Data Fusion for Multi-Task Learning of Building Extraction and Height
  Estimation
Saad Ahmed Jamal Arioluwa Aribisala
In accordance with the urban reconstruction problem proposed by the DFC23
Track 2 Contest, this paper attempts a multitask-learning method of building
extraction and height estimation using both optical and radar satellite
imagery. Contrary to the initial goal of multitask learning which could
potentially give a superior solution by reusing features and forming implicit
constraints between multiple tasks, this paper reports the individual
implementation of the building extraction and height estimation under
constraints. The baseline results for the building extraction and the height
estimation significantly increased after designed experiments.