Assessing the Quality of Urban Footprint Coverages of Kampala

Gewa Li; Bin Lyu; Yang Zhao; Tianming Xue; Shoucheng Jiang

doi:10.47297/taposatWSP2633-456933.20260708

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Assessing the Quality of Urban Footprint Coverages of Kampala

Volume 7, Issue 8

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Assessing the Quality of Urban Footprint Coverages of Kampala
Assessing the Quality of Urban Footprint Coverages of Kampala
Gengdan Institute of Beijing University of Technology, Beijing, 101301, China
Vol. 7, Issue 8, Pages: 168-171(2026)
Published：30 August 2026
DOI：10.47297/taposatWSP2633-456933.20260708
Accepted：

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Li Gewa,Lyu Bin,Zhao Yang,et al.Assessing the Quality of Urban Footprint Coverages of Kampala[J].Theory and Practice of Science and Technology,2026,07(08):168-171.
DOI：

Li Gewa,Lyu Bin,Zhao Yang,et al.Assessing the Quality of Urban Footprint Coverages of Kampala[J].Theory and Practice of Science and Technology,2026,07(08):168-171. DOI： 10.47297/taposatWSP2633-456933.20260708.

摘要

Abstract

Assessing the accuracy of land-cover classifications is a major challenge in remote sensing

due to the lack of reliable

highly accurate

temporal-comparable reference data. This research assessed the quality of Kampala's urban footprint products using reference data from Kampala Capital City Authority (KCCA)

taking Global Human Settlement Layer (GHSL

developed by Joint Research Centre (JRC)) and Urban Footprint Classification (UFC

developed by German Aerospace Centre (DLR)

a fully-automated binary settlement mask product) as study objects.This research assessed product quality via reference data

to identify factors causing wrong classification and explore applicable urban planning scenarios for these products.Research procedures include—checking reference data quality; processing reference data to match the comparability of target data; assessing accuracy via error matrix table and SPSS statistical comparison; summarizing influencing factors via overlay of products

reference data and image mosaic. Results showed both products had acceptable overall accuracy of ≥75%. Influencing factors were extracted from error reference points and misclassified areas

with no significant difference between the two products. Factors were divided into built environment and natural environment factors. Statistical comparison showed high correlation between each product and its corresponding reference dataset

while comparison with contrasting reference datasets (GHSL matched with buildings and roads; UFC matched only with buildings) showed low correlation.This research discussed applications in urban densification

land use and land cover. Continuous value data GHSL performs better in describing urban densification; UFC performs better in land cover analysis than land use analysis. This research only uses one-year data

which limits further temporal analysis to clarify product suitability for urban planning dynamics.

关键词

Keywords

references

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