Generative adversarial networks (GANs) are a type of neural network that are characterized by their unique construction and training process. Utilizing the concept of the latent space and exploiting ...the results of a duel between different GAN components opens up interesting opportunities for computer vision (CV) activities, such as image inpainting, style transfer, or even generative art. GANs have great potential to support aerial and satellite image interpretation activities. Carefully crafting a GAN and applying it to a high-quality dataset can result in nontrivial feature enrichment. In this study, we have designed and tested an unsupervised procedure capable of engineering new features by shifting real orthophotos into the GAN’s underlying latent space. Latent vectors are a low-dimensional representation of the orthophoto patches that hold information about the strength, occurrence, and interaction between spatial features discovered during the network training. Latent vectors were combined with geographical coordinates to bind them to their original location in the orthophoto. In consequence, it was possible to describe the whole research area as a set of latent vectors and perform further spatial analysis not on RGB images but on their lower-dimensional representation. To accomplish this goal, a modified version of the big bidirectional generative adversarial network (BigBiGAN) has been trained on a fine-tailored orthophoto imagery dataset covering the area of the Pilica River region in Poland. Trained models, precisely the generator and encoder, have been utilized during the processes of model quality assurance and feature engineering, respectively. Quality assurance was performed by measuring model reconstruction capabilities and by manually verifying artificial images produced by the generator. The feature engineering use case, on the other hand, has been presented in a real research scenario that involved splitting the orthophoto into a set of patches, encoding the patch set into the GAN latent space, grouping similar patches latent codes by utilizing hierarchical clustering, and producing a segmentation map of the orthophoto.
Generative adversarial networks (GAN) opened new possibilities for image processing and analysis. Inpainting, dataset augmentation using artificial samples, or increasing spatial resolution of aerial ...imagery are only a few notable examples of utilising GANs in remote sensing (RS). The normalised difference vegetation index (NDVI) ground-truth labels were prepared by combining RGB and NIR orthophotos. The dataset was then utilised as input for a conditional generative adversarial network (cGAN) to perform an image-to-image translation. The main goal of the neural network was to generate an artificial NDVI image for each processed 256 px × 256 px patch using only information available in the panchromatic input. The network achieved a structural similarity index measure (SSIM) of 0.7569 ± 0.1083, a peak signal-to-noise ratio (PSNR) of 26.6459 ± 3.6577 and a root-mean-square error (RSME) of 0.0504 ± 0.0193 on the test set, which should be considered high. The perceptual evaluation was performed to verify the method's usability when working with a real-life scenario. The research confirms that the structure and texture of the panchromatic aerial RS image contain sufficient information for NDVI estimation for various objects of urban space. Even though these results can highlight areas rich in vegetation and distinguish them from the urban background, there is still room for improvement regarding the accuracy of the estimated values. The research aims to explore the possibility of utilising GAN to enhance panchromatic images (PAN) with information related to vegetation. This opens exciting opportunities for historical RS imagery processing and analysis.
Small cities are an important part of the settlement system, a link between rural areas and large cities. Although they perform important functions, research focuses on large cities and metropolises ...while marginalizing small cities, the study of which is of great importance to progress in social sciences, geography, and urban planning. The main goal of this paper was to verify the impact of selected socio-economic factors on the share of built-up areas in 665 small Polish cities in 2019. Data from the Database of Topographic Objects (BDOT), Sentinel-2 satellite imagery from 2015 and 2019, and Local Data Bank by Statistics Poland form 2019 were used in the research. A machine learning segmentation procedure was used to obtain the data on the occurrence of built-up areas. Hot Spot (Getis-Ord Gi*) analysis and geographically weighted regression (GWR) was applied to explain spatially varying impact of factors related to population, spatial and economic development, and living standards on the share of built-up areas in the area of small cities. Significant association was found between the population density and the share of built-up areas in the area of the cities studied. The influence of the other socio-economic factors examined, related to the spatial and economic development of the cities and the quality of life of the inhabitants, showed great regional variation. The results also indicated that the share of built-up areas in the area of the cities under study is a result of the conditions under which they were established and developed throughout their existence, and not only of the socio-economic factors affecting them at present.
This paper investigates the social, demographic, and economic factors determining differences between forest identification based on remote sensing techniques and land registry. The Database of ...Topographic Objects and Sentinel-2 satellite imagery data from 2018 were used to train a forest detection supervised machine learning model. Results aggregated to communes (NUTS-5 units) were compared to data from land registry delivered in Local Data Bank by Statistics Poland. The differences identified between above mentioned sources were defined as errors of land registry. Then, geographically weighted regression was applied to explain spatially varying impact of investigated errors’ determinants: Urbanization processes, civic society development, education, land ownership, and culture and quality of spatial planning. The research area covers the entirety of Poland. It was confirmed that in less developed areas, local development policy stimulating urbanization processes does not respect land use planning principles, including the accuracy of land registry. A high education level of the society leads to protective measures before the further increase of the investigated forest cover’s overestimation of the land registry in substantially urbanized areas. Finally, higher coverage by valid local spatial development plans stimulate protection against forest classification errors in the land registry.
The wide availability of multispectral satellite imagery through projects such as Landsat and Sentinel, combined with the introduction of deep learning in general and Convolutional Neural Networks ...(CNNs) in particular, has allowed for the rapid and effective analysis of multiple classes of problems pertaining to land coverage. Taking advantage of the two phenomena, we propose a machine learning model for the classification of land abandonment. We designed a Convolutional Neural Network architecture that outputs a classification probability for the presence of land abandonment in a given 15–25 ha grid element by using multispectral imaging data obtained through Sentinel Hub. For both the training and validation of the model, we used imagery of the Łódź Voivodeship in central Poland. The main source of truth was a 2009 orthophoto study available from the WMS (Web Map Service) of the Geoportal site. The model achieved 0.855 auc (area under curve), 0.47 loss, and 0.78 accuracy for the test dataset. Using the classification results and the Getis–Ord Gi* statistic, we prepared a map of cold- and hotspots with individual areas that exceed 50 km2. This thresholded heatmap allowed for an analysis of contributing factors for both low and intense land abandonment, demonstrating that common trends are identifiable through the interpretation of the classification results of the chosen model. We additionally performed a comparative field study on two selected cold- and hotspots. The study, along with the high-accuracy results of the model’s validation, confirms that CNN-type models are an effective tool for the automatic detection of land abandonment.
Purpose. Presentation of authors’ own method based on ICTs for the description of a tourism space’s attractiveness.
Method. Using ICTs (application Geosearch Integration Engine was developed during ...the research process) for collecting geographical data on multimedia content published in the Internet and using exploratory methods of spatial analysis and spatial analysis of point data for the description of atourism space’s attractiveness . The study was conducted in July 2016 for the metropolitan area of Kraków based on the multimedia content of Wikimedia Commons.
Findings. The presented method created by the authors’ makes the description and assessment of the attractiveness of a tourism space more complex. Perception of the geographical space by tourists (as a population, not particular people) is enabled. This makes the presented method valuable for both the tourism industry and regional policy. The study showed the significant potential of using ICTs and GIS tools for collecting and processing the data on tourists’ perception and behaviour in tourism space was indicated as the study was conducted.
Research and conclusion limitations. The Geosearch Integration Engine does not allow for the selection of photographs published only by tourists. Thus, all the multimedia and photo files in Wikimedia Commons were investigated based on an assumption of universal nature regarding the attractiveness of a tourism space. Therefore, development of this application is strongly recommended.
Practical implications. Using ICTs for the measurement of tourists’ perception of a tourism space’s attractiveness should enable development of business analytics for tourism enterprises and increase effectiveness of tourism destination management.
Originality. Elimination of the disadvantages of the methods used so far to describe the attractiveness of tourism spaces. The investigated methods based hitherto on static measurement of supply-side elements of tourism spaces (attractions, infrastructure, accessibility). On the other hand, demand-side methods based on imperfect public statistics on the number of tourists hosted in tourist accommodation establishments.
Type of paper. The article presents findings from empirical research.
The aim of this paper is to describe geographic differences in the prices of accommodation services offered in the Łódź Metropolitan Area. In this paper, the authors justify the need for their ...research on a metropolitan area scale, as opposed to research in which the study area is limited to the central city of a metropolis. The authors present a methodology for collecting empirical material and the area of research; however, the focus is only on the issue of the identification of the boundaries of the Łódź Metropolitan Area. Discussing the results of the study, the authors characterize in detail the location of accommodation facilities in the Łódź Metropolitan Area and identify geographic differences in the prices of accommodation services offered by the surveyed companies.
The main objective of the study is deep characteristics of tourism development expenditure disclosed in the budgets of local government units (hereinafter: LGUs) in Łódź voivodeship and in the ...Norwegian county of Oppland. The implementation of the above-mentioned goal required prior characterization and comparison of the construction principles of budgets in Poland and Norway. It was also necessary to determine the place of spending on tourism in the structure of Polish and Norwegian LGU budgets. By comparing the financing of tourism development by LGUs of Łódź voivodeship and Norwegian Oppland, the authors have encountered the issue of the various structures of local government budgets, including the different treatment of expenditure on the development of tourism in the budgets of local governments. These problems have been widely discussed in the first part of this work. The rest of the study is devoted to the discussion of expenditure on the development of tourism in the municipalities and communes of Łódź voivodeship. They were compared with the expenditures of Oppland County and the expenses of the municipalities included in the commune.
RNAComposer is a fully automated, web-interfaced system for RNA 3D structure prediction, freely available at http://rnacomposer.cs.put.poznan.pl/ and http://rnacomposer.ibch.poznan.pl/. Its main ...components are: manually curated database of RNA 3D structure elements, highly efficient computational engine and user-friendly web application. In this paper, we demonstrate how the latest additions to the system allow the user to significantly affect the process of 3D model composition on several computational levels. Although in general our method is based on the knowledge of secondary structure topology, currently the RNAComposer offers a choice of six incorporated programs for secondary structure prediction. It also allows to apply a conditional search in the database of 3D structure elements and introduce user-provided elements into the final 3D model. This new functionality contributes to a significant improvement of the predicted 3D model reliability and it facilitates a better model adjustment to the experimental data. This is exemplified based on the RNAComposer application for modelling of the 3D structures of precursors of the miR160 family members.
PDF
