The most recent xView competition, xView2, sourced computer vision solutions to automate building damage assessment, using satellite imagery from before and after natural disasters.
Illegal, unreported, and unregulated (IUU) fishing is a major threat to human food supply, marine ecosystem health, and geopolitical stability. IUU fishing is widespread, threatening the sustainability of global fisheries in national waters and on the high seas. Developing countries are most at risk from IUU fishing, with estimated actual catches in West Africa, for example, being 40 percent higher than reported catches. Worldwide, one in five wild-caught fish is likely to be illegal or unreported; the econonomic value of these fish never reaches the communities that are the rightful beneficiaries. Annual global losses due to this illegal activity are valued at $10 billion to $23.5 billion USD.
The worst examples of IUU fishing are often connected to trans-national crimes, including human rights abuses, bonded labor, tax evasion, piracy, and drug, arms and human trafficking. IUU fishing also exacerbates the effects of climate change on ocean resources.
The trans-national nature of IUU fishing demands the ability to readily share and receive data among nations. More cost-effective tools for rapid detection of suspicious and illicit fishing activity will strengthen enforcement and control efforts, mitigating the damages caused by IUU fishing.
For IUU fishing activity to be addressed and reduced, it must first be detected. While many tools have been developed to help detect and monitor IUU activity at sea, the current options all have limitations:
- Approaches which rely on cooperation from the vessel, such as radio transponder signals like the automatic identification system (AIS) and vessel monitoring system (VMS), are susceptible to manipulation and tampering by those who wish to conceal their activity.
- Passive satellite sensors, such as electro-optical (EO) imagery, which acquire the reflected electromagnetic waves of sunlight and/or the infrared radiation emitted by objects on the ground, are confounded by cloud cover, haze, weather events, and seasonal darkness at high latitudes.
Synthetic aperture radar (SAR) is one of the power tools of remote sensing, and an increasingly valuable complement to other vessel detection systems. Active satellite sensors, such as SAR, transmit radar waves to the Earth and measure the backscatter and traveling time of the signals that are reflected back from objects on the ground. Vessels at sea provide a strong reflection signal, giving SAR robust detection capability that is unaffected by clouds or weather.
SAR has proven to be the most consistent option for detecting vessel presence, as it does not require cooperation or cloud-free skies. However, analysts have traditionally struggled to glean useful information from SAR imagery for identification and classification of vessels for a variety of reasons, including: image resolution, sensor incidence angle, vessel build material, radar cross section, wind/wave conditions, and the lack of ground-truth data (such as AIS) to corroborate analysis results.
Moving from simple vessel presence and length estimates to reliable detection of vessels engaged in IUU fishing further complicates the challenge. Vessels must be categorized by type and activity (fishing vs. not fishing) and evaluated for the likelihood that any fishing activity is illegal, unreported or unregulated.
Although SAR sensors have been providing satellite imagery for decades, the technical and economic barriers to improving analytical capabilities have proven to be substantial. There are no global, high-quality, consistently processed SAR datasets with validated ground truth detections available for academic, industrial, and open source communities to tackle the problem of detecting and classifying the activity of vessels at sea.
For xView3, we created a free and open large-scale dataset for maritime detection, and the computing capability required to generate, evaluate and operationalize computationally intensive AI/ML solutions at global scale. The data are consistently processed to include aligned views and relevant context above and below the ocean surface, with ground truth detections derived by combining AIS tracks, existing automated SAR analysis, and human visual detections. We believe it to be the biggest and best dataset of its kind, and are eager to see it in action throughout the community!
xView is a series of international computer vision competitions run by the Defense Innovation Unit, to advance, benchmark, and procure state-of-the-art computational solutions in domains relevant to national security. We have partnered with Department of Defense organizations, federal, state, and local first responders, and non-governmental organizations to create and release big, high-quality, open datasets aligned to specific prediction tasks that are relevant to national security and the world at large.
- Over 2000 submissions resulted in three winning solutions, with 80% damage detection success rate
- The top solution was 266% better than the government baseline algorithm
- Winning solutions were deployed to assist with a variety of natural disasters around the world in 2020 and beyond, from the Australian bushfires, to seasonal wildfires in California, to hurricanes and floods in the Southeastern USA.
Awards:- Recognition & support
Deadline:- 30-11-2021
