  Extracting building heights from satellite images is an active research area
used in many fields such as telecommunications, city planning, etc. Many
studies utilize DSM (Digital Surface Models) generated with lidars or stereo
images for this purpose. Predicting the height of the buildings using only RGB
images is challenging due to the insufficient amount of data, low data quality,
variations of building types, different angles of light and shadow, etc. In
this study, we present an instance segmentation-based building height
extraction method to predict building masks with their respective heights from
a single RGB satellite image. We used satellite images with building height
annotations of certain cities along with an open-source satellite dataset with
the transfer learning approach. We reached, the bounding box mAP 59, the mask
mAP 52.6, and the average accuracy value of 70% for buildings belonging to each
height class in our test set.
