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Building extraction and height estimation are two important basic tasks inremote sensing image interpretation, which are widely used in urban planning,real-world 3D construction, and other fields. Most of the existing researchregards the two tasks as independent studies. Therefore the height informationcannot be fully used to improve the accuracy of building extraction and viceversa. In this work, we combine the individuaL buIlding extraction and heiGHtestimation through a unified multiTask learning network (LIGHT) for the firsttime, which simultaneously outputs a height map, bounding boxes, and asegmentation mask map of buildings. Specifically, LIGHT consists of an instancesegmentation branch and a height estimation branch. In particular, so as toeffectively unify multi-scale feature branches and alleviate feature spansbetween branches, we propose a Gated Cross Task Interaction (GCTI) module thatcan efficiently perform feature interaction between branches. Experiments onthe DFC2023 dataset show that our LIGHT can achieve superior performance, andour GCTI module with ResNet101 as the backbone can significantly improve theperformance of multitask learning by 2.8% AP50 and 6.5% delta1, respectively.

LIGHT: Joint Individual Building Extraction and Height Estimation from Satellite Images through a Unified Multitask Learning Network