## 2 Methods
### Geospatial Processing, Modelling, Expert Based Revision, and Manual Editing  
A multi-step post-processing of the rule-based OBIA was necessary in order to overcome some of the limitations of the dataset. In the intermediate dataset, large patches detected as a single building often in fact represented multiple adjacent buildings in densely built-up areas. The official cadastral dataset of Kigali from August 2014, which was provided by the City of Kigali, was used to split these large objects at the borders of plots.  
However, for many plots, several adjacent \"Basic\" buildings remain, resulting in an underestimation of the number of \"Basic\" buildings. To give a better estimation of the total number of basic buildings, 1162 \"Basic\" buildings were digitized manually and assessed using various descriptive statistics. The median ground floor area is 75.2 m\({}^{2}\). After a visual inspection of the results, as well as taking into account the statistics, the authors decided to split areas as soon as the doubled median size is reached (150.4 m\({}^{2}\)) and for \"Basic\" buildings above this size, to add a further split every 75.2 m\({}^{2}\) using a look-up table (LUT, Table 2). Therefore, basic buildings with the respective attribute \"Model = 1\" (see Table 7), do not represent the real-world delineations to adjacent buildings.  
The building outlines detected with OBIA mostly occur as irregular shapes without straight edges, due to the spatial resolution of the Pleiades image and the segmentation process. In order to provide buildings with more standardized geometries for use in urban planning and cartographic illustrations, the outlines were automatically generalized to reduce geometric artifacts and to create more regular and angular shapes [15]. The Shape Index (SI) was utilized to identify and remove artefacts [16]. The dataset was also corrected for topology and plausibility.  
\begin{table}
\begin{tabular}{|c|c|} \hline
**Range of Object Area** & **New Number of** \\
**(m\({}^{2}\))** & **Buildings** & \\ \hline
0–150.4 & 1 \\ \hline
150.4–225.6 & 2 \\ \hline
225.6–300.8 & 3 \\ \hline
300.8–376 & 4 \\ \hline
376–426.2 & 5 \\ \hline
426.2–501.4 & 6 \\ \hline
501.4–531.31 & 7 \\ \hline \end{tabular}
\end{table}
Table 2: LUT to model the number of “Basic” buildings of large patches. The histogram is showing the distribution of the ground floor area of the manually digitized reference buildings.  
In some cases, the spectral properties of rusty corrugated metal roofs were similar to the surrounding open soil and dusty roads. The segmentation and classification in the OBIA processing was therefore erroneous. In addition, light and shadow effects of complex roof structures often resulted in several segments where one would have been correct. A laborious visual inspection of the intermediate results (object geometries and classification) was conducted and a manual correction applied where appropriate. A manual correction of the delineations and exact geometries of the modelled \"Basic\" buildings using the LUT was not applied.  
A mapping of the change between the aerial images of 2008-2009 and the Pleiades 2015 analysis was then conducted manually. The reason for this is that the aerial images do not provide spectral information in the infrared domain, and color adjustments by the initial data provider lowered the radiometric resolution, resulting in the automated identification of buildings and type changes having insufficient accuracy. For the manual revision, the aerial images were overlaid with the 2015 results and then revised for the existence of buildings and the change of geometries. In addition, the type of change between the periods was also recorded in this process (see Table 3).