Deep Learning Based Urban Building Coverage Ratio Estimation Focusing on Rapid Urbanization Areas

Abstract

Urban parameters, such as building density and the building coverage ratio (BCR), play a crucial role in urban analysis and measurement. Although several approaches have been proposed for BCR estimations, a quick and effective tool is still required due to the limitations of statistical-based and manual mapping methods. Since a building footprint is crucial for the BCR calculation, we hypothesize that Deep Learning (DL) models can aid in the BCR computation, due to their proven automatic building footprint extraction capability. Thus, this study applies the DL framework in the ArcGIS software to the BCR calculation task and evaluates its efficiency for a new industrial district in South Korea. Although the accuracy achieved was limited due to poor-quality input data and issues with the training process, the result indicated that the DL-based approach is applicable for BCR measuring, which is a step toward suggesting an implication of this method. Overall, the potential utility of this proposed approach for the BCR measurement promises to be considerable.

Advancing Rural Building Extraction via Diverse Dataset Construction and Model Innovation with Attention and Context Learning

“…The extraction results of SegNet and AGs-UNet display varying degrees of internal structural deficiencies. C 3 Net exhibits more complete internal information but insufficiently defined edges. CSA-UNet has sharper yet less smooth and natural edges.…”

“…On the second dataset, as delineated in Table 3, AGSC-Net improved the OA metric by 0.2-0.5 percentage points and the IoU metric by 0.6-1 percentage points compared to other models, maintaining significant performance advantages. Although C 3 Net has the highest precision P on the first dataset, AGSC-Net has a higher F1 score by balancing precision and recall, evaluating the overall effect of the model more comprehensively. In addition, the IoU metric combines accuracy and recall metrics, and the IoU metric of AGSC-Net is significantly higher than that of the other models, indirectly verifying that its extraction results are more accurate and complete.…”

“…To evaluate our rural building extraction method, we selected the typical encoderdecoder architecture extraction model SegNet, AGs-UNet with a structure similar to our building extraction model, C 3 Net, and CSA-UNet, also applied to rural building extraction for comparative experiments. A brief introduction of these models is as follows:…”

“…(3) C 3 Net adopts an encoder-decoder structure. The decoder integrates context-aware and edge residual refinement modules to achieve a balance between extraction accuracy and integrity [23].…”

“…Accurate extraction of urban buildings is significant for urban planning, disaster assessment, building area estimation, 3D urban modeling, and so on [1][2][3][4]. Compared with urban buildings, rural buildings have their own characteristics: rural buildings have smaller scales, lower floors, scattered distributions in villages, and higher dispersion degrees; second, building materials, designs and construction times vary greatly, leading to larger internal differences [5].…”