Building Segmentation on Satellite Images and Performance of Post-Processing Methods
This work addresses building segmentation for applications like urban planning, but it is incremental as it applies existing methods to new data without achieving SOTA.
The study tackled building segmentation on satellite images by training models in China and applying post-processing, then evaluating them on the Chicago region of the INRIA dataset, achieving promising but not state-of-the-art results.
Researchers are doing intensive work on satellite images due to the information it contains with the development of computer vision algorithms and the ease of accessibility to satellite images. Building segmentation of satellite images can be used for many potential applications such as city, agricultural, and communication network planning. However, since no dataset exists for every region, the model trained in a region must gain generality. In this study, we trained several models in China and post-processing work was done on the best model selected among them. These models are evaluated in the Chicago region of the INRIA dataset. As can be seen from the results, although state-of-art results in this area have not been achieved, the results are promising. We aim to present our initial experimental results of a building segmentation from satellite images in this study.