  Within the remote sensing domain, a diverse set of acquisition modalities
exist, each with their own unique strengths and weaknesses. 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. optical data is often the preferred choice for geospatial
applications, but requires clear skies and little cloud cover to work well.
Conversely, Synthetic Aperture Radar (SAR) sensors have the unique capability
to penetrate clouds and collect during all weather, day and night conditions.
Consequently, SAR data are particularly valuable in the quest to aid disaster
response, when weather and cloud cover can obstruct traditional optical
sensors. 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. <1m Ground Sample Distance
(GSD).
  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). The dataset and challenge focus on mapping and building
footprint extraction using a combination of these data sources. 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. 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).
