Work

Data Fusion for Multi-Task Learning of Building Extraction and Height Estimation

Year: 2023

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2308.02960

Authors: Jamal, Saad Ahmed, Aribisala, Arioluwa

Language: English

Topic: Automated Road and Building Extraction

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: green

Funders: European Commission, Chinese Academy of Sciences, Erasmus+

OpenAlex ID: W4385681394

Cites:

Cited by: 1

Related to: 10