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On June 29, 2023 at 2:42:00 PM UTC, Gravatar ib1_finance:

  • Removed maintainer from Land Cover Classification in Harare, Zimbabwe


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  • Updated description of Land Cover Classification in Harare, Zimbabwe from

    These raster files show the land cover classification around Harare in 2006 and 2010. The classification results were based on Spot 5 imagery. Land cover classes in the attribute table are as follows: Class 1 Regular Residential (small planned buildings) Class 2- Regular Residential (small unplanned buildings) Class 3 Commercial/Industrial (large buildings) Class 4 Natural (Vegetation/Soil/non built-up This dataset is part of a paper which illustrates how the capabilities of GIS and satellite imagery can be harnessed to explore and better understand the urban form of several large African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar). To allow for comparability across very diverse cities, this work looks at the above mentioned cities through the lens of several spatial indicators and relies heavily on data derived from satellite imagery. First, it focuses on understanding the distribution of population across the city, and more specifically how the variations in population density could be linked to transportation. Second, it takes a closer look at the land cover in each city using a semi-automated texture based land cover classification that identifies neighborhoods that appear more regular or irregularly planned. Lastly, for the higher resolution images, this work studies the changes in the land cover classes as one moves from the city core to the periphery. This work also explored the classification of slightly coarser resolution imagery which allowed analysis of a broader number of cities, sixteen, provided the lower cost. When using this dataset keep in mind: Accuracy is higher in closer to the City center, and the distinction between class 1 and class 2 has not been validated, so use with caution. To learn more about the methodology please refer to https://ssrn.com/abstract=2883394
    to
    These raster files show the land cover classification around Harare in 2006 and 2010. The classification results were based on Spot 5 imagery. Land cover classes in the attribute table are as follows: Class 1 Regular Residential (small planned buildings) Class 2- Regular Residential (small unplanned buildings) Class 3 Commercial/Industrial (large buildings) Class 4 Natural (Vegetation/Soil/non built-up This dataset is part of a paper which illustrates how the capabilities of GIS and satellite imagery can be harnessed to explore and better understand the urban form of several large African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar). To allow for comparability across very diverse cities, this work looks at the above mentioned cities through the lens of several spatial indicators and relies heavily on data derived from satellite imagery. First, it focuses on understanding the distribution of population across the city, and more specifically how the variations in population density could be linked to transportation. Second, it takes a closer look at the land cover in each city using a semi-automated texture based land cover classification that identifies neighborhoods that appear more regular or irregularly planned. Lastly, for the higher resolution images, this work studies the changes in the land cover classes as one moves from the city core to the periphery. This work also explored the classification of slightly coarser resolution imagery which allowed analysis of a broader number of cities, sixteen, provided the lower cost. When using this dataset keep in mind: Accuracy is higher in closer to the City center, and the distinction between class 1 and class 2 has not been validated, so use with caution. To learn more about the methodology please refer to https://ssrn.com/abstract=2883394.


  • Changed the license of Land Cover Classification in Harare, Zimbabwe to (previously CC-BY-4.0)


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  • Added the following fields to Land Cover Classification in Harare, Zimbabwe

    • harvest_source_title with value World Bank Water Data Portal
    • harvest_object_id with value 561bbe90-8dfe-483b-8bfc-a20c6fa04dc8
    • harvest_source_id with value e60f21e9-2630-4761-a46b-20d0d55940a0