mlsanityData to 14 Oct 2026

Multi-Task Learning of Height and Semantics from Aerial Images

arXiv 1911.07543 ↗First posted 18 Nov 2019cs.CV cs.LGcited by 14

Not on the map. Why?

Abstract (arXiv)

Aerial or satellite imagery is a great source for land surface analysis, which might yield land use maps or elevation models. In this investigation, we present a neural network framework for learning semantics and local height together. We show how this joint multi-task learning benefits to each task on the large dataset of the 2018 Data Fusion Contest. Moreover, our framework also yields an uncertainty map which allows assessing the prediction of the model. Code is available at https://github.com/marcelampc/mtl_aerial_images .

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Nov 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 4; 2024: 0; 2025: 3; 2026: 2.

14 papers.

Sort:

By area: