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SpaceNet 6: Multi-Sensor All Weather Mapping Dataset

Shermeyer, Jacob and Hogan, Daniel and Brown, Jason and Van Etten, Adam and Weir, Nicholas and Pacifici, Fabio and Hänsch, Ronny and Bastidas, Alexei and Soenen, Scott and Bacastow, Todd and Lewis, Ryan (2020) SpaceNet 6: Multi-Sensor All Weather Mapping Dataset. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2003, pp. 4371-4378. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020-06-14 - 2020-06-19, virtual.

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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 MultiSensor 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 120km2 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).

Item URL in elib:https://elib.dlr.de/139667/
Document Type:Conference or Workshop Item (Speech)
Title:SpaceNet 6: Multi-Sensor All Weather Mapping Dataset
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Shermeyer, Jacobn-Q-Tel - CosmiQ WorksUNSPECIFIED
Hogan, Danieln-Q-Tel - CosmiQ WorksUNSPECIFIED
Brown, JasonCapella SpaceUNSPECIFIED
Van Etten, Adamn-Q-Tel - CosmiQ WorksUNSPECIFIED
Weir, Nicholasn-Q-Tel - CosmiQ WorksUNSPECIFIED
Pacifici, Fabio3Maxar TechnologiesUNSPECIFIED
Hänsch, RonnyUNSPECIFIEDhttps://orcid.org/0000-0002-2936-6765
Bastidas, AlexeiIntel AI LabUNSPECIFIED
Soenen, ScottCapella SpaceUNSPECIFIED
Bacastow, ToddMaxar TechnologiesUNSPECIFIED
Lewis, Ryann-Q-Tel - CosmiQ WorksUNSPECIFIED
Date:14 June 2020
Journal or Publication Title:Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2003
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 4371-4378
Keywords:SAR, data fusion, semantic segmentation, instance segmentation, machine learning, deep learning
Event Title:IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Event Location:virtual
Event Type:international Conference
Event Dates:2020-06-14 - 2020-06-19
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Aircraft SAR
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > SAR Technology
Deposited By: Hänsch, Ronny
Deposited On:16 Dec 2020 10:27
Last Modified:17 Feb 2022 16:57

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