Data Fusion for Multi-Task Learning of Building Extraction and Height Estimation
This work addresses urban reconstruction for remote sensing applications, but it is incremental as it focuses on individual task implementation rather than fully integrated multitask learning.
The paper tackled the urban reconstruction problem by implementing a multitask-learning method for building extraction and height estimation using optical and radar satellite imagery, reporting that baseline results for both tasks significantly increased after designed experiments.
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.