## 2 Materials and Methods
### Data Preparation  
We chose satellite imagery from the Pleiades-1 sensor with a spatial resolution of 2 m. Considering the common size of dwellings (20 to 40 m\({}^{2}\)) in the selected sites, we pansharpened the imagery with the panchromatic band from the same source and resampled it to 0.5 m in GeoTIFF format to help the models learn more geometric features. Two retrieval dates represent the dry season (24 March 2017) and the wet season (22 June 2018), respectively. The pixel depth is 16-bit. RGB bands were selected because we found that three input bands with pretrained weights from ImageNet datasets outperform four bands during the preliminary phase. All satellite images use WGS1984 as the geographic coordinate system and UTM 36N as the projected coordinate system.  
The OBIA labels of the two camps include only a small part of auxiliary buildings. To make a comparable and systematic comparison, we detect only facility buildings and dwellings in this research for both types of labels. While the footprints of dwellings can be used for population estimation [7], the footprints of facility buildings could be auxiliary information for humanitarian operators. Due to the limited number of facility buildings, they can be easily deleted as well, if such information is not useful in operations. For these reasons, we use binary classes in this research, which are _Target built-up structures_, including both refugee dwellings and facility buildings, and _Background_.  
The OBIA labels were provided by the Department of Geoinformatics--Z_GIS of Paris Lodron University of Salzburg. Manual labels were manually annotated by one expert and crosschecked by another expert using a GIS software package. The annotated polygon data were converted to GeoTIFF format with a spatial resolution of 0.5 m. Figure 3 presents the built-up structures in two camps during two seasons together with the OBIA-generated labels and the Manual labels. We can observe the OBIA labels have high quality, in general, but include some annotation errors, such as missing some built-up structures.  
Figure 3: The examples of the OBIA labels and the Manual labels of built-up structures in the Kule and Nguenyypiel refugee camps during the dry and the wet seasons.  
During data preparation, we firstly selected areas for training (and validation) and testing. The dimensions of the selected areas were kept the same for each camp across two seasons. Secondly, the selected training (and validation) data were converted into patches with an overlap of 32 pixels in a shape of (128, 128) pixels. This patch size has been applied in related research and proved to be effective [33]. Thirdly, we deleted patches without any target built-up structures to reduce the redundancy of the class _Background_, in turn, to improve training speed and reduce the imbalance between the class _Target built-up structures_ and the class _Background_. Following that, the produced patches were divided into training patches and validation patches at a ratio of 9:1. The data preparation process is briefly shown in Figure 4 below. Table 1 shows the number of training and validation patches for the OBIA labels and the Manual labels.