ON THE ASSESSMENT OF INSTANCE SEGMENTATION FOR THE AUTOMATIC DETECTION OF SPECIFIC CONSTRUCTIONS FROM VERY HIGH RESOLUTION AIRBORNE IMAGERY

Abstract

During the High Modernism period spanning from approximately 1914 to 1970, the manufacturing of steel-constructed system halls witnessed a significant surge to accommodate the growing demand across various sectors such as industry, commerce, and agriculture. Surprisingly, these specific types of buildings have been largely overlooked in the realm of construction history research, resulting in a dearth of knowledge regarding their construction methods, distribution patterns, and contextual significance for assessing their historical value. This study aims to address this gap by exploring the potential of instance segmentation methods for the automated detection of system halls using high-resolution aerial imagery. To achieve this objective, state-of-the-art deep learning models are evaluated in terms of their ability to localize and delineate system halls accurately. Our experiments reveal that Mask R-CNN yields the most accurate results both quantitatively and qualitatively, closely followed by Cascade Mask R-CNN. However, it is important to note that multi-scale methods may introduce false positives since system halls possess distinct geometric dimensions that necessitate careful consideration during the detection process.