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Number of studies covering major data aspects of OpenStreetMap (OSM) for developed cities and countries are available in scientific literature. However, this is not the case for developing ones mainly because of low data availability in OSM. This study presents a time-series spatial analysis of Turkey OSM dataset, a developing country, between the year 2007 and 2015 to understand how the dataset has developed with time and space. Five different socio-economic factors of the region are tested to find their relationship, if any, with dataset growth. An east-west spatial trend in data density is observed within the country. Population Density and Literacy Level of the region are found be the factors controlling it. It has also been observed that the street network of the region has followed the Exploration and Densification evolutionary model. High participation inequality is found within the OSM mappers, with only 5 of them responsible for the country’s 50% geo-data upload. Furthermore, it is found that these mappers use other Volunteered Geographic Information (VGI) and government open-dataset to feed into OSM. This study is believed to bring some high level insights of OSM for a developing country which would be useful for geographers, open-data policy makers, VGI projects planners and data-curators to structure and deploy similar future projects.

Compared to natural images in artificial datasets, it is more challenging to improve the spatial resolution of remote sensing optical image data using super-resolution techniques. Historical aerial images are primarily grayscale due to single-band acquisition, which further limits their recoverability. To avoid data limitations, it is advised to employ a data collection consisting of images with homogeneously distributed intensity values of land use/cover objects at various resolution values. Thus, two different datasets were created. In line with the proposed approach, images of bare land, farmland, residential areas, and forested regions were extracted from orthophotos of different years with different spatial resolutions. In addition, images with intensity values in a more limited range for the same categories were obtained from a single year's orthophoto to highlight the contribution of the suggested approach. Training of two different datasets was performed independently using a deep learning-based super-resolution model, and the same test images were enhanced individually with the weights of both models. The results were assessed using a variety of quality metrics in addition to visual interpretation. The findings indicate that the suggested dataset structure and content can enable the recovery of more details and effectively remove the smoothing effect. In addition, the trend of the metric values matches the visual perception results. [ABSTRACT FROM AUTHOR]

Precise approaches for monitoring and modeling air quality based on advanced spatial technologies for understanding the three-dimensional (3D) spread of the air quality in an indoor environment are essential. This research aims to assess and 3D visualization of the indoor air quality and explore the distribution pattern of culturable airborne bacteria (CAB), Staphylococcus spp., and culturable airborne fungi (CAF) concentrations in two identical lecture theatres (LTs) before and after usage of them under natural ventilation. To achieve these assessments and visualizations of 3D distributions in different layers of the LTs, 3D models were created using point cloud data from a terrestrial laser scanner (TLS). A 3D geostatistical study based on a Geographic Information System (GIS) was realized to demonstrate the 3D distribution in LTs utilizing measurements taken at 15 sampling points in each LT. 3D models were displayed using geostatistical tools in GIS software to visualize the distributions. The statistical distributions were also represented graphically. Results showed that the distributions of CAB, Staphylococcus spp., and CAF concentrations varied between 1- 460 CFU/m³, 1- 420 CFU/m³ and 1-980 CFU/m³ respectively. The conclusions of this research might be used for future studies. [ABSTRACT FROM AUTHOR]

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Forests that are dynamic ecosystems often change due to continuous growth, expansion, and natural and human-induced external influences. Thus, an accurate and up-to-date inventory of forests is one of the most important features of establishing a forest management policy. The most appropriate methods should be chosen after a detailed investigation for eliminating the complexity of the ecosystem, developing forest management, and shaping the environmental policy. In this study, photogrammetry and Light Detection and Ranging (LIDAR) data were evaluated both separately and together to obtain the most appropriate results related to forest inventories. Digital Elevation Models (DEM), Digital Terrain Models (DTM), Digital Surface Models (DSM), Normalized Digital Surface Models (NDSM), and Canopy Height Models (CHM) were produced using point cloud data obtained separately from each technique. Then, DEMs were produced through the classification of point clouds by means of 4 different methods in the determined study area. Although LIDAR data is more expensive and laborious than the photogrammetric method, it is the most successful method to produce forest inventory by producing more accurate results. In the first model, the full inventory of the forest could not have been realized. In the second method, better performance was realized than the first method, but even worse results were obtained than the fourth method. Unexpectedly several counted trees were obtained in the third method than the second one. The main reason for the differences obtained with various methods was evaluated as the 5 years differences between obtaining LIDAR and photogrammetric data. [ABSTRACT FROM AUTHOR]

Due to the complex structure, design and type of material of the roads, it is difficult to perform with the models trained with the data of other countries in deep learning studies based on road extraction. Especially in Turkey, deep learning based existing data sets for the modelling are not suitable due to complex road structures. Thus, local data sets that represent the features of the region must be used to be able to achieve promising results. In this study, a labelled road data set for Istanbul was created by using high-resolution orthorectified aerial images which cover a wide variety of urban and rural areas. In this context, used imageries were obtained from the Istanbul Great Municipality. In the study, Mapbox and QGIS were employed as open source tools. Apart from the road data were eliminated via Mapbox and obtained global road data was overlaid orthorectified images in QGIS. After this process overlaid images were exported as tile files and labelled data was produced combining these tile files. The generated labelled data set was tested using U-NET Deep learning-based convolutional neural network architecture. As a result, 96 % training accuracy and 89.6 % validation accuracy have been achieved. [ABSTRACT FROM AUTHOR]