by public authorities. However, the preservation of these buildings prevents their landowners from using their development rights and causes economic losses. If those losses are not compensated, it results in social injustice. Leaving this problem unsolved makes historic buildings a target for arson, facilitates the destruction of unregistered (unlisted) historic buildings for redevelopment with a higher density. The traditional methods, namely expropriation and property exchange, generally fail to compensate those development losses. However, the method of transfer of development rights (TDR) can be a good option for this task in Turkey as well. Although there have been some important legislative actions, an “implementing regulation” could not be enacted so far. In this study, we attempt to develop a step-by-step methodology for the implementation of a TDR program from the perspective of land management. The methodology introduced in this paper is also tested for its validity by interviewing 18 professionals from related sectors and all the interviewees agreed on them with minor suggestions. The results derived from this study could benefit not only property owners, but also overall preservation efforts in the country.

Vessel detection from remote sensing images is becoming exponentially crucial component in marine surveillance applications including maritime traffic control, anti-illegal fishing applications, oil discharge control, marine pollution and safety. Applying deep learning methods to vessel detection applications ineluctably improve the detection results and overcome unforeseen errors that could be made by analysts. Publicly available datasets play vital role for development and evaluation process of deep learning models. In this paper, open source DOTA dataset has been revised and trained with single-staged deep learning methods. The results show that YOLOv8 model has the most efficient value on detecting ships and fastest to detect instances from given test images on inference.

Sayısal Yükseklik Modeli (SYM) yeryüzünün fiziksel yapısını üç boyutlu olarak temsil etmek amacıyla kullanılan matematiksel bir modeldir. SYM üretimi için kullanılacak verilerin toplanma yöntemleri, çalışma alanını büyüklüğüne ve üretilecek yüzey modelin kullanım amacına göre değişebilmektedir. Son yıllarda uzaktan algılama teknikleri ile yüzey verilerinin toplanması sağladıkları zaman ve maliyet avantajları nedeniyle tercih edilmeye başlamıştır. Özellikle ulaşılması zor bölgeler ve yüzölçümü bakımından büyük alanlar söz konusu olduğunda 3B konum verilerinin uzaktan algılama teknikleri ile belirlenmesi tercih edilmektedir. Hava LiDAR (Light Detection and Ranging) tekniği kırsal, kentsel ve ormanlık alanlarda 3B konum verisini üretmede hızlı ve güvenilir bir uzaktan algılama teknolojisidir. Ayrıca klasik fotogrametrik yöntemlerle karşılaştırıldığında veri toplama aşamasında hava durumuna, mevsimlere ve zamana daha az bağımlıdır. Dahası, yoğun ormanlarla kaplı yüzeylerde bitki örtüsüne nüfuz ederek zemin noktalarına ulaşmada ve dolayısıyla SYM oluşturmada diğer yöntemlere göre daha iyi sonuçlar alınabilmektedir. Bu çalışmada LiDAR verileri kullanılarak bir sayısal yüzey modeli oluşturulmuş ve yüzey üzerinde bulunan zemin dışı objelerin filtreleme işlemi gerçekleştirilmiştir. Farklı filtreleme algoritmaları kullanılarak yapılan bu işlem sonucunda, elde edilen SYM’nin düşey doğruluğu referans olarak kabul edilen bir veri seti ile kıyaslanmış ve sonuçlar istatistik olarak değerlendirilmiştir. Elde edilen sonuçlar, filtreleme algoritmalarının üretilen SYM doğruluğunu etkilediğini göstermektedir. Ayrıca filtreleme yöntemlerinin ani yükseklik değişimi olan bölgelere duyarlı olduğu ve bu bölgelerde filtreleme kaynaklı hata büyüklüklerinin arttığı gözlemlenmiştir.