Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetation

• URL: http://arxiv.org/abs/2008.00229v1
• Date: Sat, 1 Aug 2020 09:58:22 GMT
• Title: Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetation
• Authors: Yusuke Kumakoshi, Sau Yee Chan, Hideki Koizumi, Xiaojiang Li and Yuji Yoshimura
• Abstract summary: This study proposes an improved indicator of greenery visibility for analytical use (standardized GVI; sGVI) It is shown that the sGVI, a weighted form of GVI aggregated to an area, mitigates the bias of densely located measurement sites. Also, by comparing sGVI and NDVI at city block level, we found that sGVI captures the presence of vegetation better in the city center, whereas NDVI is better in capturing vegetation in parks and forests.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Urban greenery is considered an important factor in relation to sustainable development and people's quality of life in the city. Although ways to measure urban greenery have been proposed, the characteristics of each metric have not been fully established, rendering previous researches vulnerable to changes in greenery metrics. To make estimation more robust, this study aims to (1) propose an improved indicator of greenery visibility for analytical use (standardized GVI; sGVI), and (2) quantify the relation between sGVI and other greenery metrics. Analyzing a data set for Yokohama city, Japan, it is shown that the sGVI, a weighted form of GVI aggregated to an area, mitigates the bias of densely located measurement sites. Also, by comparing sGVI and NDVI at city block level, we found that sGVI captures the presence of vegetation better in the city center, whereas NDVI is better in capturing vegetation in parks and forests. These tools provide a foundation for accessing the effect of vegetation in urban landscapes in a more robust matter, enabling comparison on any arbitrary geographical scale.

Related papers

• Coverage and Bias of Street View Imagery in Mapping the Urban Environment [0.0]
  Street View Imagery (SVI) has emerged as a valuable data form in urban studies, enabling new ways to map and sense urban environments. This research proposes a novel workflow to estimate SVI's feature-level coverage on urban environment. Using London as a case study, three experiments are conducted to identify potential biases in SVI's ability to cover and represent urban features.
  arXiv  Detail & Related papers  (2024-09-22T02:58:43Z)
• VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting [58.12667617617306]
  We propose VegeDiff for the geospatial vegetation forecasting task. VegeDiff is the first to employ a diffusion model to probabilistically capture the uncertainties in vegetation change processes. By capturing the uncertainties in vegetation changes and modeling the complex influence of relevant variables, VegeDiff outperforms existing deterministic methods.
  arXiv  Detail & Related papers  (2024-07-17T14:15:52Z)
• CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series [12.621355888239359]
  Urban transformations have profound societal impact on both individuals and communities at large. We propose an end-to-end change detection model to effectively capture physical alterations in the built environment at scale. Our approach has the potential to supplement existing dataset and serve as a fine-grained and accurate assessment of urban change.
  arXiv  Detail & Related papers  (2024-01-02T08:57:09Z)
• On the need to move from a single indicator to a multi-dimensional framework to measure accessibility to urban green [0.0]
  We investigate the extent to which the use of a single metric provides a reliable assessment of green accessibility in a city. The results suggest that, due to the complex interaction between the spatial distribution of greenspaces in an urban center and its population distribution, the use of a single indicator might lead to insufficient discrimination.
  arXiv  Detail & Related papers  (2023-08-10T12:37:13Z)
• A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection [68.84486900183853]
  We build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area.
  arXiv  Detail & Related papers  (2022-11-26T18:17:39Z)
• Urban form and COVID-19 cases and deaths in Greater London: an urban morphometric approach [63.29165619502806]
  The COVID-19 pandemic generated a considerable debate in relation to urban density. This is an old debate, originated in mid 19th century's England with the emergence of public health and urban planning disciplines. We describe urban form at individual building level and then aggregate information for official neighbourhoods.
  arXiv  Detail & Related papers  (2022-10-16T10:01:10Z)
• Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT) [0.0]
  We propose heterogeneous urban graph attention network (HUGAT) for learning the representations of urban regions. In our experiments on NYC data, HUGAT outperformed all the state-of-the-art models.
  arXiv  Detail & Related papers  (2022-02-18T04:59:20Z)
• SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds [52.624157840253204]
  We introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km2. Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset.
  arXiv  Detail & Related papers  (2022-01-12T14:48:11Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)
• Diversity and density of urban functions in station areas [0.0]
  This paper offers empirical evidence on the association between the diversity and density of urban functions in the Tokyo Metropolitan Area. It was found that highly dense station areas tended to display low diversity at multiple scales. This paper argues for considering both diversity and density in urban planning to make station areas vibrant and resilient.
  arXiv  Detail & Related papers  (2021-06-23T00:51:44Z)
• Entropy as a measure of attractiveness and socioeconomic complexity in Rio de Janeiro metropolitan area [52.77024349608834]
  We use a mobile phone dataset and an entropy-based metric to measure the attractiveness of a location. The results show that the attractiveness of a given location measured by entropy is an important descriptor of the socioeconomic status of the location.
  arXiv  Detail & Related papers  (2020-03-23T15:58:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.