The 3D dataset is updated every month automatically. The building models are generated for all ~10 million buildings in The Netherlands based on footprints of buildings and LiDAR point clouds. To standardise possible variances of LoD1 models and let the users choose the best one for their application, we have developed a LoD1 reconstruction service that generates several heights per building (both for the ground surface and the extrusion height). Users are often not aware of these differences, while these differences may have an impact on the outcome of spatial analyses. But LoD1 representations for the same building can be rather different because of differences in height references used to reconstruct the block models and differences in underlying statistical calculation methods. These so called LoD1 models can be reconstructed relatively easily from building footprints and point clouds. However, for many application block models of buildings are sufficient or even more suitable. The 3D representation of buildings with roof shapes (also called LoD2) is popular in the 3D city modelling domain since it provides a realistic view of 3D city models. We present a baseline and benchmark for building footprint extraction with SAR data and find that state-of-the-art segmentation models pre-trained on optical data, and then trained on SAR (F1 score of 0.21) outperform those trained on SAR data alone (F1 score of 0.135). MSAW covers 120 km^2 over multiple overlapping collects and is annotated with over 48,000 unique building footprints labels, enabling the creation and evaluation of mapping algorithms for multi-modal data.
The dataset and challenge focus on mapping and building footprint extraction using a combination of these data sources.
To address this problem, we present an open Multi-Sensor All Weather Mapping (MSAW) dataset and challenge, which features two collection modalities (both SAR and optical). Despite all of these advantages, there is little open data available to researchers to explore the effectiveness of SAR for such applications, particularly at very-high spatial resolutions, i.e. Consequently, SAR data are particularly valuable in the quest to aid disaster response, when weather and cloud cover can obstruct traditional optical sensors. Conversely, Synthetic Aperture Radar (SAR) sensors have the unique capability to penetrate clouds and collect during all weather, day and night conditions. optical data is often the preferred choice for geospatial applications, but requires clear skies and little cloud cover to work well. Yet, most of the current literature and open datasets only deal with electro-optical (optical) data for different detection and segmentation tasks at high spatial resolutions. Within the remote sensing domain, a diverse set of acquisition modalities exist, each with their own unique strengths and weaknesses.
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