Details
| Original language | English |
|---|---|
| Pages (from-to) | 107-115 |
| Number of pages | 9 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | X-4-2024 |
| Publication status | Published - 18 Oct 2024 |
| Event | 2024 Mid-term Symposium on Spatial Information to Empower the Metaverse - Perth, Australia Duration: 22 Oct 2024 → 25 Oct 2024 |
Abstract
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.
Keywords
- Aerial Images, Historical Geodata, Land Cover, Multi-modal Classification, Semantic Segmentation, Topographic Maps
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Instrumentation
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. X-4-2024, 18.10.2024, p. 107-115.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Multi-modal Land Cover Classification of Historical Aerial Images and Topographic Maps
T2 - 2024 Mid-term Symposium on Spatial Information to Empower the Metaverse
AU - Dorozynski, Mareike
AU - Rottensteiner, Franz
AU - Thiemann, Frank
AU - Sester, Monika
AU - Dahms, Thorsten
AU - Hovenbitzer, Michael
N1 - Publisher Copyright: © Author(s) 2024.
PY - 2024/10/18
Y1 - 2024/10/18
N2 - 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.
AB - 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.
KW - Aerial Images
KW - Historical Geodata
KW - Land Cover
KW - Multi-modal Classification
KW - Semantic Segmentation
KW - Topographic Maps
UR - http://www.scopus.com/inward/record.url?scp=85212394158&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-X-4-2024-107-2024
DO - 10.5194/isprs-annals-X-4-2024-107-2024
M3 - Conference article
AN - SCOPUS:85212394158
VL - X-4-2024
SP - 107
EP - 115
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
Y2 - 22 October 2024 through 25 October 2024
ER -