Motives: According to public statistics guidelines, areas officially classified in Lodz city as urban greenery include only forests, parks, lawns, squares and cemeteries. Areas of so-called unsealed greenery are omitted, which, however, have a great positive impact on improving the living conditions of the population. By taking information from satellite images and comparing them with official data, we have received a closer to the reality picture of the city, which is much more better than it would appear from official statistical data. Another dimension which the study addresses is the uneven distribution of greenery of a certain quality in individual units of the city. Aim: Comparing these data with the fact that the distribution of places of residence is also uneven, an attempt was made to assess the accessibility of green areas for the inhabitants of Lodz city. Results: The results show that there are much more green spaces, similar in terms of vegetation abundance to the official green spaces. That means the city is underestimated when talking about the degree of greenery.
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 utilizing GANs in remote sensing. This is due to a unique construction and training process expressed as a duel between GAN components. The main objective of the research is to apply GAN to generate an artificial Normalized Difference Vegetation Index (NDVI) using panchromatic images. The NDVI ground-truth labels were prepared by combining RGB and NIR orthophoto. The dataset was then utilized 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 256px × 256px patch using only information available in the panchromatic input. The network achieved 0.7569 ± 0.1083 Structural Similarity Index Measure (SSIM), 26.6459 ± 3.6577 Peak Signal-to-Noise Ratio (PNSR) and 0.0504 ± 0.0193 Root-Mean-Square Error (RSME) on the test set. The perceptual evaluation was performed to verify the usability of the method when working with a real-life scenario. The research confirms that the structure and texture of the panchromatic aerial remote sensing image contains sufficient information for NDVI estimation for various objects of urban space. Even though these results can be used to highlight areas rich in vegetation and distinguish them from urban background, there is still room for improvement in terms of accuracy of estimated values. The purpose of the research is to explore the possibility of utilizing GAN to enhance panchromatic images (PAN) with information related to vegetation. This opens interesting possibilities in terms of historical remote sensing imagery processing and analysis. The panchromatic orthoimagery dataset was derived from RGB orthoimagery.
The article is devoted to the subject of urban greenery. The paper attempts to present real green areas and not only those that have such a purpose featuring in lists and registers. The authors also refer to the topic of availability of urban greenery for the residents of the city, taking into account an uneven density of places of residence. The aim of the article is to present the method for assessing the availability of green areas around places of residence, using spatial data showing residential buildings and official data on greenery. The relevant analyses are based on a regular network of squares of 90 m × 90 m. It was found that Łódź is a city with rich greenery resources. However, this judgment needs revising because a significant part of the residents both in their places of residence (R = 50 m) and further surroundings (R = 500 m) do not have access to green areas intended for recreation.
Generative adversarial networks (GAN) opened new possibilities for image processing and analysis. Inpainting, dataset augmentation using artificial samples or increasing resolution of aerial imagery are only a few notable examples of utilizing GANs in remote sensing. This is due to a unique construction and training process expressed as a duel between GAN components. In the following research, GAN has been applied to enhance panchromatic images with Normalized Difference Vegetation Index (NDVI). Panchromatic orthoimagery dataset with NDVI ground-truth labels was prepared by combining RGB and NIR orthophoto. The dataset was then utilized 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 channel for each processed 256px × 256px patch using only information available in the panchromatic imagery. The network achieved 0.9869 ± 0.0099 SSIM, 47.1635643 ± 4.0527963 PNSR and 0.0048023 ± 0.0018756 RSME on the test set. Perceptual evaluation was performed to verify the usability of the method when working with a real-life scenario. The research confirms that the structure and texture of the panchromatic aerial remote sensing image contains sufficient information for NDVI estimation for various objects of urban space. Even though these results can be used to highlight areas rich in vegetation and distinguish them from urban background, there is still room for improvement in terms of accuracy of estimated values.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.