The Problem

Each year, natural disasters such as hurricanes, tornadoes, earthquakes and floods significantly damage infrastructure and result in loss of life, property and billions of dollars. As these events become more frequent and severe, there is an increasing need to rapidly develop maps and analyze the scale of destruction to better direct resources and first responders. 

To help address this need, the SpaceNet 8 Flood Detection Challenge will focus on infrastructure and flood mapping related to hurricanes and heavy rains that cause route obstructions and significant damage. The goal of SpaceNet 8 is to leverage the existing repository of datasets and algorithms from SpaceNet Challenges 1-7 and apply them to a real-world disaster response scenario, expanding to multiclass feature extraction and characterization. 

Since its launch in 2016, SpaceNet has made significant progress advancing open-source building footprint and road extraction algorithms. During SpaceNet 8, challenge participants will train algorithms on imagery and labels from previous challenges—as well as newly created labeled training datasets from Maxar—to rapidly map an area affected by flooding. Any winning open-source algorithm from SpaceNet 1-7 may also be used. New areas of interest (AOIs) will include New Orleans, Louisiana, following Hurricane Ida in August 2021; Dernau, Germany, during the June 2021 floods across Western Europe; and a new “mystery city” for blind testing of the algorithms. 

SpaceNet 8 aims to answer these questions:

• How have algorithms that extract buildings and roads improved since SpaceNet was launched, and how can top algorithms from previous challenges be leveraged? 
• What is the impact on performance for a multiclass feature extraction challenge—i.e., buildings and roads? 
• How accurately can roads obstructed by flood waters be characterized from pre-event road detections and post-event satellite imagery? 

Training Data

Testing Data

Catalog

• The data is hosted on AWS as a Public Dataset. It is free to download. An AWS account is not required; however, you must have the AWS CLI installed to access the data.  
• To explore the dataset: 
  aws s3 ls s3://spacenet-dataset/spacenet/SN8_floods/

SpaceNet 8 – The Detection of Flooded Roads and Buildings publication 

License

The SpaceNet Dataset by SpaceNet Partners is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

The Problem

Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 232 United Nations Sustainable Development Goals, but the World Bank estimates that more than 100 countries currently lack effective Civil Registration systems. The SpaceNet 7 Multi-Temporal Urban Development Challenge aims to help address this deficit and develop novel computer vision methods for non-video time series data. In this challenge, participants will identify and track buildings in satellite imagery time series collected over rapidly urbanizing areas. The competition centers around a new open source dataset of Planet satellite imagery mosaics, which includes 24 images (one per month) covering ~100 unique geographies. The dataset will comprise over 40,000 square kilometers of imagery and exhaustive polygon labels of building footprints in the imagery, totaling over 10 million individual annotations. Challenge participants will be asked to track building construction over time, thereby directly assessing urbanization.

This Challenge has broad implications for disaster preparedness, the environment, infrastructure development, and epidemic prevention. Beyond the humanitarian applications, this competition poses a unique challenge from a computer vision standpoint because of the small pixel area of each object, the high object density within images, and the dramatic image-to-image difference compared to frame-to-frame variation in video object tracking. We believe this challenge will aid efforts to develop useful tools for overhead change detection and object tracking.

The SpaceNet 7 Challenge will be featured as a competition at the 2020 NeurIPS conference in December, where winning results will also be analyzed.

The Data – ~100 locations, spread out across the globe

Catalog

The data is hosted on AWS as a Public Dataset. It is free to download, but an AWS account is required.

To explore the dataset:

A sample is available at:

License

The SpaceNet Dataset by SpaceNet Partners is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

The Problem

Synthetic Aperture Radar (SAR) is a unique form of radar that can penetrate clouds, collect during all- weather conditions, and capture data day and night. Overhead collects from SAR satellites could be particularly valuable in the quest to aid disaster response in instances where weather and cloud cover can obstruct traditional electro-optical sensors. However, despite these advantages, there is limited open data available to researchers to explore the effectiveness of SAR for such applications, particularly at ultra-high resolutions.

The task of SpaceNet 6 was to automatically extract building footprints with computer vision and artificial intelligence (AI) algorithms using a combination of SAR and electro-optical imagery datasets. This openly-licensed dataset features a unique combination of half-meter Synthetic Aperture Radar (SAR) imagery from Capella Space and half-meter electro-optical (EO) imagery from Maxar’s WorldView 2 satellite. The area of interest for the challenge was centered over the largest port in Europe: Rotterdam, the Netherlands. This area features thousands of buildings, vehicles, and boats of various sizes, to make an effective test bed for SAR and the fusion of these two types of data.

In this challenge, the training dataset contained both SAR and EO imagery, however, the testing and scoring datasets contained only SAR data. Consequently, the EO data could be used for pre-processing the SAR data in some fashion, such as colorization, domain adaptation, or image translation, but cannot be used to directly map buildings. The dataset was structured to mimic real-world scenarios where historical EO data may be available, but concurrent EO collection with SAR is often not possible due to inconsistent orbits of the sensors, or cloud cover that will render the EO data unusable.

The Problem

Determining optimal routing paths in near real-time is at the heart of many humanitarian, civil, military, and commercial challenges. This statement is as true today as it was two years ago when the SpaceNet Partners announced the SpaceNet Challenge 3 focused on road network detection and routing. In a disaster response scenario, for example, pre-existing foundational maps are often rendered useless due to debris, flooding, or other obstructions. Satellite or aerial imagery often provides the first large-scale data in such scenarios, rendering such imagery attractive.

The SpaceNet 5 challenge sought to build upon the advances from SpaceNet 3 and test challenge participants to automatically extract road networks and routing information from satellite imagery, along with travel time estimates along all roadways, thereby permitting true optimal routing.

The task of this challenge was to output a detailed graph structure with edges corresponding to roadways and nodes corresponding to intersections and end points, with estimates for route travel times on all detected edges. You can find a detailed description of CosmiQ Works’ algorithmic baseline on their blog at The DownLinQ.

SpaceNet open sourced new data sets for the following cities: Moscow, Russia; Mumbai, India; and San Juan, Puerto Rico. For the first time in SpaceNet history, the final submissions were tested on a mystery city dataset that was revealed and open sourced at the end of the Challenge.

Earn AWS GPU Credits!

The first 20 competitors to reach a score of 50 (out of a possible 100) received a credit for 10 hours on a p3.2xlarge for training and improving their models.  To further aid competitors, the SpaceNet 5 baseline is fully open source, and yields a score of 54.  If you have any questions, please reach out through the Topcoder Forum (https://www.topcoder.com/challenges/30099956).

The Problem

Can you help us automate mapping from off-nadir imagery? In this challenge, competitors were tasked with finding automated methods for extracting map-ready building footprints from high-resolution satellite imagery from high off-nadir imagery. In many disaster scenarios the first post-event imagery is from a more off-nadir image than is used in standard mapping use cases. The ability to use higher off-nadir imagery will allow for more flexibility in acquiring and using satellite imagery after a disaster. Moving towards more accurate fully automated extraction of building footprints will help bring innovation to computer vision methodologies applied to high-resolution satellite imagery, and ultimately help create better maps where they are needed most.

The main purpose of this challenge was to extract building footprints from increasingly off-nadir satellite images. The created polygons were compared to ground truth, and the quality of the solutions were measured using the SpaceNet metric.

Read more about the Challenge winners from our blog!

The Problem

The commercialization of the geospatial industry has led to an explosive amount of data being collected to characterize our changing planet. One area for innovation is the application of computer vision and deep learning to extract information from satellite imagery at scale. CosmiQ Works, Radiant Solutions and NVIDIA have partnered to release the SpaceNet data set to the public to enable developers and data scientists to work with this data.

Today, map features such as roads, building footprints, and points of interest are primarily created through manual techniques. We believe that advancing automated feature extraction techniques will serve important downstream uses of map data including humanitarian and disaster response, as observed by the need to map road networks during the response to recent flooding in Bangladesh and Hurricane Maria in Puerto Rico. Furthermore, we think that solving this challenge is an important stepping stone to unleashing the power of advanced computer vision algorithms applied to a variety of remote sensing data applications in both the public and private sector.