TRANSFERABILITY ASSESSMENT OF OPEN-SOURCE DEEP LEARNING MODEL FOR BUILDING DETECTION ON SATELLITE DATA

• Main Authors: A. Spasov, D. Petrova-Antonova
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-10-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W4-2021/107/2021/isprs-archives-XLVI-4-W4-2021-107-2021.pdf

A great number of studies for identification and localization of buildings based on remote sensing data has been conducted over the past few decades. The majority of the more recent models make use of neural networks, which show high performance in semantic segmentation for the purpose of building detection even in complex regions like the city landscape. However, they could require a substantial amount of labelled training data depending on the diversity of objects targeted, which could be expensive and time consuming to acquire. Transfer Learning is a technique that could be used to reduce the amount of data and resources needed by applying knowledge obtained solving one problem to another one. In addition, if open-source data and models are used, this process is much more affordable. In this paper, the Transfer Learning challenges and issues are explored by utilizing an open-sourced pre-trained deep learning model on satellite data for building detection.