Mapping Urban Green Spaces at the Metropolitan Level Using Very High Resolution Satellite Imagery and Deep Learning Techniques for Semantic Segmentation

Autor:	Adrián L. Ferriño Fierro, Victor Hugo Guerra Cobián, Fabiola D. Yépez, Adriana Vargas-Martínez, D. F. Lozano-Garcia, Ricardo A. Cavazos González, Roberto E. Huerta, Héctor de León Gómez
Rok vydání:	2021
Předmět:	010504 meteorology & atmospheric sciences Standardization Computer science Science 0211 other engineering and technologies 02 engineering and technology computer.software_genre 01 natural sciences Convolutional neural network urban vegetation Sørensen–Dice coefficient Satellite imagery Segmentation 021101 geological & geomatics engineering 0105 earth and related environmental sciences sustainable development Artificial neural network neural networks urban open spaces Monterrey Metropolitan Area business.industry Deep learning Metropolitan area General Earth and Planetary Sciences Data mining Artificial intelligence business computer
Zdroj:	Remote Sensing, Vol 13, Iss 2031, p 2031 (2021) Remote Sensing; Volume 13; Issue 11; Pages: 2031
ISSN:	2072-4292
DOI:	10.3390/rs13112031
Popis:	Urban green spaces (UGSs) provide essential environmental services for the well-being of ecosystems and society. Due to the constant environmental, social, and economic transformations of cities, UGSs pose new challenges for management, particularly in fast-growing metropolitan areas. With technological advancement and the evolution of deep learning, it is possible to optimize the acquisition of UGS inventories through the detection of geometric patterns present in satellite imagery. This research evaluates two deep learning model techniques for semantic segmentation of UGS polygons with the use of different convolutional neural network encoders on the U-Net architecture and very high resolution (VHR) imagery to obtain updated information on UGS polygons at the metropolitan area level. The best model yielded a Dice coefficient of 0.57, IoU of 0.75, recall of 0.80, and kappa coefficient of 0.94 with an overall accuracy of 0.97, which reflects a reliable performance of the network in detecting patterns that make up the varied geometry of UGSs. A complete database of UGS polygons was quantified and categorized by types with location and delimited by municipality, allowing for the standardization of the information at the metropolitan level, which will be useful for comparative analysis with a homogenized and updated database. This is of particular interest to urban planners and UGS decision-makers.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e09239ceebc6ff70b4d99594c70739db https://doi.org/10.3390/rs13112031 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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