## 1 Introduction
[30] selected Mask-RCNN with ResNet-101 as a backbone to extract dwellings in multiple refugee camps. [PERSON] et al. [31] chose a Faster-RCNN model with ResNet-50 as a backbone to count dwellings in refugee camps. [PERSON] et al. [32] selected a Mask-RCNN model with ResNet-50 as a backbone to extract built-up structures in Khartoum in Sudan for fast humanitarian operations. [PERSON] et al. [33] tested the spatial transferability of U-Net in this extraction task. [PERSON] et al. [34] selected a Mask-RCNN model with ResNet-101 as a backbone to extract refugee dwellings in Cameroon. [PERSON] and [PERSON] [35] tested the performance of multiple neural networks and found that a fully neural network (FCN) outperforms Mask-RCNN in detecting refugee tents near the Syria-Jordan border. On the basis of the findings from [PERSON] and [PERSON] [35], this research selected an FCN model for semantic segmentation for all experiments. Semantic segmentation can assign a class label to each pixel in an image to produce a fine-grained delineation of target objects with embedded spatial information [36] and has been proven to be robust in labelling noise in image analysis applications [37; 38]. Up to now, many techniques have been developed to handle label noise in deep learning. [PERSON] et al. [39] reviewed related techniques and categorized them into six major classes, as shown in Figure 1. Recent progress in weakly supervised semantic segmentation (WSSS) enables CNNs to learn from weakly labelled data such as image-level annotations for semantic segmentation tasks. Related techniques such as self-supervised equivariant attention mechanism [40], group-wise learning [41], regional semantic contrast and aggregation [42], and adaptive early-learning correction [43] for WSSS also bring high value to make the best of the OBIA label data for refugee dwelling extraction tasks. Among all techniques, intermixing training data from multiple sources proved to be a less intuitive but effective approach to increase the robustness of deep learning models for image segmentation [39]. [PERSON] and [PERSON] [44] intermixed image data from multiple study sites, various dates, and/or different sensors and trained models with label data from the Microsoft building footprint dataset to extract building footprints from satellite imagery. The results reveal that intermixing training data from multiple sources can produce promising results despite label noise. Inspired by these findings, this research selected intermixing as the strategy to handle the label noise issue as a starting point. Inspired by this idea, we selected data from two refugee camps (Kule and Nguyenygiel in Ethiopia) from a dry season and a wet season (seen in Section 2.1) and designed multiple intermixing strategies (seen in Section 2.5) to make the comparison between the OBIA labels and the Manual labels in extracting refugee-dwelling footprints from very high-spatial-resolution satellite imagery (Pleiades-1, seen in Section 2.2) by semantic segmentation (U-Net with ResNet-34 as a backbone, seen in Section 2.3). In addition, we used a small amount (i.e., 10%, 20%, 30%, 40%, and 50%) of the Manual labels to fine-tune models trained with the OBIA labels to verify whether we can use a smaller amount of the Manual labels and existing OBIA labels to achieve similar performance as that of the models purely trained with a large amount of the Manual labels (seen in Section 2.5). The main contribution of this research is that we first prove the OBIA labels, even though they include delineation errors, have very high potential to replace the manual labels, to a certain extent, in extracting refugee-dwelling footprints. Additionally, fine-tuning models trained with the OBIA labels with a small amount of the Manual labels can outperform models trained with purely Manual labels. The currently available OBIA labels from the last decade's humanitarian operations could be fuel for future deep-learning-based refugee-dwelling extraction tasks.