Browsing by Author "Nakiranda, Proscovia"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Building identification in aerial imagery using deep learning
    (Stellenbosch : Stellenbosch University, 2024-03) Nakiranda, Proscovia; Grobler, Trienko; Stellenbosch University. Faculty of Science. Dept. of Computer Science.
    ENGLISH ABSTRACT: Advancements in the field of remote sensing have facilitated the effortless re- trieval of information about any location on Earth at any given time. This has resulted in a variety of developments, including the identification of economic activities taking place in a particular area. The task of identifying build- ings is one significant application of remote sensing imagery as it is crucial for assessing resource distribution, especially in low-resource or data-scarce areas. Advances in machine learning and computation resources allow for au- tomatic analysis of collected remote sensing data, eliminating the need for human intervention. The task of building identification falls under computer vision and is an example of a task that can be automated. Several machine learning architectures (models) have been proposed for building identification. However, choosing the appropriate one can be challenging due to limitations such as difficulty in accurately identifying boundary or near boundary pixels, resource requirements, and overall model accuracy. Therefore, conducting a comparative study is necessary to evaluate the performance of the building identification models. In this thesis, we carry out a comparative study of four state-of-the-art models used for the building identification task. We eval- uate their performance both qualitatively and quantitatively. Furthermore, we investigate the effect of multitask learning on the models’ performance in building identification. The thesis concludes by providing our research find- ings and outlining prospective future research avenues. Moreover, it provides a thorough overview of the fundamental theory underpinning remote sensing and machine learning.