Multi-modal Land Cover Classification of Historical Aerial Images and Topographic Maps: A Comparative Study

• Published in: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences remote sensing and spatial information sciences, 2024-10, Vol.X-4-2024, p.107-115
• Main Authors: Dorozynski, Mareike, Rottensteiner, Franz, Thiemann, Frank, Sester, Monika, Dahms, Thorsten, Hovenbitzer, Michael
• Format: Article
• Language: English
• Subjects: Classification; Comparative studies; Computer aided mapping; Digital computers; Digital imaging; Digital mapping; Environmental monitoring; Image segmentation; Information systems; Land cover; Machine learning; Monitoring; Qualitative analysis; Topographic mapping; Topographic maps; Topography; Vegetation
• ISSN: 2194-9050; 2194-9042; 2194-9050
• DOI: 10.5194/isprs-annals-X-4-2024-107-2024

Knowledge about land cover is relevant for many different applications such as updating topographic information systems, monitoring the environment, and planning future land cover. Particularly for monitoring, it is of interest to be not only aware of current land cover but of past land cover at different epochs, too. To allow for efficient, computer-aided spatio-temporal analysis, digital land cover information is required explicitly. In this context, historic aerial orthophotos and scanned historic topographic maps can serve as sources of information, in which land cover information is contained implicitly. The present work aims to automatically extract land cover from this data using classification. Thus, a deep learning-based multi-modal classifier is proposed to exploit information from aerial imagery and maps simultaneously for land cover prediction. Two variants of the classifier are trained, utilizing a supervised training strategy, for building segmentation and vegetation segmentation, respectively. Both classifiers are evaluated on independent test sets and compared to their respective two uni-modal counterparts, i.e. an aerial image classifier and a map classifier. Thus, a mean F1-score of 62.2% for multi-modal building segmentation and a mean F1-score of 83.7% for multimodal vegetation segmentation can be achieved. Detailed analysis of quantitative and qualitative results gives hints for promising directions for future research of multi-modal classifiers to further improve the performance of the multi-modal classifier.