## 1 Introduction  
Forcibly displaced people (FDP) refers to a group of people who leave their home or home region involuntarily due to conflicts, generalized violence, persecution, or human rights violations [1]. The global FDP population surpassed 100 million in mid-2022, which represents more than 1% of the global population [2]. Refugees, as an important category of FDP, refer to a group of people who are outside their home country and have received recognized mandates from the United Nations (UN) [3]. Approximately 86% of global refugees are hosted by low- and middle-income countries which usually cannot provide sufficient living space, medical services, food, water, and sanitation [4]. United Nations High Commissioner for Refugees (UNHCR) [5] together with many non-governmental organizations such as Medecions Sans Frontieres (MSF, Doctors Without Borders in English) [6] have provided multiple humanitarian operations, including allocation and distribution of living resources and medical services to protect refugees. Estimating the refugee population in need is essential for the logistics planning of humanitarian aid [7]. However, in most cases, it is difficult to collect such information in the field due to security and access reasons [8; 9]. With the availability of very high spatial resolution satellite imagery, remote sensing has become one of the most powerful tools for population estimation for humanitarian operations over the last decade [8; 10; 11; 12; 13; 14]. Refugee-dwelling footprints are useful in population estimation [15; 16]. Therefore, producing high quality refugee dwelling footprints efficiently can save much time for urgent humanitarian operations [17]. Multiple remote sensing image analysis techniques have been applied for this dwelling-extraction task during the last decade. Visual interpretation is thought to be capable of producing the refugee-dwelling footprints with the highest accuracy even though it still includes errors from inter-variability of observations [8]. However, it is labour-intensive and time-consuming [8]. Over the last decade, the OBIA approach has been widely applied to this task. Researchers have developed mature processing routines for producing refugee-dwelling footprints [18; 19; 20]. Nevertheless, due to the fast dynamics and diverse ontology of dwellings, the complexity and transferability of OBIA rulesets remain challenging [21]. In the past ten years, the development of deep learning in computer vision [22; 23] has boosted a large number of applications in remote sensing [24; 25; 26]. However, related research for this extraction task is still limited. One main reason is the unavailability of adequate accurate label data of refugee dwellings [27] because most refugees are hosted by the countries where accurate label data of the footprints are not available in open sources, such as Open Street Map. With the efforts of researchers and staff from humanitarian services at the Department of Geoinformatics Z_GIS, a large amount of label data for FDP dwellings has been produced by semi-automatic approaches (e.g., OBIA), with limited manual correction as post-processing [8]. Nevertheless, due to the urgency of the humanitarian operations, the produced OBIA labels may exhibit several types of annotation errors. Therefore, the OBIA label data are considered \"noisy label data\" compared with manually annotated label data (Manual labels). To the best of our knowledge, no research thus far has discussed whether we can make use of the generated tremendous OBIA labels to replace the Manual labels for this extraction task by deep learning approaches. Currently, there are numerous deep learning models for remote-sensing-based building extraction tasks [28]. However, up to now, very few of them have been applied for this task. [PERSON] et al. [29] designed shallow convolutional neural networks (CNNs) to extract refugee dwellings in the Minawao refugee camp in northern Cameroon. The prediction results prove that CNNs have a high potential in this task. [PERSON] et al.