Multi-source data fusion for land use classification using deep learning - Cao, Rui (2021)
Land use classification is the process of characterising the land by the purposes of usage. It is significantly important since timely and accurate land use information is essential for land monitoring and management. Traditional classification relies heavily on labour-intensive land survey, which is time-consuming and expensive. The development of remote sensing (RS) technologies has greatly advanced the automation of land monitoring, since RS images can efficiently capture the physical attributes of the earth surface. However, it is challenging to use single RS images to recognise land use, especially for high-density cities with complex and mixed land-use patterns. There are several major challenges. Firstly, it is challenging to extract representative features from RS images that are highly related to land use semantics and invariant to complex landscapes. Secondly, RS images capture the physical information from nadir view, and thus miss out many useful ground-level details. Thirdly, RS images cannot directly capture information about human activities on the land, which is critical to determine land use. To address these issues, this thesis leverages deep learning techniques and exploits recently emerged geospatial big data to complement RS images for land use classification. In summary, the thesis has made following contributions: