Evaluating the role of geospatial artificial intelligence in monitoring spatiotemporal changes in green areas in Heliopolis district during the period 2015-2024 | ||||
| International Journal of Sustainable Development and Science | ||||
| Volume 7, Issue 1, 2024, Page 298-314 PDF (2.44 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijsrsd.2024.330278.1066 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Sobhi A. Abdelhameed   1; Iman Fouad Mohamed2
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| 1Faculty of Arts, Port Said University and Egyptian Chinese University ECU | ||||
| 2geography , faculty of arts , Mansoura university | ||||
| Abstract | ||||
| The study addressed the evaluation of the role of geospatial artificial intelligence in monitoring the spatiotemporal changes in the locations and areas of green spaces in the Heliopolis district between 2015 and 2024. Satellite images from the specified period were analyzed, and geostatistical analyses were conducted, focusing on the spatial distribution pattern of green spaces and vegetation using remote sensing technology. Spatial analyses, available in geographic information system (GIS) software like ArcGIS Pro, were activated to derive areas of vegetation presence in Heliopolis from satellite images, aiming to calculate the green spaces in both 2015 and 2024, identify the locations of changes, and study their spatial distribution pattern in the study area. The study followed a spatial analysis approach based on deriving general land use layers and vegetation cover to determine green space areas and their spatial distribution pattern. Digital base maps and satellite data from Landsat for the years 2015 and 2024 as an applied model to reveal the extent of the changes' impact on temperature values in the region, reflecting on the surrounding environment. This is particularly important for specialists and decision-makers to reconsider spatial justice in distribution. The results of the spatial analysis methods in the GIS environment confirmed the concentration and irregular distribution of tree-planting projects in the study area, achieving spatial justice in their distribution across the entire region. These methods also identified areas lacking in tree-planting projects, which should be considered when planning future projects | ||||
| Keywords | ||||
| Heliopolis district; Remote sensing; Artificial intelligence | ||||
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