Toward Automatic Building Footprint Delineation From Aerial Images Using CNN and Regularization

Boundary-Aware Refined Network for Automatic Building Extraction in Very High-Resolution Urban Aerial Images

“…As a consequence, the boundary accuracy of the building mask is limited. To support these statements, an instance of a segmentation error map acquired by U-Net, PSPNet [29], DeepLab-v3+ [30] and MA-FCN [23] is demonstrated in Figure 1, from which all four state-of-the-art methods present errors, with missed holes in the large buildings and inconsistent boundary segmentation. Therefore, a novel method needs to be developed to address these issues mentioned above and further enhance the performance.…”

“…To reveal whether the proposed method gains an advantage over other recent State-Of-The-Art (SOTA) studies, several remarkable CNNs for semantic segmentation and building extraction were chosen as comparative methods, namely U-Net [26], DeepLab-v3+ [30] and DANet [36]. Moreover, MA-FCN [23], which achieved a very high IoU score of 90.7% on the WHU dataset [23], was also chosen to verify the superiority of our method. Among these methods, DANet is a representative dilated FCN, where the self-attention mechanism was applied to aggregate the holistic context.…”

“…To objectively assess the performance of the proposed method, following the work in [1,21,23], four commonly used metrics, i.e., precision, recall, F 1 score and Intersection over Union (IoU), were adopted in the following experiments. These metrics are expressed as:…”

“…In general, under the support of massive training data with highquality annotations, the performance of CNN based approaches is superior to the other learning based methods in terms of generalization and precision. As a result, CNN based learning methods are widely utilized and represent an exciting program of research [21][22][23][24][25].…”

“…Based on the Fully-Convolutional Network (FCN), previous works have achieved great success as reflected by numerous variants of FCNs, such as the encoder-decoder based U-Net [26] and SegNet [27]. In previous methods, the encoder-decoder structure [21,23] and some off-the-shelf context modelling approaches [22,25] were adopted to perform building extraction without considering the multi-scale problem and the boundary segmentation accuracy. There were some early attempts to tackle the multi-scale problem [28], but they are not yet adequate to cope with the scale variations in buildings and often involve insufficient multi-scale context aggregation, which leads to incomplete segmentation, particularly for large-scale building extraction.…”