Understanding building area in agricultural land is important since arable land area in Taiwan is limited. One of the practical ways is manual digitization on high resolution satellite imagery, which can avoid field investigation and achieve satisfying results. However, such practice is tedious and labor intensive. Past researches have shown that deep learning methods are useful to segment buildings in different cities using satellite imagery. In this study, ENVINet5 model was trained and used to segment buildings from high resolution Pleiades pansharpened imagery. The training images (with the size of 2500 pixels × 2500 pixels) were randomly selected from 9 counties/cities to increase diversity since each county/city has different building patterns. The performance of ENVINet5 model reached 0.977, 0.814, 0.847, and 0.829 respectively on accuracy, precision, recall, and F1 score. Since evaluation by pixels can be difficult to show geometry of buildings, we evaluated the model by counting the number of inference building segments, which was post-processed from inference result of ENVINet5 trained model. Further analysis by counting the inference building segments is discussed in this study.
|頁（從 - 到）||587-594|
|期刊||International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives|
|出版狀態||Published - 2021 6月 28|
|事件||2021 24th ISPRS Congress Commission III: Imaging Today, Foreseeing Tomorrow - Nice, France|
持續時間: 2021 7月 5 → 2021 7月 9
All Science Journal Classification (ASJC) codes