Comparing OBIA-Generated Labels and Manually Annotated Labels for Semantic Segmentation in Extracting Refugee-Dwelling Footprints

Abstract

Refugee-dwelling footprints derived from satellite imagery are beneficial for humanitarian operations. Recently, deep learning approaches have attracted much attention in this domain. However, most refugees are hosted by low- and middle-income countries where accurate label data are often unavailable. The Object-Based Image Analysis (OBIA) approach has been widely applied to this task for humanitarian operations over the last decade. However, the footprints were usually produced urgently, and thus, include delineation errors. Thus far, no research discusses whether these footprints generated by the OBIA approach (OBIA labels) can replace manually annotated labels (Manual labels) for this task. This research compares the performance of OBIA labels and Manual labels under multiple strategies by semantic segmentation. The results reveal that the OBIA labels can produce IoU values greater than 0.5, which can produce applicable results for humanitarian operations. Most falsely predicted pixels source from the boundary of the built-up structures, the occlusion of trees, and the structures with complicated ontology. In addition, we found that using a small number of Manual labels to fine-tune models initially trained with OBIA labels can outperform models trained with purely Manual labels. These findings show high values of the OBIA labels for deep-learning-based refugee-dwelling extraction tasks for future humanitarian operations.

Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning

“…However, in spite of current advancements in image processing techniques for RS data and the increased availability of imagery sources, extracting information from refugee camps has not yet been fully explored [21]. Several factors may contribute to the limited amount of research on refugee camps, including the high cost of acquiring and processing VHR satellite imagery and the absence of large labeled data sets for developing and training intended deep learning approaches [22]. It must be noted that refugee camps include small-scale dwellings and buildings that are often irregularly positioned and cannot be separated from background features such as bare ground and vegetation, since these features usually appear together in one coarse pixel of moderate-resolution images [23].…”

“…Purely pixel-based approaches for dwelling extraction as an alternative to visual interpretation were rapidly replaced by spatially aware and context-sensitive ones such as object-based image analysis (OBIA) [22,23,[26][27][28][29], template-matching [30], mathematical morphology-based algorithms [31,32], and, recently, deep learning approaches [21,33]. During the past decade, the standard deep learning (DL) approach to image classification has been chiefly convolutional neural network (CNN) which has continuously optimized and achieved cutting-edge performance [34][35][36][37].…”