The collection of solid waste is a challenging issue, especially in highly urbanized areas. In developing countries, landfilling is currently the preferred method for disposing of solid waste, but each landfill has a limited lifecycle. Therefore, changes in the amount of stored waste should be monitored for the sustainable management of such areas. In this study, volumetric changes in a landfill were examined using a low-cost unmanned aerial vehicle (UAV). Aerial photographs obtained from five different flights, covering approximately two years, were used in the volume calculations. Values representing the amount of remaining space between the solid waste and a reference plane were determined using digital elevation models, which were produced based on the structure from motion (SfM) approach. The obtained results and potential of UAVs in the photogrammetric survey of a landfill were further evaluated and interpreted by considering other possible techniques, ongoing progress, and the information existing in an environmental impact assessment report. As a result of the study, it was proved that SfM carried out using a low-cost UAV has a high potential for use in the reconstruction of a landfill. Outcomes were obtained over a short period, without the need for direct contact with the solid waste, making the UAV preferable for use in planning and decision-making studies.

Obtaining the shoreline of a water body and spatial changes on it provide valuable information regarding the fact that freshwater resources constitute a small fraction of available water resources in the world. Nowadays, various types of image are used to extract shoreline details with the contribution of near infrared response (NIR) band. In this study, the potential utility of light-detection-and-ranging LIDAR-derived intensity image (LDII) as an infrared band in shoreline extraction was investigated. Study area was Kestel Dam operated in Izmir province of Turkey. Orthophoto, multispectral Pléiades image (PI), and LDII were processed and evaluated to obtain the shoreline of the dam lake. Mean-shift segmentation was applied on the LDII as smoothing to maintain the edge details while eliminating noise. Noise-free LDII was then added to red, green and blue (RGB) bands of PI instead of NIR band to obtain layer stacked image. Two shorelines were extracted from these two imageries by means of rule-based object-oriented classification. Implemented rules were directly based on threshold values of normalized difference water indexes derived from imageries. Areal-based change detection analysis was carried out with reference to the occupancy rates at the minimum and maximum operating volumes of the dam lake. Also, change detection analysis based on minimum distance differences between extracted and digitized shorelines was performed to examine the subpixel values. Both analyses proved that LDII created from point cloud produced by a beam of 1064 nm can be used as an infrared band in object-based shoreline extraction and may provide better distinction between water and nonwater objects.

Deep learning-based super-resolution (SR) is an effective state-of-the-art technique for enhancing low-resolution images. This study explains a hierarchical dataset structure within the scope of enhancing grayscale historical aerial photographs with a basic SR model and relates it to non-reference image quality metric. The dataset was structured based on the hierarchy of photo interpretation elements. Images of bare land and forestry areas were evaluated as the primary category containing tone and color elements, images of residential areas as the secondary category containing shape and size elements, and images of farmland areas as the tertiary category containing pattern elements. Instead of training all images in all categories at once, which is the issue that any SR model with low number of parameters has difficulty handling, each category was trained separately. Test images containing the features of each category were enhanced separately, which means three enhanced images for one test image. The obtained images were divided into equal parts of 5 × 5 pixel size, and the final image was created by concatenating those that were determined to be of higher quality based on the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) metric values. Subsequently, comparative analyses based on visual interpretation and reference-based image quality metrics proved that the approach to the dataset structure positively impacted the results.

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.